THE MOVIE SCHEDULING PROBLEM IN FLANDERS Aantal woorden: 16.942

Maud Vandevenne Stamnummer : 000130664757

Promotor: Prof. Dr. Dries Goossens

Masterproef voorgedragen tot het bekomen van de graad van:

Master in Business Engineering: Operations Management

Academiejaar: 2018-2019

Preface This thesis forms the conclusion to my studies in Business Engineering – Operations Management at Ghent University. I have truly enjoyed my time here and will be eternally grateful to my parents for giving me this opportunity.

First of all I would like to thank my promotor, professor Goossens, for his excellent guidance and helpful comments which have made this thesis possible. Next, my most sincere thanks to the movie exhibitors who were so kind to meet me for an interview, this really proved to be very helpful.

Furthermore, I have to send out my deepest thanks to my college friends who were always there to help me with programming and econometrical questions. Also, many thanks to my brother and his friends for showing me parts of Minerva I had never heard of in my previous five years at Ghent University. I would also like to thank my roommates and my siblings for pretending to listen to all my thesis rambling over these last months. Last but not least, my sincerest thanks to my friends and family for their undying support throughout this journey.

Permission For Use On Loan

I declare that the content of this Master's Dissertation can be consulted and / or reproduced if the sources are mentioned.

Maud Vandevenne, January 2019

Abstract This master’s dissertation focuses on finding an optimal scheduling method for movies in Flemish movie theaters. This problem is solved in two stages, first the demand function gets estimated and in the second phase an actual movie schedule is created. In this paper, the impact on the overall profit of a number of scheduling constraints is examined. Out of the test results it became clear that the requirement to play movies during a period of consecutive weeks causes a significant decrease in profit. As well as the constraint which forces the cinema to show a different movie on every screen at a given point in time and the constraint to show a diverse selection of movies. The extent of the impact of these last two constraints largely depends on the size of the theater.

Table of Contents Preface ...... I Permission For Use On Loan ...... II Abstract ...... III List of Figures ...... VII List of Tables ...... VIII 1. Introduction ...... 1 2. Movie Industry ...... 2 3 Literature review ...... 7 3.1 Predicting movie Demand ...... 7 3.2 Scheduling movies in the cinema ...... 9 4 Field research ...... 13 4.1 Interviews movie exhibitors ...... 13 4.1.1 Cinema Cartoon’s Antwerp – Jan-Willen Van Eemeren ...... 13 4.1.2 Sphinx Cinema Ghent – Patrick Deboes ...... 15 4.1.3 Studio Skoop Ghent – Walter Vander Cruysse ...... 15 4.1.4 Euroscoop Genk – An Van de Casseye ...... 16 5 Predicting Demand in R ...... 17 5.1 Parameters ...... 17 5.2 Dataset ...... 18 5.3 Hypotheses ...... 20 5.4 Regression model ...... 21 5.5 Demand division ...... 23 6 Scheduling Movies in Cplex ...... 25 6.1 Optimization function ...... 25 6.2 Constraints...... 26 6.2.1 Necessary Constraints ...... 26 6.2.2 Optional Constraints ...... 26 6.3 Computational Results ...... 27 6.3.1 Objective function ...... 28 6.3.2 Movie schedule ...... 30

7 Conclusions and future research ...... 33 7.2 Conclusions ...... 33 7.3 Future research ...... 34 8 References ...... 35 9 Appendices ...... 38 A. Purchasing Power ...... 38 B. Interviews movie exhibitors ...... 39 B.1 Interview Jan-Willem Van Eemeren, Cinema Cartoon’s Antwerpen ...... 39 B.2 Interview Patrick Deboes, Sphinx Gent ...... 44 B.3 Interview Walter Vander Cruysse, Studio Skoop Gent...... 49 B.4 Interview An Van de Casseye, Euroscoop Genk ...... 52 C. Output R ...... 54 C.1 Original Model ...... 54 C.2 Second Model ...... 55 C.3 Significance second order interactions ...... 56 C.4 Final Model ...... 57 C.5 Statistical Tests on Final Model ...... 58 C.6 Regression with GLS ...... 59 C.7 Regression Model Following Weeks ...... 62 C.8 ANOVA test demand model ...... 66 D. Demand division special cases ...... 67 D.1 Kids’ movies in school week ...... 67 D.2 Kids’ movies in holiday week ...... 67 D.3 Horrors and thrillers ...... 67 E. Program in Cplex ...... 68 F. Movie schedules ...... 72 F.1 Five screens, week 3 ...... 72 F.2 Five screens, week 17 ...... 74 F.3 Three screens, week 3 ...... 77 F.4 Three screens, week 17 ...... 79 G. Movie titles ...... 82

List of Figures Figure 2-1: Annual ticket sales and box office gross in millions for Belgium 2007 – 2017 ...... 3 Figure 2-2: Annual ticket sales and box office gross in millions for the United States 1980 - 2017 ...... 3 Figure 2-3: Global box office revenue 2017 ...... 4 Figure 2-4: Leading film markets worldwide - box office revenue 2017 ...... 5 Figure 3-1: Stock value for movies opening week 45-46, 2018 ...... 8 Figure 7-1: Comparison profit in euros for some different sets of constraints for the five screen example ...... 33

List of Tables Table 2-2 Global movie productions 2017, source Screen Australia, 2018 ...... 6 Table 2-3: European box office – 2017: European box office – 2017, source: UK Cinema association .. 7 Table 5-1: Weekly demand division ...... 24 Table 6-1: Relative impact of the constraints on the overall profit ...... 28 Table 6-2: Comparison relative impact constraint maximum one (B) or two (B2) screens playing the same movie at any given moment ...... 29 Table 6-3: Comparison of the relative impact of the constraint of movies playing at least one (D) or seven (D2) times during their release week ...... 29 Table 6-4: Robustness check - comparison impact constraints on two five screen movie theaters ... 30 Table 6-5: Total number of weeks movies spend at the box office. Underlined and in italics means that the period does not consist out of consecutive weeks...... 32

1. Introduction The movie industry – or as movie people refer to it, “THE industry” – consists of three main actors: producers, distributors and exhibitors. Producers make movies, distributors sell them all over the world and exhibitors show them to the public in their movie theaters. The first two are often integrated (Einav L., 2007) and in markets like the United States, some exhibitors have even integrated with them as well (Gil R., 2008). Making a movie requires a lot of money and the only way to earn this back is at the box office. Given the fact that movie exhibitors usually do not pay a fixed price to distributors in order to play a movie, but rather divide the box office profit between the two of them, distributors as well as exhibitors only make money through ticket sales. Therefore the task of scheduling movies in a cinema is of importance, not only to the exhibitors, but also to the distributors.

The movie scheduling problem consists out of two smaller sub-problems. The first one is the decision about which movies a certain movie theater decides to purchase and consequently show to its audience. The second problem is the decision of which movie to play on which screen at what time and depends therefore on the choices made when solving the first problem.

The decision of this first problem, which in literature sometimes gets described as the macro problem, is often made weeks or even months in advance. Naturally the timing of the movie selection depends on the supply at hand. If no distributor in the Benelux region has bought the distribution rights to a certain movie, no exhibitor in the area can show it. The choice for which films to include in the program of a cinema is either based upon early screenings attended by an employee of the theater or upon prior box office successes.

The second scheduling issue, also known as the micro problem, is the main focus of this thesis. This problem is again twofold: first the movie demand has to be predicted and then the actual schedule can be constructed. Specifically, this paper will focus on constructing a scheduling model for movie theaters in Flanders. The main goal is to create a tool which can accurately build a weekly movie program for a movie theater which will maximize its profit.

This thesis will step away from the old habit of first determining the number of showings a movie should get before deciding upon the actual schedule. With a computer program it is possible and convenient to determine both at once.

Furthermore there are a few scheduling decisions that are generally taken when planning movies, the financial impact of which will be investigated in this thesis. The constraints under consideration are listed in the next paragraph.

Cinemas almost always choose to show movies in consecutive weeks, also movies get always screened in their release week. Moreover most movie theaters prefer to show different movies at every screen at a given point in time. Next, it is generally assumed to play a certain amount of children’s movies on afternoons when there is no school. Lastly, movie theaters prefer to offer a diverse selection of movies to their audience.

The next chapter sketches an image of the movie industry, with extra focus on the particular situation in Belgium. Chapter 3 provides an analysis of the available research on movie scheduling relevant to this paper. In chapter 4 the field research, which consists out of a couple of interviews with movie exhibitors in Flanders, gets discussed. Next in chapter 5 the demand model is estimated and in chapter 6 a program to generate the actual movie schedule is described.

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2. Movie Industry As mentioned in the previous section, the movie industry has three major players producers, distributors and exhibitors. This paper considers the movie theater industry, which has recently been struggling with the new developments in the entertainment industry and the vast expansion of movie watching possibilities. With innovations like Netflix, more high-quality television series, movie on demand services and a bulk of illegal streaming sites, movie theaters are being challenged by other competitors. This makes the issue of obtaining an optimal movie schedule more relevant than ever.

In the early days of the movie industry, people simply had to go to the theater if they wanted to see a movie. In the ‘50s, television became mainstream and started showing films, followed by the invention of VHS. Although disturbing for the movie industry, these developments did not allow for showings of the most recent movies and were consequently no direct competitors of the movie theaters. Streaming services etc. are.

When looking at the box office results over the last few years, the number of tickets sold has been in decline, both for Belgium and the United States – as can be seen in the graphs on figure 1-1 and figure 1-2. However due to increasingly higher ticket prices, there has overall been a growing trend of the total gross in the United States. In Belgium the total box office gross has been going down this last decade, but the decline has not been as steep as the downward trend in visitor numbers. Furthermore, movie theaters make a lot of profit on concessions (TIME Magazine, 2009). Boosting the sales of snacks and drinks is therefore an important part of the exhibitor’s business strategy. These high revenues mean that the movie theater industry is not in immediate danger of bankruptcy – despite an overload of articles claiming otherwise (MakeUseOf, 2015; Coldiron P., 2013; Marion P., Gaudreault A., 2015) – but they must stay relevant. Hence exhibitors should schedule the movies people are most eager to see and deem worthy of a cinema visit.

The industry is not only changing in terms of sales numbers but also in the way people wish to experience a visit to the movie theater. Recently, exhibitors – in particular of big cinema chains – have been investing quite some money in new techniques like 3D, 4DX, IMAX and Barco Escape (Moviepulp, 2017). All these innovations facilitate a more immersive movie experience. 3D makes it possible for images to float all around the room, 4DX is an extension to this principle by adding smell and physical movement of the seats. Through IMAX, the movie gets portrayed on a bigger, more high quality screen combined with superior sound systems (imax.com, 2018). Lastly, Barco Escape uses three screens simultaneously to show the movie (techpulse.be, 2016). These three technologies require movie producers to film with specialized equipment, therefore these investments can only be as profitable as production companies allow them to be. Given the difficulties Barco Escape had in creating sufficient interest of the industry, the company behind the idea has terminated this technology in early 2018 (Dager N., 2018).

Furthermore theaters are not only investing in new technologies, but also in more luxurious experiences. Examples include private screenings and dinner at the movies (Lee J., 2015). The latter however places movie theaters into a completely different market, one where different rules and expectations apply, raising questions as to how fitting cinemas are for this business and increasing the risk (Toy S., 2018).

Both new technologies and adding luxury can lead to higher revenues, but they lay outside the scope of this thesis.

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$250,00 24

23 $200,00 22

$150,00 21

20

$100,00 Visitors Visitors Belgium Box office Box office Belgium 19 $50,00 18

$- 17 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Figure 2-1: Annual ticket sales and box office gross in millions for Belgium 2007 – 2017, source: FCB https://www.cinevox.be/nl/2016-was-geen-topjaar-belgische-bioscopen/; De Morgen https://www.demorgen.be/film/na- tegenvallende-cijfers-van-2016-belgen- gaan-weer-vaker-naar-de-bioscoop-b9e10630/; box office mojo https://www.boxofficemojo.com/intl/belgium/yearly/?yr=2017&sort=gross&order=DESC&pagenum=2&p=.htm (*assuming Belgium and Luxembourg follow the same trend. The original data on boxofficemojo.com shows the box office results for both countries combined.)

$12 000,00 1800 1600 $10 000,00 1400 $8 000,00 1200 1000 $6 000,00

800 Visitors Visitors US

Box office Box office US $4 000,00 600 400 $2 000,00 200 $- 0

Figure 2-2: Annual ticket sales and box office gross in millions for the United States 1980 - 2017, source: box office mojo, 2018, https://www.boxofficemojo.com/yearly/

The Belgian movie theater landscape is dominated by a few big players: Kinepolis, UGC, Euroscoop and Pathé. Especially Kinepolis with its 10 multiplexes, which together account for more than 130 screens, and involvement in many Belgian film productions is unavoidable when talking about movies in Belgium. Next there are some smaller cinemas, these are either arthouse cinemas in the metropolises or independent movie theaters in less populated cities. Contrary to the situation in a lot of other places, movie theaters in Flanders do not receive a lot of governmental support. This makes the exploitation of arthouse cinemas way riskier financially, even compared to Wallonia or Brussels. As mentioned by Patrick Deboes of Cinema Sphinx and confirmed by numbers from the United Kingdom’s Independent Cinema Office, arthouse movie theaters are economically deficient. This means that most arthouse movie theaters can only be profitable or even viable with the help of governmental support, which is due to the relatively small public they attract and the high costs involved in movie exhibition. First of

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there is the direct cost of renting movies from distributors, secondly quite a lot of indirect costs are involved with the exhibition of movies. The latter include personnel, cleaning, heating, projection and much more.

The overall worldwide box office gross of 2017 was 40.6 billion dollar, or 35.7 billion euro – which is a 5 percent rise in comparison with the previous year (statista, 2018). In the graph below the evolution of the worldwide box office revenues over the last 13 years is depicted.

Figure 2-3: Global box office revenue 2017, source: statista, 2018 https://www.statista.com/statistics/271856/global-box- office-revenue/

One can easily see that Belgian’s box office gross of €161.000.000 (UK Cinema Association, 2018) is only a small drop in the ocean. The most important movie markets are unsurprisingly the United States and China, which together account for almost half of the global box office revenue. The biggest European market is the United Kingdom, followed by France and Germany (statista, 2018). All this information can be found in figure 1-4. The United States and China are also major movie producing countries, in 2017 only India has produced more films (Screen Australia, 2018). Due to the extremely low ticket price of $0.80 on average, the Indian box office results are way lower compared to other major movie industries. As can be seen in table 1-1, the United Kingdom is only ranked 10th when it comes to movie productions, despite being ranked 4th in generated box office revenues. Again part of this can be explained by referring to the relatively high ticket prices, especially in comparison with non- western countries – with the exception of Japan. Another reason for this discrepancy may be the fact that the cultural distance between the United States and the United Kingdom is rather small. As proven

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by Moon S. and Song R. (2015) cultural distance between the production country and that of consumption largely relates to the success foreign movies might have.

Figure 2-4: Leading film markets worldwide - box office revenue 2017, soure: statista https://www.statista.com/statistics/252730/leading-film-markets-worldwide--gross-box-office-revenue/

Average Box Average Number ticket Admissions No. of Country office admissions produced* price (m) screens (US$m) per capita (US$)

India 1,986 1,600.0 0.80 1,980.0 1.5 11,209

China 970 8,270.0 5.10 1,620.0 1.2 50,776

US and 913 11,100.0 9.00 1,240.0 3.4 43,500 Canada

Japan 594 2,040.0 11.70 174.5 1.4 3,525

South 494 1,600.0 7.1 219.9 4.3 n.a. Korea

France 300 1,560.0 7.50 209.4 3.1 5,913

Spain 247 676.8 6.8 100.2 2.2 3,625

Germany 247 1,190.0 9.80 122.3 1.5 4,803

Italy 235 692.0 7.00 99.2 1.6 5,298

UK 212 1,650.0 9.7 170.6 2.6 4,309

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Average Box Average Number ticket Admissions No. of Country office admissions produced* price (m) screens (US$m) per capita (US$)

Turkey 148 239.1 3.4 71.2 0.9 3,013

Russian 108 913.0 4.30 212.2 1.5 4,793 Federation

Poland 71 284.8 5.0 56.6 1.5 n.a

Australia 41 920.5 10.80 85.0 3.4 2,210

New 27 134.7 8.50 15.8 3.4 421 Zealand

South 23 89.6 4.5 19.9 0.4 782 Africa Table 2-1 Global movie productions 2017, source Screen Australia, 2018

Compared to other European countries, Belgians spend slightly more than the average amount of money on movie visits, as can be seen in table 1-1. Concretely, Belgium is ranked 14th out of 33 European countries with an average of €14.19 per inhabitant, the overall European average amounts to €11.58. The comparative amount spend per inhabitant on movies in Europe is largely in line with the differences in purchasing power (index mundi, 2018) as can be seen in appendix A. Germany is the only country with greater purchasing power where inhabitants spend on average less money at the movie theater than Belgians. On the other side, only the United Kingdom, Israel and Finland have lower purchasing power and relatively higher box office grosses.

Country Gross Box Office 2017 Change 2017-2016/ % Per inhabitant 1 Norway € 133 264 000,00 -8.3% € 25,34 2 Denmark € 144 861 000,00 -4.1% € 25,11 3 Ireland € 113 800 000,00 5.8% € 23,79 4 United Kingdom € 1 454 590 000,00 2.5% € 22,03 5 Switzerland € 183 425 000,00 1.0% € 21,78 6 France € 1 380 600 000,00 -0.6% € 20,57 7 Sweden € 190 745 000,00 2.7% € 19,08 8 Finland € 98 300 000,00 8.8% € 17,86 9 The Netherlands € 301 900 000,00 4.9% € 17,68 10 Israel € 138 603 000,00 7.0% € 15,91 11 Austria € 130 200 000,00 -1.9% € 14,84 12 Estonia € 19 400 000,00 9.8% € 14,74 13 Luxembourg € 8 400 000,00 3.8% € 14,22 14 Belgium € 161 000 000,00 8.6% € 14,19 15 Spain € 597 000 000,00 -0.7% € 12,82 16 Germany € 1 056 100 000,00 3.2% € 12,80 17 Italy € 584 800 000,00 -11.6% € 9,65 18 Portugal € 81 700 000,00 5.8% € 7,92

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19 Czech Republic € 77 539 700,00 -0.3% € 7,33 20 Lithuania € 20 200 000,00 14.0% € 7,09 21 Latvia € 12 900 000,00 5.3% € 6,62 22 Poland € 249 674 000,00 10.8% € 6,58 23 Hungary € 64 144 000,00 4.1% € 6,56 24 Slovakia € 34 500 000,00 18.9% € 6,35 25 Greece € 65 000 000,00 0.9% € 6,04 26 Slovenia € 11 600 000,00 -0.6% € 5,61 27 Russia € 705 599 000,00 9.5% € 4,88 28 Croatia € 18 089 000,00 5.8% € 4,35 29 Bulgaria € 25 920 000,00 4.7% € 3,65 30 Romania € 57 138 700,00 10.3% € 2,91 31 Turkey € 136 948 000,00 25.9% € 1,72 32 Montenegro/Serbia € 12 349 300,00 6.4% € 1,14 33 Bosnia and Herzegovina € 2 910 000,00 21.0% € 0,83 Table 2-3: European box office – 2017: European box office – 2017, source: UK Cinema association 3 Literature review Throughout the last decades some amount of research has been conducted into the issue of movie scheduling. These papers and models will be the starting point of this dissertation. 3.1 Predicting movie Demand Anita Elberse and Jehoshua Eliashberg (2003) have investigated five different movie markets to come to a demand and supply model for the movie industry. The markets under consideration were the US, UK, France, Spain and Germany; the movies in the dataset were all US releases. The four foreign markets were chosen for their importance and similarity in data collection. In order to predict first week demand, Elberse and Eliashberg consulted the website of Hollywood Stock Exchange. Out of this research they concluded which factors play a relevant role in the prediction of demand. US performance for example, is a trustworthy predictor for demand in the UK, Spain and Germany. Which makes it safe to conclude this fact is of relevance for the Belgian market as well. However, not all movies score as good abroad – more specifically in Belgium – as they do in the United States, for example films with an ‘American theme’ (Moon S., Song R., 2015). Besides, some movies get released around the same time in Belgium as in the United States, which removes the possibility of using foreign box office performance as a predictor. In figure 3.1 on the next page, an outtake of the Hollywood Stock Exchange website from October 29, 2018 is given, in order to get a clear view of what kind of data one can obtain through this site.

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Figure 3-1: Stock value for movies opening week 45-46, 2018, source: Hollywood stock exchange, 2018

The price a stock – or movie – at the Hollywood Stock Exchange holds can be seen as a predictor for future box office revenues. On this site, people can trade and purchase movies with fictional Hollywood dollars. The stock values in this simulation game are quite accurate predictors for actual box office performances (Elberse A., Gilbert S.J.; 2005). Here one notices for example that big budget movie Fantastic Beasts: The Crimes of Grindelwald is quoted at H$203.73, whereas the Oscar bait movie Widows strands at H$45.43 and indie movie Weightless is valued at H$0.21. Of course these stock prices do not translate one on one into movie visitors, but they do give a useful indication to the exhibitors. However, due to the European Union’s new GDPR legislation, this site cannot be consulted in real time from within a country which is a EU member. Therefore, this convenient site will not be used to predict the demand for movie exhibition in Belgium.

As Suman Basuroy, Subimal Chatterjee, and S. Abraham Ravid’s state in their 2003 paper, negative reviews have an increasingly smaller influence over time, and those negative reviews are comparatively more harmful to a film than positive reviews are helpful. Popular stars and big budgets can increase box office revenue for a poorly reviewed film but do not noticeably increase profits for positively reviewed films.

The number of movie visitors can be predicted based upon three characteristics: the box office results of previous weeks, movie characteristics (see last paragraphs) and seasonal effects like the weather or holidays (Eliashberg J., Heggie Q., Ho J., Huisman D., Miller S.J., Swami S., Weinberg C.B., Wierenga B.,

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2009). The main goal is to maximize these numbers which will consequently maximize the overall profit of the movie theater.

Other significant factors which will lead to more moviegoers are star power and movie buzz (Karniouchina E.V., 2011). For her research Karniouchina relied on information from boxofficemojo.com, IMDB and Yahoo!. She concluded that more than half of the moviegoing population led their movie pick be influenced by others’ opinions. Both star power and movie buzz are leading causes when it comes to box office results and therefore screen allocation. Amplifying these results are the findings of Joonhyuk Yang, Wonjoon Kim, Naveen Amblee and Jaeseung Jeong (2012), who stated that word of mouth – which is basically the same as movie buzz – has a positive impact on box office results. No matter the content, bad publicity is still publicity.

Over the different weeks, one generally assumes an exponentially decaying course of the movie demand. On average, the opening week generates approximately 40% of the total profit a movie makes during its entire runtime (Einav L., 2007). Given these extreme differences concerning demand for the first week in comparison with later weeks, Karniouchina (2011) made the decision to split the demand model into two different equations, with different decision variables. In her research article, she looked into the predicting value of movie buzz and star buzz. Both variables depend upon a list of other explanation variables, amongst others sex appeal and Academy Awards for star buzz. The researcher has concluded that both star and movie buzz have a considerable impact on the box office results for the opening week. Throughout later weeks, movie buzz keeps reigning in more viewers, however star buzz generates lower results after week one. Furthermore, the interaction between both buzz variables is relevant and positive for both models – with exception for week two. 3.2 Scheduling movies in the cinema In order to maximize the overall profit of a cinema, one has to take into consideration a certain number of constraints (Eliashberg J., Heggie Q., Ho J., Huisman D., Miller S.J., Swami S., Weinberg C.B., Wierenga B., 2009) :

1. Theater capacity 2. Movie runtimes 3. No two movies on the same floor starting at the same time 4. Penalty for switching movies on a particular screen 5. At least one movie starts every 20 minutes 6. Theater closing time (includes cleaning)

The third and fifth constraint seem rather odd and should possibly be reconsidered in this paper. The writers have chosen to include those because of the potential queuing and accompanying unbalanced workload. When looking at most of Belgian movie theaters however, one can see that the management has chosen to show a majority of their movies at the same exact time, to ensure visitors of the possibility to make a last-minute decision. The fourth constraint is outdated, since nowadays every movie is digital and therefore switching to another movie for a next showing does no longer require a lot of time and effort (Eliashberg J., Elberse A., Leenders M.A., 2006).

Usually one assumes movies to be played in consecutive weeks. However when exhibitors let go of this constraint, profits can be increased enormously (Dawande M., Drobouchevitch I., Rajapakshe T., Sriskandarajah C., 2010). Dropping this constraint makes the problem harder to solve, given that more movies have to be considered when constructing the weekly schedule.

Given the vastness of the problem – especially when considering the many movie characteristics and other variables that might play a role in the demand function – some researches have suggested to

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solve the scheduling problem in consecutive stages through column generation (Eliashberg J., Heggie Q., Ho J., Huisman D., Miller S.J., Swami S., Weinberg C.B., Wierenga B., 2009). First the total problem gets divided into smaller reduced subproblems with only some of the variables – which are also called columns – included. Second, an optimal solution for one such reduced problem gets found. Third the dual solution to this reduced problem creates new columns. When it becomes clear that the optimal solution found by this reduced problem can no longer be enhanced by adding new variables, this is the optimum of the overall problem.

SilverScreener is a pioneering model for movie scheduling developed in 1999 by Sanjeev Swami, Jehoshua Eliashberg and Charles B. Weinberg. Based upon several predefined parameters – genre, Motion Pictures Association of America rating, sequel, stars, distributor and managerial judgment – their integer program can be used for both decision phases of the movie scheduling problem. This scheduling issue can be compared to a parallel machine scheduling problem with the addition of a time-indexed formulation. In this comparison, movies are viewed as jobs and screens as parallel machines. The parallel machine scheduling problem is a classic optimization issue and a lot of research into optimizing it has been conducted in the past (Li K., Shi Y., Yang S. and Cheng B., 2011). The concept is that a certain set of jobs has to be performed on a limited amount of machines and each job has a specific known-in-advance duration. These machines can run simultaneously, thus parallel scheduling. Just like there is only one job that can be executed at every machine at the same time, there is also only one movie that can be played at a certain time instance on every screen. These similarities make it therefore possible to use analogous methods when solving the movie scheduling issue.

There are however vast differences between both scheduling problems. The goal in machine scheduling is usually to minimize the total production time, taking some constraints into consideration. The movie scheduling goal however is to maximize the total profit, which depends both on the total number of visitors and the predefined split of profits between exhibitor and distributor. How much money a movie yields for the exhibitor is time-dependent, as discussed in section 3.1. When scheduling a movie one does not know in advance how many weeks it will take until the movie demand is saturated, whereas for jobs, the processing time is usually known or at least easily estimated. Another difference is that jobs only contribute value once finished, movies scheduled in a theater start bringing in money right from the start. Lastly, all jobs included in the model have to be processed, but not all movies have to be shown.

The inclusion of a time-index comes down to dividing the total planning horizon (e.g. a year) into smaller time periods (e.g. weeks). The time-index can then be used to define during which time periods a certain movie has to be screened.

The first decision phase considers the creation of a movie selection plan for the whole season. Of these movies, they estimate box office results based upon previous movies played in that theater with similar characteristics. The second phase is the weekly schedule made and updated throughout the entire season. For this program to work, some assumptions have to be considered. One of these assumptions is that weekly revenues of each movie can be estimated in advance. However far stretched this may seem, empirical evidence from Variety (1998) has actually proven that movie theaters are quite good (R2=0.96) in predicting these numbers. Another assumption for SilverScreener to be successfully implemented is that all screens of the movie theater have the same amount of seats.

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SilverScreener Parameters: ▪ W length of planning horizon ▪ H number of screens in the multiplex ▪ N total number of movies considered during the planning horizon, indexed by j = 1 ,…, N ▪ rj release date of movie j ▪ dj due date (if applicable) of movie j ▪ Pjiw profit received by the exhibitor if xjiw is equal to 1 ▪ GROSSjw box-office gross revenue generated by movie j in week w ▪ POPjw concession profit generated by movie j in week w ▪ VCjw variable cost due to movie j in week w ▪ FCw fixed cost of multiplex in week w ▪ EXSHAREjw exhibitor’s share of box-office revenue for movie j in week w ▪ OPDj obligation period of movie j ▪ C house nut, a small negotiated amount, which the exhibitor receives from the distributor. It does not necessarily bear any relationship to the theater’s actual expenses, and is only meant to allow for some cushion in the exhibitor’s profit margins. ▪ w = rj, …, dj – SCRji + 1, week

▪ θju is a logical condition given by

θju = (0.9 * (GROSSjw – C) > (1 – EXSHAREjw) * (GROSSjw), 1 𝑖푓 푋 = 푇푅푈퐸 ▪ IX = { 0 표푡ℎ푒푟푤𝑖푠푒 ▪ kj = dj – rj – OPDj + 1, maximum possible number of weeks movie j can be shown

beyond its obligation period starting in rj or any feasible week thereafter

▪ SCRji = OPDj + I, total screening period for movie j if it is shown for i weeks beyond

its obligation period, where i = 0, …, kj.

▪ qj = w – SCRji+1, weeks since release of movie j

Decision variable xjiw binary 0–1 variable which takes value 1 if movie j is scheduled for i weeks beyond its obligation period starting in week w

The profit function for each individual movie becomes:

푤+푆퐶푅푗푖−1 Pjiw = ∑푢=푤 . (-FCu + POPju – VCju) + Iθju * {0.1 * (GROSSju – C) + C} + (1 – Iθju ) * EXSHAREju * GROSSju Which in turn leads to the overall profit function, the one to maximize:

푁 푘푗 푑푗−푆퐶푅푗푖+1 max ∑푗=1.. ∑푖=0. ∑푤=푟푗 .Pjiwxjiw subject to

푘푗 푑푗−푆퐶푅푗푖+1 ▪ ∑푖=0. ∑푤=푟푗 .xjiw ≤ 1, j = 1, …, N (only played in consecutive weeks) 푁 푘푗 푤 ▪ ∑푗=1. ∑푖=0. ∑푞푗.xjiqj ≤ H, w = 1, …, W ▪ rj ≤ qj ≤ dj – SCRji + 1; j = 1, …, N; i = 0, …, kj (restricts qj to feasible values)

▪ xjiw ϵ {0,1}

They have coded this in AMPL and tested their theory on data from Variety and the 84th St. Sixplex in New York. Comparing these results with the schedule that has actually been used, shows that SilverScreener leads to significantly higher profits – respectively 37.7% and 31.4% with ex post and ex ante data. Furthermore, the schedule suggested by these researchers consisted out of less movies and had them play for a longer time compared to the schedule implemented by the management.

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Particular to the movie industry is the way the profits are split between exhibitors and distributors. Throughout the first weeks, the distributor gets a larger percentage and later on more money goes to the exhibitor. Making it thus more profitable for the exhibitor to give movies a longer runtime. Also contributing to the profit margin, is the money made on concessions which goes entirely to the exhibitor. The split between exhibitors and distributors varies between different movies, depending on the box office potential (e.g. blockbuster versus indie movie) and how close to opening week the exhibitor wants to show the movie (Wozniak K., 2012). The contractual agreement between distributors and exhibitors often includes several clauses concerning release date, due time and the minimum number of weeks a movie should be scheduled (Dawande M., Drobouchevitch I., Rajapakshe T., Sriskandarajah C., 2010).

As an extension to SilverScreener, Sanjeev Swami, Martin L. Puterman, Charles B. Weinberg (2001) suggested an implementation as a Markov Decision Process. In a Markov Decision Process, at certain predefined points in time, one looks at the current state of the system and based upon the information at hand, picks a future direction. In order to simplify this model, the researchers have chosen to give every movie a number, which refers to expected revenues. Throughout the weeks, the expected box office result of a certain movie diminishes, so after every week the different options should be reconsidered. The current solution can then be compared to new releases as well as movies that used to have a lower rank, but might have surpassed the movie’s expected return for the coming week. This model, however, can only be used for a one-screen-theater. The idea behind it might nevertheless still proof valuable as a starting point for decision making later on in this dissertation.

In later research (Eliashberg J., Swami S., Weinberg C.B., Wierenga B., 2009) SilverScreener got adapted for multiple multiplexes. The models had to be combined with more intuitive thinking due to differences between theaters and movies which require a more specific dealing. Again, the rolling horizon approach was used. Special cases were constructed to include children’s movies and matinees. The first led to the creation of fictional screens in the scheduling phase, the latter caused the program to add extra shows for the least profitable movies. These additions to the SilverScreener model have resulted in a 4.8% rise in net margin when implemented in Amsterdam.

Recently researchers have been experimenting with Genetic Algorithms (Goff Inglis K., Zolfaghari S., 2017) in order to solve the movie scheduling issue. However applying these kind of algorithms is particularly interesting for solving the macro problem, less during the season when flexibility is of considerable importance (Dawande M., Drobouchevitch I., Rajapakshe T., Sriskandarajah C., 2010).

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4 Field research Some interviews with Belgian cinema exhibitors were conducted in order to create a more clear view of how movie scheduling goes down in reality. The excerpts of these interviews can be found in appendix B. Past researchers have focused either on generalizations or their home market, but these markets are very different from the Belgian movie landscape. Moreover even in Belgium there are vast regional differences, due to the fact that culture and media falls under regional jurisdiction and therefore the situation in Flanders is very different from the one in Wallonia or Brussels. The movie landscape is largely influenced by the policies executed by the government. In Flanders, as mentioned in the introduction, the level of governmental support is very small, resulting in few arthouse cinemas. Most arthouse movie theaters in Belgium can however rely on a certain amount of financial support from the Europa Cinemas Network. This Association is created by the European Commission and CNC. In exchange for the assurance that theaters will show a certain number of European movies, this network offers both operational and financial support to 1204 movie theaters all over the world (Europa-Cinemas.org, 2018).

Movie exhibitors can only exist through their relationship with distributors. Without negotiated contracts, exhibitors cannot play any movie. The number one condition in order to be able to play a movie is the issue of whether there is a distributor in the Benelux who has bought the rights to this movie. If this is the case and the movie exhibitor is interested, contract negotiations can start. This contract determines the profit split as well as a minimum amount of showings. The most regular scenario for arthouse theaters is a 50/50 split between distributor and exhibitor during the first three weeks (Jan-Willem Van-Eemeren, Cinema Cartoon’s). Then going down with 5 percent on a weekly basis, making it more interesting for exhibitors to play an older movie. However these numbers can differ based upon preliminary expectations. Sometimes special requests considering the time or screen a movie is to be played at can be included in the contract. Furthermore, big players like Kinepolis, UGC and Euroscoop Group will receive better contract conditions. Not only because they are more powerful, but also because they can show a movie in multiple cinemas, hence bringing in more money for the distributor even with a for them less profitable contract. Distributors are long term partners, consequently sometimes movie exhibitors have to show less interesting movies in order to maintain good relations with these partners. This too mostly applies to smaller theaters.

Cinema weeks in Belgium start on Wednesday and end on Tuesday. The weekend generates by far the most profit, consequently the weekend box office grosses are a determining factor in movie scheduling for the next week. Every Monday, exhibitors renegotiate contracts with distributors, thereby taking the weekend grosses under consideration. Next, the movie schedule for the upcoming week is made and shared with the public.

As becomes clear when looking at the different movie schedules of different movie theaters – and definitely when talking to the exhibitors – every cinema has its own very specific vision when it comes to scheduling. 4.1 Interviews movie exhibitors 4.1.1 Cinema Cartoon’s Antwerp – Jan-Willen Van Eemeren Cartoon’s is a cinema in the city center of Antwerp, Belgian’s second biggest city. During its 40 year of existence, this movie theater has faced bankruptcy three times. The history of cinema in Antwerp is rather turbulent. After a prosperous start, cinema in Antwerp had a major drawback in the 90’s when the Rex concern, which at the time owned most of the movie theaters in the city went bankrupt. At the time, many people assumed it would be the end of cinemagoing in Antwerp all together. However,

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Cinema Cartoon’s fought back, found a new owner and is now the only arthouse movie theater in the city.

Today the movie exhibition scene in Antwerp is still very crowded, with two multiplexes – UGC in the city center and Kinepolis (formerly known as Metropolis) in the Luchtbal district – and a handful smaller, arthouse cinemas. This turns every decision regarding what movie to play into a case of profiling. Cinema Cartoon’s’ location in an old chocolate factory in the city center and its long history, which makes the theater breathe authenticity, puts it in sharp contrast with the multiplexes.

After its last bankruptcy in 2013, Cartoon’s has been taken over by the Lumière Group, a film distributor which owns another cinema in Bruges. Although the vast distinction between the cinema landscape of both cities – Bruges has only the Kinepolis multiplex at the outskirts of town and Lumière in the city center – this seems to be a successful collaboration.

Jan-Willem Van Eemeren has been working at Cartoon’s since 2015, he is responsible for the exploitation and determines the movie schedule. Cinema Cartoon’s has three screens at their disposition, which naturally does not give them a lot of programming options. The Lumière Group employs one programmer, Alexander Vandeputte who develops the weekly schedules for both movie theaters. More precisely, he decides which movies to play and offers suggestions to Mr. Van Eemeren as to what would work on which screen. Afterwards, Mr. Van Eemeren creates the exact movie schedule. Having one person responsible for two theaters offers the gained benefit of a stronger position in negotiations with movie distributors.

These negotiations lead to a certain profit split between distributor and exhibitor, the most common scenario at Cartoon’s is the following: 50% to the distributor in the first three weeks, then 5% less every week. However this may differ a little, depending on the expected box office results. Next, these contracts include a minimum number of screenings a movie should get. Given the fact that distributors are long term partners, Mr. Van Eemer occasionally chooses to show a movie which he knows will not attract large audiences in order to maintain a good relationship with the distributor.

Furthermore, distributors demand movies to be shown throughout an uninterrupted number of weeks. During this time, movies get less and less screenings as the weeks pass by.

Interesting and rather unique is the fact that Lumière Group works with monthly schedules, in contrast to the common weekly ones. This gives them the opportunity to tell the public well in advance what to expect and reduces the negotiations with distributors. Nevertheless, there is a downside to this way of planning as well, being that the movie schedule becomes absolutely inflexible. In case of unexpected hits or flops, no changes can be made. Whether a movie attracts a big audience is largely due to external factors, however the exhibitor can push the numbers by decent planning and maintenance of their cinema rooms.

Since Cartoon’s is an arthouse cinema, the diversity of the movies they show is of great importance. The theater mainly screens European and independent films. Given the presence of all the competitors in the city of Antwerp, Mr. Van Eemeren sometimes decides not to show a particular movie when another theater is already planning on screening it.

According to Mr. Van Eemeren box office numbers of other countries where the movie has been released earlier can sometimes give an indication of the quality, however there generally are big cultural differences.

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4.1.2 Sphinx Cinema Ghent – Patrick Deboes The Sphinx is one of the two arthouse cinemas in Ghent, the other one is Studio Skoop, which is mentioned below. Given the fact that Ghent is a relatively small city when it comes to moviegoing audience, both cinemas have a silent agreement that if one theater plays a certain movie, the other one will not. Besides these arthouses, there is also a Kinepolis multiplex in the city of Ghent, but this cinema mostly appeals to a different audience and is run with a different philosophy.

Patrick Deboes has been working at the Sphinx for more than 20 years. Originally he started out as an employee, later on he has gained more and more responsibilities and now he is responsible for the exploitation and the creation of the weekly movie schedule. What sets Mr. Deboes way of planning apart from all others, is that for five years now, they have been using a unique computer program. More specifically, Mr. Deboes has hired a computer programmer who has created a program of interacting Excel files through which scheduling movies becomes way easier. His philosophy is to start with the cinema screens at hand and not with the movies. For every weeks he creates so-called modules which consist out of a combination of movies which together take up the whole playtime of a certain screen. By using this method, Mr. Deboes, claims to be more flexible concerning events or other special screenings. Contrary to many of its competitors, the movie schedule of Sphinx Cinema looks different every single day. It is only thanks to the recent invention of the digital register and digital projection that such a flexible schedule became possible.

The reason why they have switched to this new way of programming is multi-faceted. Firstly, Mr. Deboes had noticed that the late night showings attracted less and less visitors. Secondly, in the beginning most movies get too many screens and later on it often happens that the demand for a certain movie exceeds the supply. Thirdly, Sphinx seemed to only have movies that performed either extremely good or extremely bad at the box office. This is of course an issue because the mediocre films do not get a fair chance at success. These realizations thus led to the above mentioned programming innovation.

As stated earlier, cinemas in Flanders do not get governmental support, which is rather problematic for arthouse theaters like the Sphinx. Mr. Deboes declared that the only reason they can stay afloat is thanks to their profitable café. 4.1.3 Studio Skoop Ghent – Walter Vander Cruysse Studio Skoop first opened the doors of its cinema and accompanying café in 1970. Ever since the beginning, Skoop has offered a stage for alternative movies. However due to the lack of sufficient financial support, the theater regularly shows mainstream movies as well. Nevertheless Mr. Vander Cruysse does not consider Kinepolis a competitor, claiming that both theaters mainly attract different audiences.

Walter Vander Cruysse has been working for Studio Skoop since 1976 and has been responsible for the exploitation since the 80s. His most important tasks are looking out for new releases and negotiating with distributors. These negotiations generally occur in a very smooth fashion. The profit split is in line with what Mr. Van Eemeren has mentioned. Therefore it is safe to assume that these numbers are somewhat standard for arthouse cinemas in Belgium. In order to present the best movies to the public, Studio Skoop tries to always have an employee watch the movies before their release date. This however is not always possible, in these cases they base the decision of whether or not to show a film on professional reviews. Another selection criterium is the quality of a director’s previous movies. When they pick a movie to play in the Skoop, this one is shown right from its release date.

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Contrary to what is assumed in some of the papers mentioned in section 3.1, not all movies in their program can be chosen at the begin of the season. This due to the fact that release dates tend to change or only be determined a few weeks in advance. This is particularly true for smaller independent movies which can be easily moved around by distributors depending on the overall supply.

Under normal circumstances, all movies start at the same time, however when movies are over two hours long, the evening screening starts earlier than 8 p.m. because otherwise it would be too late to show a late evening movie afterwards. Just like the other exhibitors, Mr. Vander Cruysse always uses all the screens, except in the case when one is rented out for a movie-related event. 4.1.4 Euroscoop Genk – An Van de Casseye In contrast with the other exhibitors in this section, Euroscoop Genk is not an arthouse cinema but part of a cinema chain. Euroscoop is active in six Belgian cities: Genk, Maasmechelen, Lanaken, Sint-Niklaas (under the name Siniscoop), Namur (Acinapolis) and Louvain-la-Neuve (Cinéscope). Next, Euroscoop holding also owns three multiplexes in the Netherlands, making them a rather important player in the Benelux and the third largest exhibitor in Belgium after Kinepolis Group and UGC.

The cinema in Genk is – just like the one in Maasmechelen – located in an old mine building, adding an authentic element to these rather new and large movie theaters. Interesting also is that these theaters have both small and large cinema rooms, opening up a vast range of planning possibilities.

Since Euroscoop has a lot of screens at its disposition, almost all the movies from big film studios are shown, because these movies will most likely attract a crowd. For the smaller movies, the situation is similar to the other exhibitors, where someone watches the film in advance in order to make a quality assessment.

An Van de Casseye is the one responsible for the movie scheduling in all Euroscoop theaters. Every city has a different audience, therefore creating different movie scheduling challenges. In Genk for example, Euroscoop often offers alternative movies next to the mainstream circuit. These movies would barely get any attention in Lanaken or Maasmechelen. The latter has a large Turkish population, hence Ms. Van de Casseye regularly shows Turkish movies there.

They use a rather extended programming tool which simplifies the scheduling. However as Ms. Van de Casseye has stated, a computer program will never be able to get all the details right. Meaning that even though some movies may not draw big crowds, they are still deserving of an audience. Another example is that older people as well as small children prefer cinema rooms which are reachable without taking the stairs.

Despite the high expenses related to occupying a screen, they always use all screens at Euroscoop. This because according to Ms. Van de Casseye, a movie theater is only as attractive as its overall program.

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5 Predicting Demand in R 5.1 Parameters Based upon previous research (Karniouchina E.V., 2011; Eliashberg J., Heggie Q., Ho J., Huisman D., Miller S.J., Swami S., Weinberg C.B., Wierenga B., 2009) and interviews conducted with the four Belgian movie exhibitors mentioned in section 4, the following parameters were used to define the demand model:

Dependent variable:

▪ Box Office Weekend x: the total weekend box office result for the whole Belgium during week x a movie is being shown in Belgian theaters. As mentioned before, weekend results are generally used in the industry as a means to determine future demand. The weekend is interpreted as the period from Friday till Sunday.

Independent variables:

▪ Star: the sum of the bankability of all stars involved in a certain movie. These values are achieved through the American website The Numbers (2018). Here a list of star scores can be found in which actors are appointed a certain score based upon the box office performance of their movies in the last three years. The point system gives actors a score of 100 if they appeared in the highest grossing movie of the year, 99 for the second most profitable one etc., this for all the movies in the top-100. In order to avoid interference, for the dataset of 2014, the star scores of 2013 were used. Given the fact that this is an American website which only tracks Hollywood stars, no data for foreign or local Belgian actors is available. The average star value of American movies in the dataset is 189. Foreign and local movies were given a score of either 0, 189 of 378 depending on the relative fame of the people involved in the movie. ▪ Genre: every movie can be placed in one or more categories based upon its genre. These film genres are: action, adventure, animation, biography, comedy, crime, documentary, drama, family, fantasy, history, horror, music, musical, mystery, romance, science fiction, short, sport, thriller, war and western (imdb.com). ▪ Sequel: 0 or 1, whether the movie is a sequel (or prequel) to a previously released one. This does not concern remakes. ▪ US: 0 or 1, whether a movie was produced in the United States of America. ▪ Local: 0 or 1, whether a movie was produced in Belgium. ▪ Foreign: 0 or 1, if a movie was not produced in Belgium nor the United States. ▪ Reviews: the weighted average score (out of 10) the movie has received on imdb.com, the leading website in movie related information. Here IMDb members can review films and based upon multiple variables the website calculates the weighted average rating in order to counteract vote stuffing. Today’s score is largely in line with reviews a movie has received at the time – capturing both the appreciation of the general audience and critics. Thanks to the internet, audience members can share their opinion of a certain movie with the whole world right from the release. In the case of frequent reviewers, e.g. bloggers or youtubers, it is even possible that they are granted early screening access together with professional journalists. ▪ Long: 0 or 1, depending on whether the movie is longer than 2 hours. This length was chosen because of the fact that multiple movie theaters charge customers extra when the play length surpasses the two hour mark. ▪ Adaptation: refers either to the adaptation of a book, series, video game or theater piece, or the remake of an old movie. No ‘based on a true story’ movies.

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▪ Budget: the total movie budget in million dollars. With the term movie budget, one usually refers to the complete production budget, excluding the promotion and distribution costs (imdb.com). This is also the definition used for this thesis. It was chosen to use dollars as unit due to the fact that for the majority of movies, the budget shared with the public is represented in dollars. Generally, one considers a movie a success once its worldwide box office results surpasses the double of its production budget. This due to the fact that promotion costs often skyrocket. ▪ Buzz: in order to get a clear overview about the buzz a specific movie created, Google Trends was consulted. This tracks how much a certain topic or query was looked up during a chosen time period and in a certain region. The region under consideration was Belgium and the period 15 days before the Belgian release day of the movie in question until 100 days after. The buzz of almost every movie peaked in its opening week. Given how Google trends only shows relative results, the numbers were compared to the popularity of the search of the color grey. After all, this is a reasonably constant amount over the whole year and thus allows you to compare results of all the movies over their different time periods. ▪ Season: moviegoing is a seasonal phenomenon, depending on the time of the year, expectations considering ticket sales should be adapted. The four classic seasons were taken as parameters: spring, summer, autumn and winter. ▪ Award: four film awards were taken into consideration. On the one hand there are the two largest American film awards: the Oscars – or Academy Awards – and the Golden Globes. On the other hand, there are the awards handed out at the two most prestigious European film festivals: Venice and Cannes. Each movie gets a score based upon the number of festivals and award shows it has won prices at, thereby an Oscar win counts for two given the fact it is the most well-known award in the movie industry. Naturally this score can increase over the time a movie is in theaters, the Academy Awards for example, were handed out on March 2, before the start of the 10th week of 2014. Therefore films which had left the box office before they were presented with an award have a score of 0. ▪ Holiday: 1 or 0 according to whether or not this weekend falls in a school vacation. 5.2 Dataset All the 386 movies shown in Belgian theaters between Wednesday November 27, 2013 and Tuesday November 25, 2014 are used to estimate the demand function. The weekend box office results for the whole of Belgium can be regarded as a close approximation of the total movie demand for the weekend at hand. Naturally movies that were not shown anywhere could not result in box office results, but by taking the numbers of the whole country this effect is already leveled out for a big part. Most information concerning these films could be found on the Internet Movie Database page (imdb.com) and the box office results for Belgium are available at boxofficemojo.com.

One should take into consideration that the available box office results are for Belgium, which consists out of three completely different regions, with each its own specific characteristics when it comes to cinemas. On the one hand as a consequence to the different policies, on the other hand because of cultural differences. These cultural differences, although larger between the regions, also exist when comparing one city or village with another. Some cities house a lot of students, some have a large immigrant population, some have a big cinema background… All these factors will lead to variations between movie preferences, making every movie theater unique and therefore in need of an adapted, unique movie schedule.

In accordance with what the interviewed movie exhibitors have said, movies are almost always shown during consecutive weeks. Yet, there were a few exceptions to this rule, but in most occasions an easy

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explanation was at hand. This either concerns movies which have been in theaters for a very long time, e.g. Marina, which was in Belgian theaters for a total of 29 weeks, or small documentary features which only attract a very select public. However considering that these are numbers of the whole country, it is also possible that for example cinema A showed the film from week 1 till week 4 and cinema B only started playing the movie 6 weeks after its release date. Despite the fact that most movie theaters prefer to show films immediately when they have been released, this does occasionally happen with smaller films. For example, in 2018 the movie Disobedience premiered in Studio Skoop mid-June (UiT in Gent, 2018) but was only released in Euroscoop Genk at the end of October (euroscoop.be). This was possible given that both theaters have virtually no overlapping audience and the movie has created but a little buzz. Furthermore since these are the weekend numbers, movies which have only been shown during weekdays – meaning Monday to Thursday – were not concluded in these numbers. Again, this is largely leveled out by looking at the overall results for the whole country.

During the period under consideration, the movie The Pirate Fairy spent the longest time in Belgian theaters, 34 weeks. The majority of the movies however, has a way smaller runtime, more than half of the movies played for less than seven weeks. 46 percent of the movies in the dataset were American productions, nevertheless their market share was with 77 percent a lot more impressive. Belgian movies account for 7 percent of the productions and 10 percent of the total box office gross. Foreign movies usually attract a smaller audience, as these numbers – with 47 percent of the movies and only 12 percent of the audience – confirm.

Not all movies follow a similar pattern at the box office, despite the fact that the weekly demand practically always follows a downward trend. Some movies open very big and promising, sometimes even claiming the number one spot for the weekend, but disappear from the charts within a few weeks. Others have a great staying power, despite only opening with moderate results. Examples are award movies like Blue Jasmine, which entered the box office as the ninth highest grossing movie of the weekend, but only left after 18 weeks.

10 percent of the movies that had a Belgian release during this period are sequels and 27 percent is an adaptation. This is in line with the current feeling of fatigue experienced by a lot of moviegoers and exhibitors. Many of whom get annoyed by the lack of original content at the box office. Next, almost one out of five movies in the dataset has a duration over two hours. Blockbusters Frozen and The Wolf of Wall Street unsurprisingly generated the most movie buzz.

Most movie budgets could be found on imdb.com. The budgets for 2014 varied from 100.000 dollar for the Belgian Image, Adil El Arbi and Bilall Fallah’s feature film debut, to 246,5 million dollar for the Spider-Man sequel The Amazing Spider-Man 2. For some movies, no data could be found. However, according to statista.com, the average production budget for movies produced in France in 2014 was 3,9 million euros or 5,2 million dollar. In Germany this was 4,4 million euros or 5,8 million dollar and in Italy 1,9 million euros or 2,5 million dollar. For the United Kingdom, the average production budget for feature films was 6,2 million pound or 7,9 million dollar (S. Follows, 2014). According to an article on the movie related Parlay Studios blog, the average budget of a feature length US movie lies between 70 and 90 million dollar. Given how this is a 2017 article, the lower bound was taken. After contacting the VAF (Flanders Audiovisual Fund), average numbers for Flemish movie productions in 2014 were shared. The VAF is an initiative of the Flemish regional government which helps financing Flemish productions, up to 400.000 euros per movie and with a total annual budget of 4,5 million euros. The production costs of a standard Flemish film amounted to 1.781.254 euro or 2,4 million dollar in 2014. Due to confidentiality reasons, no specific numbers could be obtained. The fund has a counterpart in Wallonia, named Wallimage, whose budget amounts to 5,5 million euros annually with the same upper bound of 400.000 euros per production (Belgium.be). The 15 movies for which no exact budget could

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be found nor were produced in one of the above mentioned countries are appointed a budget in accordance with the average budget of the other foreign movies in this dataset, 7,7 million dollar.

In the dataset, less than 5 percent of the movies belong to the genre documentary, history, horror, music, musical, short, sport, war or western. Therefore these parameters will not result in reliable results and should be removed from the demand equation. 5.3 Hypotheses The explanatory variables are plotted against the box office results of weekend 1 in order to obtain null hypotheses concerning the linear regression method. Furthermore the box office results of later weekends are plotted against those of the preceding weekend.

▪ Positive correlation between star and box office results of weekend 1. ▪ Genre ✓ No clear relation between genre action and box office results of weekend 1. ✓ Positive correlation between genre adventure and box office results of weekend 1. ✓ Negative relation between genre animation and box office results of weekend 1. ✓ Negative correlation between genre biography and box office results of weekend 1. ✓ No clear relation between genre comedy and box office results of weekend 1. ✓ Weak negative correlation between genre crime and box office results of weekend 1. ✓ Negative relation between genre drama and box office results of weekend 1. ✓ Negative correlation between genre family and box office results of weekend 1. ✓ No clear relation between genre fantasy and box office results of weekend 1. ✓ No clear relation between genre mystery and box office results of weekend 1. ✓ Negative correlation between genre romance and box office results of weekend 1. ✓ Positive correlation between genre science fiction and box office results of weekend 1. ✓ No clear correlation between genre thriller and box office results of weekend 1. ▪ Positive correlation between sequel and box office results of weekend 1. ▪ Production country ✓ Positive correlation between US-produced and box office results of weekend 1. ✓ Weak positive correlation between made locally and box office results of weekend 1. ✓ Clear negative correlation between foreign and box office results of weekend 1. ▪ Positive correlation between reviews and box office results of weekend 1. ▪ No clear relation between movie length and box office results of weekend 1. ▪ No clear relation between adaptation and box office results of weekend 1. ▪ Positive correlation between production budget and box office results of weekend 1. ▪ Strong positive correlation between movie buzz and box office results of weekend 1. ▪ Weak negative correlation between award and box office results of weekend 1. ▪ Seasonality could not be plotted given the fact that this is not a numerical variable. ▪ Small positive correlation between holiday and box office results of weekend 1. ▪ Clear positive linear correlation between box office results of consecutive weekends.

These null hypotheses are now tested by looking at the t-values resulting from the linear regression. Not all three variables related to the production country should be included in the regression model. Given the fact that they are complementary collections, the presence of all three of them would lead to multicollinearity. Therefore the choice was made to exclude the explanatory variable ‘foreign’ from the regression function. This makes ‘foreign’ the reference category.

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For the weekends after the opening weekend, the regression model takes the box office results of the previous weekend as only input parameter. This is after all standard in the industry and simplifies the model. Furthermore the statistic results support this hypothesis. 5.4 Regression model R is a statistical program used to estimate regression models through different methods, unless stated differently, the following models are all estimated with ordinary least squares (OLS). All the R outputs from this section can be found in appendix C.

The demand model for movies in weekend 1 is:

BoxOfficeWeekend_1 = β0 + β1Star + β2Genre_Action + β3Genre_Adventure + β4Genre_Animation +

β5Genre_Biography + β6Genre_Comedy + β7Genre_Crime + β8Genre_Drama + β9Genre_Family +

β10Genre_Fantasy + β11Genre_Mystery + β12Genre_Romance + β13Genre_SciFi + β14Genre_Thriller +

β15Sequel + β16US + β17Local + β18Reviews + β19Long + β20Adaptation + β21Budget + β22Buzz +

β23Award_1+ β24Season_1Spring + β25Season_1Summer + β26Season_1Winter + β27Holiday_1 + µ

This model which can be found in appendix C.1 has been estimated through ordinary least squares (OLS) and has a high explanatory value, adjusted R² = 0,6919 and the overall significance of the model is proven by the high F-statistic 33,02 and a p-value very close to 0. When compared to the critical value of 1,53 it becomes clear that the null hypothesis can be rejected, meaning that this model fits the data quite well. By looking at the individual t-values, one can control the significance of specific explanatory variables. In this case, most of the results are in line with the expectations drawn from the plots.

▪ Star: β1 = 92,08 at the 0.01 significance level ▪ Genre

✓ Genre_Adventure: β3 = 92,08 at the 0.05 significance level

✓ Genre_Animation: β4 = -59046,1 at the 0.05 significance level

✓ Genre_Drama: β8 = -29650,9 at the 0.1 significance level

✓ Genre_SciFi: β13 = 56218,9 at the 0.1 significance level ✓ No significant linear relation between other genres and the box office result of the opening week.

▪ Sequel: β15 = 150962,9 at the 0 significance level ▪ Production country

✓ US: β16 = -45572,8 at the 0.05 significance level

✓ Local: β17 = 80283,8 at the 0.01 significance level

▪ Budget: β21 = 1502,6 at the 0 significance level

▪ Buzz: β22 = 464923,8 at the 0 significance level ▪ Seasons

✓ Season _1Spring: β24 = -44204,9 at the 0.05 significance level.

✓ Season_1Summer: β24 = -36379,1 at the 0.1 significance level. ✓ Winter has no statistically relevant impact and the computer program has chosen autumn as reference category. ▪ Reviews, long, adaptation, award and holiday do not have significant relevance in relation to the box office demand of week 1. ▪ Residual standard error µ = 116700

Based upon these results, the decision is made to remove the variables with little explanatory power form the equation, turning the model into:

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BoxOfficeWeekend_1 = β0 + β1Star + β2Genre_Adventure + β3Genre_Animation + β4Genre_Drama +

β5Genre_SciFi + β6Sequel + β7US + β8Local + β9Budget + β10Buzz + β11Season_1Spring +

β12Season_1Summer + β13Season_1Winter + µ

The new linear regression model is given in appendix C.2 and has a slightly better adjusted R² value, namely 0,693. The F-statistic is higher as well, amounting to a value of 67,85 which is far above the F statistic of 1,75. In combination with an infinitesimally small p-value, one can conclude that this model is significant. However, after testing the normality hypotheses for this regression model, this regression model should be rejected due to a specification error. This has been brought to light by the means of the Ramsey RESET test. This test checks whether the correct functional form has been used for the model specification by adding non-linear variables. These variables are either combinations of the fitted values or the explanatory variables (Gujarati D.N., Porter D., 2008). In this case an F-value of 37,075 is returned, together with a p-value close to zero. This points to the presence of a specification error, which explains the diverging p-values of the heteroskedasticity and Jarque-Bera tests as well. The latter tests for normality by comparing the skewness and kurtosis of the distribution of box office results in the first weekend to these values of the normal distribution (Gujarati D.N., Porter D., 2008).. In order to solve this issue some other functional forms have been tested out.

By including cross variables, more precise estimations can be acquired. This will nevertheless lead to a certain degree of multicollinearity in the model, but this is no reason to reject the model equation. At first a model containing all previously mentioned variables as well as all the second order interaction effects was tested out, their significance can be found in appendix C.3. This model had of course a lot of insignificant variables which were thereupon chosen to be removed, leading to the following final model:

BoxOfficeWeekend_1 = 4025.18 – 983982.39 * Sequel + 379718.94 * Buzz + 900.78 * Budget + 476.05 * Star + 493549.35 * Sequel * Genre_Comedy – 225386.76 * Sequel * Genre_Mystery + 500767.59 * Sequel * US + 125649.20 * Sequel * Reviews + 253066.92 * Sequel * Long – 479248.79 * Sequel * Season_Spring + 225015.34 * Sequel * Season_Autumn – 199190.51 * Sequel * Season_Summer – 548159.57 * Local * Genre_Crime – 118354.04 * Local * Genre_Romance – 224188.03 * Local * Genre_Thriller + 217717.21 * Local * Adaptation + 1925105.35 * Local * Buzz + 235.39 * Star * Genre_Action + 1086.25 * Star * Genre_Family + 702.01 * Star * Genre_SciFi – 904.24 * Star * Sequel – 146.07 * Star * US – 79.92 * Star * Reviews + 850.53 * Star * Buzz – 222.14 * Star * Award – 249.22 * Star * Summer + 188.87 * Star * Winter + 340.57 * Star * Holiday + µ

This regression model has an adjusted R2 value of 0,8692 and an F-statistic of 92,34 and therefore has great explanatory power. A summary of this model is provided in appendix C.4. The Ramsey RESET test returns a p-value of 0,1901 meaning that it is safe to assume this model has the correct functional form. However, the Breusch-Pagan test for heteroskedasticity has a p-value very close to zero, hence the assumption of homoskedasticity should be rejected. This test compares n*R2 with k degrees of freedom – n being the sample size 386 and k the number of independent variables 28 – to the chi square distribution (statisticshow.com, 2018). These and other statistical tests are listed in appendix C.6.

This heteroskedasticity issue can easily be resolved by using the estimated generalized least squares method to estimate the model instead of the ordinary least squares (OLS) method, which was used before. However given the fact that the actual GLS function is not known but only estimated, the decision is made to stick with the OLS estimator, which despite its diverging standard errors remains unbiased. A summary of the GSL model is available in appendix C.7.

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The demand for the following weekends gets predicted by developing a linear regression model based on the box office results of the preceding weekend. As the relative weeks move on, fewer data becomes available due to the fact that more and more movies have left the theaters. Because of this, the regression models become less and less reliable, thus possibly leading to estimation errors. In order to avoid this problem, the demand for all the weekends after weekend 5 is estimated by the average of the ordinary least squares regressions of weekend 6, 7 and 8. The adjusted R2 values of these equations lay between 0.647 and 0.8299, hence this model is a good fit. This leads to the following equations:

▪ BoxOfficeWeekend_2 = 9.279 * 103 + 6.725 * 10-1 * BoxOfficeWeekend_1 ▪ BoxOfficeWeekend_3 = 5.174 * 103 + 6.446 * 10-1 * BoxOfficeWeekend_2 ▪ BoxOfficeWeekend_4 = 2.918 * 103 + 6.476 * 10-1 * BoxOfficeWeekend_3 ▪ BoxOfficeWeekend_5 = 2.303 * 103 + 7.025 * 10-1 * BoxOfficeWeekend_4 ▪ BoxOfficeWeekend_n = 3.908 * 103 + 6.141 * 10-1 * BoxOfficeWeekend_n-1 (for all n > 5)

All these equations estimate box office results in euros, in order to eventually get to the expected amount of visitors, these results should be divided by 7,25 euro, which was the average ticket price in Belgian movie theaters anno 2014.

The model was verified by comparing the actual demand with the estimation for a random set of 53 movies released in Belgium in 2016-2017. With the help of an analysis of variance (ANOVA) test, the conclusion can be made that the estimation model is quite accurate. For validation reasons, an ANOVA test was also conducted for the estimated GLS model, here it becomes clear that it was a wise decision to opt for OLS. These test results can be found in appendix C.8. 5.5 Demand division For the movies in this dataset, 69% of the total box office gross was made during the weekends. This whopping number is in line with the expectations built up throughout interviews with industry members and once more confirms the significance of weekend results, regardless of the fact whether it is a school week or a week during school holidays. This conclusion has been made given that the difference between the average of the profit proportion made during the week for school weeks and the average during the holidays is infinitesimally small. There is however quite some variation in between weeks. For example during the first week of the Christmas holidays in the 2013-2014 season, over 83% of the total box office gross was made in the weekend. On the other side of the spectrum, throughout the spring break of 2014, less than 62% of the total profit was generated during the weekend. Furthermore, the overall success of the movie has also no impact on the division between weekend and week grosses. Consequently, this number will be used to guide the box office predictions for the whole week during the construction of the scheduling program.

Now that the weekend demand can be predicted and by consequence the demand for the whole week, it is time to consider how these weekly numbers should be split over the different days and times. As everyone working in the movie industry can confirm and in line with the 2014 data, the weekend really takes the lion’s share of the profit. Especially Saturday evening and Sunday in the late afternoon are big shots. Most theaters have four showings a day, with sometimes an exceptional fifth one in the forenoon, the so-called matinee. Given the peculiar character of this occurrence, the model does not take this possibility into consideration. Usually the afternoon movies start at 2:30 p.m., the late afternoon screenings at 5 p.m., the ones in the evening at 8 p.m. and the late night showings at 10:30 p.m. Depending on both the movie theater and the movie at hand, these hours can however differ. For bigger theaters, like for example a Kinepolis, the choice can be made to not start all movies at the exact same in order to prevent too long queues at the ticket booth and the concessions store. Next, in case of longer movies, starting for example the evening exhibition at 8 p.m., would mean that the same

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screen could not be used for the late evening screening at 10:30 p.m. Thus resulting in the decision to move these starting times further apart, hereby the logical choice is to change the starting point of the evening showing. This might however lead to having to move up more time slots during the rest of the day. When scheduling a movie at a certain time, one should always consider the duration of the movie, the commercials, the possible break and the cleaning. For now the movies will just be scheduled at the four different moments during the day, afterwards the time slots can be moved up if necessary.

Considering the fact that no day by day data of the Belgian box office is publicly available, some daily box office results of the United States were consulted with the necessary precaution. Data of three different months – September 2018, December 2016 and May 2014 – was compared in order to get an overview concerning the average division of box office gross over the various days of the week. From the Belgian data, the conclusion had been drawn that 69 percent of the overall box office gross is made during the weekend. With this knowledge and the new information retrieved from the American data, the following subdivision can be drawn up:

Wednesday Thursday Friday Saturday Sunday Monday Tuesday 2:30 p.m. Early afternoon 3,077% 1,538% 1,538% 7,692% 7,692% 1,538% 1,538% 5 p.m. Late afternoon 2,051% 1,538% 2,564% 7,692% 10,256% 1,538% 1,538% 8 p.m. Evening 3,590% 3,590% 7,692% 10,256% 2,564% 3,590% 3,590% 10:30 p.m. Late evening 0,513% 1,026% 5,128% 5,128% 0,513% 0,513% 0,513% Table 5-1: Weekly demand division

Naturally, one should keep in mind that not all movies reign in the most profit on Saturday at 8 p.m., some are known to draw a bigger audience at different times. Children’s movies as well as animated features are obviously at their most popular during the day when children can go see them. On the other hand, thrillers and especially horror movies are more appealing to people in the late evening. For these special cases, specified division tables were created and these can be found in appendix D.

Furthermore the elderly mostly prefer going to the cinema in the afternoon, and younger people generally favor the evening showings.

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6 Scheduling Movies in Cplex Cplex is a computer program from IBM, used to “build and solve complex optimization models to identify the best possible actions” (ibm.com). The main goal here is to optimize the profit, while conforming to a set of constraints.

The model in Cplex was tested with movie data coming from 131 movies that were in Belgian theaters in between May 6, 2015 and September 22, 2015 – this is a total of 21 weeks. The demand function generated through R is used as input for the scheduling. However due to deviation, this could sometimes return negative numbers in the first weekend. It is safe to assume that this is an incorrect reflection of the reality and therefore the results for these 7 movies should be adjusted. So, in case the formula returns a negative profit, the average of the opening weekend box office results of the 10% worst performing movies in the 2014 dataset is assigned. In order to get from the weekend numbers to these for the whole week, the results get divided by 0,688652 – which is the average profit proportion made during the weekend. Afterwards these profits are divided by 7,52 euros – the average ticket price in Belgian movie theaters in 2014 – to end up with the expected visitors total. Despite the fact that 2015 data is under consideration here, the 2014 average is chosen given that the demand function was based on 2014 data.

An overall finding from the interviews is that the people responsible for movie planning first determine the number of screenings they want to give a certain film in a given week. It is only in the next stage that they will look at the actual timetable. Big movies always get a ‘full program’, this is the same for all movie theaters and means that the movie is being shown at every single time of every single day of the week. Thanks to computer programming, this no longer needs to be the case. As mentioned by Mr. Deboes, this way of scheduling regularly leads to a mismatch between supply and demand. Because of this reason, the choice has been made to step away from this method. 6.1 Optimization function

The decision variable is kmwdts with m movie, w week, d day, t time and s screen. This variable is either 0 or 1, depending on whether movie m plays in week w, day d at time t on screen s or not.

The function to optimize is:

푁푏푀표푣푖푒푠 푁푏푊푒푒푘푠−1 7 4 푁푏푆푐푟푒푒푛푠 Max ∑푚=1 . ∑푤=0 . ∑푑=1. ∑푡=1. ∑푠=1 . kmwdts * expProfitmwdts ▪ Movie m the 131 movies in the dataset, ranked from 1 to 131 ▪ Week w the 21 weeks in the dataset, ranked in chronological order from 0 to 20 ▪ Day d the 7 weekdays, ranked chronologically from 1 to 7, with Wednesday being the first day ▪ Times t the four moments throughout a day when movies are shown: 2:30 p.m., 5 p.m., 8 p.m. and 10:30 p.m. – ranked in this order from 1 to 4

▪ expProfitmwdts the expected profit for movie m in week w, day d at time t on screen s. ▪ NbMovies number of movies to play during certain time period ▪ NbWeeks number of weeks in time period under consideration ▪ NbScreens number of screens in the movie theater

▪ expDmwdt expected demand of movie m in week w, day d at time t

= WeekDmw*WDivisiondt

▪ WeekDmw week demand movie m in week w, as predicted before in section 5.4 * market share of the theater

▪ WDivisiondt proportion of the total week demand at day d, time t

▪ ProfitSplitmw profit split between exhibitor and distributor for movie m in week w

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▪ Capacitys seat capacity of screen s ▪ avT average ticket price at the movie theater expProfitmwdts = 푒푥푝퐷(푚, 푤, 푑, 푡) ∗ 푎푣푇 ∗ 푃푟표푓𝑖푡푆푝푙𝑖푡(푚, 푤) 𝑖푓 푒푥푝퐷(푚, 푤, 푑, 푡) ≤ 퐶푎푝푎푐𝑖푡푦(푠) { 퐶푎푝푎푐𝑖푡푦(푠) ∗ 푎푣푇 ∗ 푃푟표푓𝑖푡푆푝푙𝑖푡(푚, 푤) 표푡ℎ푒푟푤𝑖푠푒

6.2 Constraints 6.2.1 Necessary Constraints When scheduling movies a couple of constraints need to be taken into consideration. These first two are required in order to return viable results.

A. One movie per screen, for each week, day and time As confirmed by all the exhibitors interviewed in section 4, general practice is to use all the screens all the time. 푁푏푀표푣푖푒푠 ∑푚=1 .kmwdts ≤ 1 Ɐw ϵ {0, …, NbWeeks-1}, Ɐd ϵ {1, …, 7}, Ɐt ϵ {1, …, 4}, Ɐs ϵ {1, …, NbScreens} B. Movies cannot be shown before their release date

kmwdts = 0 Ɐm ϵ {1, …, NbMovies}, Ɐw: Releasem= w ϵ {1, …, NbWeeks}, Ɐd ϵ {1, …, 7}, Ɐt ϵ {1, …, 4}, Ɐs ϵ {1, …, NbScreens}

With Releasem the release week of movie m 6.2.2 Optional Constraints Whether these constraints are included depends upon the strategical decisions made by the movie theater in question.

A. Movies have to be played in consecutive weeks

If w > 0 and Releasem < w 7 4 푁푏푆푐푟푒푒푛푠 7 4 푁푏푆푐푟푒푒푛푠 ∑푑=1. ∑푡=1. ∑푠=1 . km(w-1)dts ≥ ∑푑=1. ∑푡=1. ∑푠=1 . kmwdts Ɐm ϵ {1, …, NbMovies}, Ɐw ϵ {1, …, NbWeeks-1} For movies in week 0, no such constraint is included because it would compromise the model if it were limited to scheduling habits used by the theater prior to the introduction of this model. In the release week of a movie, there is also no limit to the number of screenings. B. Movies must be played on consecutive days ▪ For the first day of the schedule, there is no limit to the number of screenings for movies that have been released prior to the start of this scheduling period.

▪ If w = 0, d > 1 and Releasem < w 4 푁푏푆푐푟푒푒푛푠 4 푁푏푆푐푟푒푒푛푠 ∑푡=1. ∑푠=1 . km(w=0)(d-1)ts ≥ ∑푡=1. ∑푠=1 . km(w=0)dts Ɐm ϵ {1, …, NbMovies}, Ɐw ϵ {1, …, NbWeeks-1}

▪ If w > 0, d = 1 and Releasem < w 4 푁푏푆푐푟푒푒푛푠 4 푁푏푆푐푟푒푒푛푠 ∑푡=1. ∑푠=1 . km(w-1)(d=7)ts ≥ ∑푡=1. ∑푠=1 . kmw(d=1)ts Ɐm ϵ {1, …, NbMovies}, Ɐw ϵ {1, …, NbWeeks-1}

▪ If w > 0, d > 1 and Releasem < w 4 푁푏푆푐푟푒푒푛푠 4 푁푏푆푐푟푒푒푛푠 ∑푡=1. ∑푠=1 . kmw(d-1)ts ≥ ∑푡=1. ∑푠=1 . kmwdts Ɐm ϵ {1, …, NbMovies}, Ɐw ϵ {1, …, NbWeeks-1}, Ɐd ϵ {2, …, 7} C. Movies must be played in their release week

If Releasem = w 7 4 푁푏푆푐푟푒푒푛푠 ∑푑=1. ∑푡=1. ∑푠=1 . kmwdts ≥ 1 Ɐm ϵ {1, …, NbMovies}, Ɐw ϵ {1, …, NbWeeks-1}

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This constraint forces every movie in the set to be shown, which in reality is a good thing given that the selection of the movies to play has been made in advance. D. Diverse movie schedule The goal of this constraint is to provide a diverse set of movies at all times, meaning that there should be options available for different kind of visitors at every moment. These following constraints help limit the number of movies of a certain genre to be screened as well as the number of movies produced in Belgium and the United States. This choice has been made based upon the aspiration – which was voiced by all the interviewed movie exhibitors – to provide a culturally attractive movie schedule. In the following sections, this set of constraints will be referred to as ‘the diversity constraint’. 푁푏푀표푣푖푒푠 푁푏푆푐푟푒푒푛푠 ▪ ∑푚=1 . ∑푠=1 . kmwdts*USm ≤ 0.8*NbScreens Ɐw ϵ {0, …, NbWeeks-1}, Ɐd ϵ {1, …, 7}, Ɐt ϵ {1, …, 4} 푁푏푀표푣푖푒푠 푁푏푆푐푟푒푒푛푠 ▪ ∑푚=1 . ∑푠=1 . kmwdts*Localm ≤ 0.5*NbScreens Ɐw ϵ {0, …, NbWeeks-1}, Ɐd ϵ {1, …, 7}, Ɐt ϵ {1, …, 4} 푁푏푀표푣푖푒푠 푁푏푆푐푟푒푒푛푠 ▪ ∑푚=1 . ∑푠=1 . kmwdts*Genre_Romancem ≤ 0.5*NbScreens Ɐw ϵ {0, …, NbWeeks-1}, Ɐd ϵ {1, …, 7}, Ɐt ϵ {1, …, 4} 푁푏푀표푣푖푒푠 푁푏푆푐푟푒푒푛푠 ▪ ∑푚=1 . ∑푠=1 . kmwdts*Genre_Comedym ≤ 0.5*NbScreens Ɐw ϵ {0, …, NbWeeks-1}, Ɐd ϵ {1, …, 7}, Ɐt ϵ {1, …, 4} 푁푏푀표푣푖푒푠 푁푏푆푐푟푒푒푛푠 ▪ ∑푚=1 . ∑푠=1 . kmwdts*Genre_Actionm ≤ 0.5*NbScreens Ɐw ϵ {0, …, NbWeeks-1}, Ɐd ϵ {1, …, 7}, Ɐt ϵ {1, …, 4} Other constraints like these can be added, movie theaters which are part of the European Cinemas Network are for example required to play a certain relative amount of European films. E. Different movie on every screen 푁푏푆푐푟푒푒푛푠 ∑푠=1 . kmwdts ≤ 1 Ɐm ϵ {1, …, NbMovies}, Ɐw ϵ {0, …, NbWeeks-1}, Ɐd ϵ {1, …, 7}, Ɐt ϵ {1, …, 4} This constraint can easily be adapted to the requirements of a particular theater. For example multiplexes may not mind having a movie play at two screens, but not more. F. On 2:30 p.m. (t = 1) when children do not have school, at least half the movies shown at the theater should be targeted towards them. Concretely, this means that half of the movies should be an animation or a family film. ▪ During school holidays

If Holidayw = 1 푁푏푀표푣푖푒푠 푁푏푆푐푟푒푒푛푠 ∑푚=1 . ∑푠=1 .kmwdts*AnimationFamilym≥ 0.5*NbScreens Ɐw ϵ {0, …, NbWeeks-1}, Ɐd ϵ {1, …, 7}, Ɐt ϵ {1} 1 𝑖푓 푔푒푛푟푒 푚표푣𝑖푒 𝑖푠 푎푛𝑖푚푎푡𝑖표푛 푎푛푑/표푟 푓푎푚𝑖푙푦 AnimationFamilym = { 0 표푡ℎ푒푟푤𝑖푠푒 ▪ On Wednesdays, Saturdays and Sundays

If Holidayw = 0 푁푏푀표푣푖푒푠 푁푏푆푐푟푒푒푛푠 ∑푚=1 . ∑푠=1 . kmwdts*AnimationFamilym ≥ 0.5*NbScreens Ɐw ϵ {0, …, NbWeeks-1}, Ɐd ϵ {1, 4, 5}, Ɐt ϵ {1} 6.3 Computational Results The program is run with varying constraints for three different hypothetical cinemas, with 3, 5 and 10 screens respectively. These screen capacities vary between 80-200, 50-300 and 50-400 seats. The robustness of the conclusions can be verified by comparing the results of these different cinemas. Furthermore, for all these examples the average ticket price of 8,50 euros was used. Lastly the profit split as mentioned by Mr. Van Eemeren of Cartoon’s was applied on all these examples. Concretely,

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this comes down to 50 percent in the first three weeks, with a five percent increase in favor of the exhibitor in every following week until it reaches a maximum of 80 percent. The computer program is provided in appendix E.

The total amount of screens in Belgium anno 2015 was 472, amounting to a total of 102410 seats. With this information, the market share of the theater can be estimated. To retrieve this, the total number of seats in the theater needs to be divided by the total amount of cinema seats in Belgium. 6.3.1 Objective function The impact of the addition of optional constraints will be compared to the standard model with only the two necessary constraints. The relative impact on the objective function, or the overall profit is shown in table 6-1 below.

50% Kids Must movies Diverse All play in No extra at program different release A + C A+B+C+D constraints certain (A) (B) week moments (D) (C) No continuity constraints 100,00% 93,20% 97,80% 99,96% 98,78% 93,03% 92,22% Consecutive weeks 94,77% 89,26% 93,75% 94,67% 93,62% 89,04% 88,36% 3 Consecutive screens days 93,03% 85,67% 91,70% 92,65%

No continuity constraints 100,00% 94,61% 93,73% 99,96% 99,66% 94,54% 91,25% Consecutive weeks 96,45% 91,20% 91,93% 96,37% 96,04% 91,14% 89,11% 5 Consecutive screens days 94,54% 88,29% 90,18% 94,40%

No continuity constraints 100,00% 97,24% 89,21% 100,00% 99,83% 97,20% 88,49% Consecutive weeks 96,73% 94,16% 88,15% 96,70% 96,54% 94,09% 87,26% 10 Consecutive screens days 95,39% 91,89% 86,56% 95,26% Table 6-1: Relative impact of the constraints on the overall profit

All the constraints have a similar impact on the profit of all the theaters under consideration. The diverse movie schedule however has a bigger relative impact the smaller the amount of screens. This makes perfect sense, given that they are now only allowed to schedule two American movies at the same time in the example of the three screen cinema. Due to the fact that Hollywood movies are generally the most profitable, this naturally results in a lower overall profit.

On the other hand, the constraint which forces the cinema to play a different movie on every screen at any given time, has the biggest relative effect on the ten screen example. This is again logical, because it often occurs that there are less than ten interesting movies at the box office. Moreover,

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where smaller cinemas in reality almost never schedule the same movie twice or more at the same time, this does regularly occur in multiplexes, as was confirmed by Ms. Van de Casseye of Euroscoop. For this reason, the choice has been made to also check the profit with a relaxed version of this constraint, specifically where every movie can only be shown on maximum two different screens at the same time. By relaxing this constraint, its impact becomes relatively smaller in comparison with the other two cases, as can be seen in table 6-2.

No continuity Consecutive Consecutive constraints weeks days

All different(B) 89,21% 88,15% 86,56% Max two the same (B2) 95,63% 93,76% 92,25% 10 screens A+B+C+D 88,49% 87,26% A+B2+C+D 94,24% 92,19% Table 6-2: Comparison relative impact constraint maximum one (B) or two (B2) screens playing the same movie at any given moment

The smallest overall impact is caused by the constraint concerning the requirement of playing children’s movies at 2:30 p.m. on school-free afternoons. Part of the reason why the impact is so small is because of the fact that kids’ movies generate the biggest share of their money during these afternoons. Consequently it is also from an economic perspective more interesting to schedule family and animation movies at these times.

The obligation to play movies in their release week has a rather small effect as well. If the demand of a movie is really low the model will only schedule it for one or maybe two screenings, thus not causing a big overall impact. However when theaters want these movies to have a minimum amount of screenings in their first week, such a constraint can be added as well. Here, the decision has been made to go with at least 7 screenings in the opening week. This has of course more dramatic effects, especially for the small three screen cinema, where profit decreases with around 10 percent more than with the previous constraint. These test results are given in table 6-3. One has to keep in mind that all the movies which were in Belgian theaters at this point in time are included in the dataset. In reality however, movie exhibitors have made a deliberate choice of which new releases they want to show. Consequently, if this program were to be used by an actual cinema, this constraint has to be included – in order not to violate contracts with distributors – but the dataset will be smaller. Therefore the negative effect of this enhanced constraint will probably be less articulate.

3 Screens 5 Screens 10 Screens Must play at Must play at Must play at Must play in Must play in Must play in least 7 times least 7 times least 7 times release week release week release in release in release in release (D) (D) week (D) week (D2) week (D2) week (D2)

No continuity constraints 98,78% 89,10% 99,66% 96,95% 99,83% 98,68% Consecutive weeks 93,62% 82,69% 96,04% 92,19% 96,54% 95,17% Table 6-3: Comparison of the relative impact of the constraint of movies playing at least one (D) or seven (D2) times during their release week

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When movies are forced to be played in consecutive weeks, the overall profit drops with a little more than 3 percent in case of the five and the ten screen cinema in comparison with the model without any optional constraints. For the three screen theater this leads to a decrease of more than 5 percent. These numbers remain largely in the same range when more constraints are added. The difference between the effect of the consecutive weeks and consecutive days constraints is rather small, between the 1 and 2 percent for all theaters. Since successful movies get played on most days anyway, this should not be super surprising. This constraint leads however to the unfortunate event of children’s movies being played on regular weekdays when they will probably not attract large crowds. It also has a larger impact when combined with the diversity constraint, because the combination of these two really minimizes the scheduling possibilities.

Next, when combining all these constraints, the overall profit drops to just below 90 percent of the original profit received from the objective function without any optional constraints. Interesting to notice is that despite the fact that the overall relative decrease is similar for the three situations, the main cause differs. In the case with three movie screens, there is only less than one percent that cannot be ascribed to the diversity or the continuity constraint. With the ten screen theater, a comparable conclusion can be drawn, but here the majority of the reduction is caused by the all different constraint, resulting in the biggest relative drop of all the examples under consideration. When its relaxed version is used however, the difference with the original solution is down to less than 8 percent.

Thanks to the fact that the results of all the three examples point toward the same conclusions – with some logical deviations due to the different sizes – this model looks quite robust. Another robustness check is conducted, where some of the previous results of the five screen cinema get compared to these of a five screen cinema with different screen capacities – varying between 300 and 400 seats. Naturally, the relative differences are not the exact same as can be seen in table 6-4, but the results are similar and therefore one can conclude that this model is robust. The reason why for this second example the reduction caused by the consecutive weeks constraint are larger, is because its screens have a larger seat capacity. Therefore having to plan less profitable movies at certain times in order to obey this rule will have a bigger impact on the overall result.

Old Example New Example No continuity Consecutive No continuity Consecutive constraints weeks constraints weeks

No extra constraints 100,00% 96,45% 100,00% 94,70% 50% Kids movies at certain moments (C) 99,96% 96,37% 99,77% 94,53% Table 6-4: Robustness check - comparison impact constraints on two five screen movie theaters

6.3.2 Movie schedule Upon looking at the actual movie schedule, some interesting discoveries can be made. Here the focus is on the first five screen example, its screen capacities are, ranked from screen 1 to 5: 300, 300, 250, 150 and 50. A comparison will be made with the three screen example, its respective capacities are: 200, 150 and 80. The weekly schedules discussed below can all be found in the appendix F, the list which links the numbers mentioned in these schedules to actual movie titles is given in appendix G.

In the case where no additional constraints were included, it is striking that for some weeks only a very small number of movies gets screen time at this hypothetical theater. For example in week 3, only three movies are shown: Avengers: Age of Ultron, Tomorrowland and San Andreas. All three of them

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are big budget movies. Thereby, at most times during this week, only Avengers: Age of Ultron is scheduled to play. The same thing can be noticed at the three screen theater. However not all weeks’ results are this radical, in week 17 for example, a total of 16 movies is played at the five screen theater, and 11 at the three screen theater.

When adding the restriction that movies should play in consecutive weeks, this schedule changes completely and becomes already a lot more acceptable. Nevertheless at multiple moments during this period, still only two different movies are scheduled. The total numbers of movies in the cinema in week 3 increases to ten, with Avengers responsible for 36 percent of the screenings. Moreover, in the previous case this movie took up all the screenings on Saturday, here on the other hand the movie is only assigned late night screenings on Saturday. The explanation is that since this movie is apparently the biggest moneymaker, it will draw a crowd at any possible moment. Smaller movies which were originally not scheduled to play, might really depend upon these Saturday screenings in order to get the numbers up. It is also worth noting that in the model without added constraints, movie 55, Jurassic World was not being shown during week 17 whereas it is now scheduled for a total of 26 screenings. Another observation is that some movies like Pitch perfect 2 and Inside Out in week 17 get a small boost in their total screening numbers in comparison with the previous model. This has happened as a consequence to the drastic changes that occurred to other movie and proves how interdependent the whole schedule is. At this point it is important to keep in mind that the overall profit of the whole 21 week period is being optimized, therefore when analyzing these changes one should keep an eye on the overall picture.

The schedule for the model which includes all four major constraints from the previous section as well as the consecutive weeks restriction also leads to a few insights. When again looking at week 3 and week 17, one can see a few differences. In week 3, only a total of 19 movies is scheduled in the five screen theater example, interestingly enough this is the same amount of movies as in the three screen theater. In week 17 on the other hand, 31 different movies will be shown at the five screen cinema. These vast differences can partly be explained by the fact that week 3 is situated at the beginning of the planning horizon and week 17 at the end. Therefore more movie data is available for movies which could possibly be shown in week 17, plus all the new releases of this week have to be screened as well. Moreover, since consecutive weeks is included as a constraint, the movie schedule of week 17 depends upon all the schedules of previous weeks in the model. Due to this constraint, movies which are amongst the more profitable ones in for example week 15 as well as week 18, but not however in week 17, might now work their ways into the schedule of week 17.

Upon analyzing this schedule, one might realize that the amount of movies which is assigned to only one single screening over a whole week is a little high. This is partly a consequence of the requirement to play movies in their release week. Furthermore one notices that the screenings a movie gets are usually divided over the different time slots, meaning that even if a movie is shown only four times during a week, not all these screenings will occur at for example 8 p.m. This is in line with what Mr. Deboes of Sphinx has been doing and in contrast with common scheduling practice.

When looking at the total number of weeks a movie spends in the theater, there are some noticeable differences between the different schedules, as is shown in table 6-5. For example movie 3, romatic comedy The Longest Ride, does not appear in the original schedule for the ten screen cinema, nor when the consecutive weeks constraint is added to the model. However after the addition of all the previously mentioned constraints (with exclusion of the need to play movies on consecutive days), the movie is scheduled for twelve weeks. In general, movies spend more time at the box office when all constraints are included. The exception being the big movies which were taking up all the screens in the restriction-free model, like movie 1, superhero blockbuster Avengers: Age of Ultron. When the only

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added constraint is that movies should be played in consecutive weeks, some smaller movies disappear from the schedule.

Movie 1 3 22 44 45 No extra constraints 14 6 4 10 3 Consecutive weeks 14 0 9 19 0 Consecutive weeks + all 3 screens constraints 12 9 6 19 12 No extra constraints 12 5 5 9 2 Consecutive weeks 13 0 8 19 0 Consecutive weeks + all 5 screens constraints 9 11 6 19 12 No extra constraints 16 0 3 14 1 Consecutive weeks 14 0 9 19 0 10 Consecutive weeks + all screens constraints 11 12 9 19 12 Table 6-5: Total number of weeks movies spend at the box office. Underlined and in italics means that the period does not consist out of consecutive weeks.

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7 Conclusions and future research 7.2 Conclusions This thesis has focused on solving the movie scheduling problem for exhibitors in Flanders. More specifically, first a function to predict movie demand has been generated, next an actual scheduling model was created.

Despite the fact that similar research has been conducted in the past, never before has the focus been on Flanders. Due to governmental decisions and cultural differences every region is unique when it comes to movie theater exhibition. Therefore a specified research like this was is helpful to come to some useful insights.

In order to forecast the movie demand, the predicting value of a lot of parameters has been tested. These potential parameters had been selected based upon previous papers on the topic () as well as interviews conducted with movie exhibitors in Flanders. The resulting demand function was then used as input for the scheduling program.

The main goal was to maximize the profit generated by a movie theater while obeying to a couple of constraints. The impact of six constraints has been tested through the generation of movie schedules for four hypothetical cinemas with a varying amount of screens and seat capacities. In figure 7-1 the results for the tests executed with a fictional five screen cinema are listed.

5 screens - overall profit in euros 3900000 3800000 3700000 3600000 3500000 3400000 3300000 3200000 3100000

No continuity constraints Consecutive weeks

Figure 7-1: Comparison profit in euros for some different sets of constraints for the five screen example

The impact of the introduction of different constraints on the overall profit of this five screen cinema is in line with the numbers resulting from tests conducted on the other exemplary cinemas. Playing movies in consecutive weeks leads to a significant drop in the overall profit in comparison with the

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model where this was no requirement. When movies have to be shown on consecutive days, the difference with the profit made when movies only have to be screened in consecutive weeks is not that big. When movies are forced to be shown in their release week, the effect on the overall profit is rather small. Given the fact that economically uninteresting movies are with this addition maybe only shown once or twice, this does not result in a significant decrease in profit. Furthermore the majority of the interesting movies is also in the model without added constraints scheduled to play during their opening week because then the demand is the highest.

When talking about a diverse movie schedule, the intent is that at any given moment only a certain percentage of the scheduled films can belong to the same genre or be produced in the same country. This set of constraints has a bigger relative effect on the overall profit, when the number of screens in the cinema is smaller. The opposite is the case when the movie theater is forced to play a different movie at every screen at any given time. Here the impact is bigger for theaters with more cinema rooms. The decision to enforce at least half of the films shown at 2:30 p.m. on Wednesdays, Saturdays, Sundays and during the holidays to be oriented towards children has no significant impact on the overall profit. 7.3 Future research In future research it can be interesting to test out some other criteria. For example delving deeper into the restrictions related to a movie in its release week. Next, it could be a good idea to include constraints which force the movie with the overall highest demand to be played at the most lucrative moments, being Saturday night and Sunday late afternoon. Another interesting additional constraint might be a maximum amount of movies to play during a week in order to obtain a more practical movie schedule.

Naturally the most valuable research would be in cooperation with an actual movie theater. That way, the researcher would have access to more accurate numbers and the constraints could be tested in reality. Given the fact that this program is able to schedule movies for a number of weeks at the same time, when working with a theater, one can check the accurateness and thereupon alter its calculations for the next weeks.

Furthermore this paper has solely focused on the profit side of the decision process, however it could be possible that it is sometimes more profitable not to us all the screens.

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9 Appendices A. Purchasing Power Country Purchasing power 1 Luxembourg $ 109 100 2 Ireland $ 72 600 3 Norway $ 70 600 4 Switzerland $ 61 400 5 The Netherlands $ 53 600 6 Sweden $ 51 300 7 Germany $ 50 200 8 Denmark $ 49 600 9 Austria $ 49 200 10 Belgium $ 46 300 11 Finland $ 44 000 12 France $ 43 600 13 United Kingdom $ 43 600 14 Malta $ 42 500 15 Spain $ 38 200 16 Italy $ 38 000 17 Israel $ 36 200 18 Czech Republic $ 35 200 19 Slovenia $ 34 100 20 Slovakia $ 32 900 21 Lithuania $ 31 900 22 Estonia $ 31 500 23 Portugal $ 30 300 24 Poland $ 29 300 25 Hungary $ 28 900 26 Greece $ 27 800 27 Latvia $ 27 300 28 Turkey $ 26 500 29 Croatia $ 24 100 30 Romania $ 24 000 31 Bulgaria $ 21 600 33 Montenegro $ 17 400 34 Macedonia $ 15 200 35 Serbia $ 15 200 Purchasing power European countries anno 2017 (with exception of Israel) – only the countries included in movie research in section sioelfjsej. Source: https://www.indexmundi.com/map/?l=nl&r=eu&v=67

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B. Interviews movie exhibitors B.1 Interview Jan-Willem Van Eemeren, Cinema Cartoon’s Antwerpen Goedemiddag Jan-Willem Van Eemeren, jij bent hier verantwoordelijk voor het inplannen van de films?

Ja, klopt. Ik werk sinds 2015 bij Cartoon’s, mijn voornaamste taak is de exploitatie, dus echt de uitbating op zich. Cartoon’s bestaat 40 jaar en is in die tijd drie keer failliet gegaan. De laatste keer is dat in 2013 gebeurd, in 2014 heeft de Lumière-groep de cinema dan heropgestart. Wij maken nu eigenlijk deel uit van een groep. We hebben ook een zustercinema in Brugge, de Lumière, waar dat ook de Lumière-groep ontstaan is. En omdat wij dus twee cinema’s hebben in dezelfde groep, wel allebei met hun eigen, specifieke programmatie, maak ik de rooster op, maar hebben we wel één hoofdprogrammeur die voor Lumière werkt, dus voor de twee cinema’s.

Dus hij beslist welke films, en jij op welke tijden?

Klopt, en hij geeft ook aan in welke zalen een bepaalde film zou werken en zo, het is ook handig om één mens centraal te hebben voor die twee cinema’s in verband met contacten met distributeurs. Omdat je dan kan zeggen van “deze film gaat ook in Lumière spelen”, zo hebben we wat meer macht in de onderhandelingen, meer marge, een hefboom.

Bepalen jullie aan het begin van het seizoen alle films die gaan spelen?

Wij spelen heel erg in op het aanbod, dus welke films er aangekocht worden voor de Benelux, dat is natuurlijk de hoofdvoorwaarde: is er een filmverdeler en is daar een releasedatum voor geprikt.

Die data worden nationaal bepaald?

Ja, klopt. Zo weten wij bijvoorbeeld van “ah in november komt de nieuwe Gouden Palm uit”.

Hoe bepalen jullie of een film goed genoeg is om te vertonen?

Ik ben dit jaar naar Cannes geweest om daar naar een aantal films te gaan kijken. Dat was heel fijn, maar ook heel intensief. Ik heb 22 films gezien op 9 dagen. Dus dat is heel veel informatie om dan nog objectief te blijven. Ieder jaar gaan we naar drie filmfestivals, dus er gaat nog één iemand naar Toronto en dan nog iemand naar Venetië. Dat zijn de belangrijkste markten en dat is ook echt de moment om te kijken van “oké dat is op welke film we gaan bieden.” Onze inkomsten hangen volledig af van ticketverkoop, dus van hoe goed films draaien. We hebben dat deels zelf in de hand door goede zaaltjes en de zaal proper te houden, maar natuurlijk is dat ook afhankelijk van de sterkte van films. Dat is hetzelfde als met een wijnjaar, het ene jaar hebben we heel veel goede films, het andere jaar zit er niets tussen. Dat hebben we zelf niet in de hand, we kunnen wel proberen daar zo goed mogelijk op in te spelen. Zoals nu met Girl, dat is echt een fenomeen, dat heeft alle records hier in de Cartoon’s sinds de heropening echt verpulverd. Als cinema kan je dat niet zelf zo in de hand werken. Dat is iets groter en dan kunnen wij wel meesurfen.

De film waarvoor je denkt dat de vraag het grootst gaat zijn, speel je in de grootste zaal, veronderstel ik?

Klopt en die spelen wij ook het meest in frequentie op een dag. Want in Cartoon’s natuurlijk, we hebben drie zalen en dat is niet zo evident ten opzichte van het aantal filmtitels dat we willen tonen. UGC heeft er bijvoorbeeld 17, Kinepolis 24, bij ons is het maximum dat we kunnen vier keer per dag drie films.

Dus jullie kunnen echt maar zes films per dag spelen?

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Correct, en dan moeten we ook gaan kiezen hoeveel keer per dag die en ook op welk moment speelt die. Natuurlijk een film om 20h heeft meer impact dan een vertoning om 14h30. Maar we kunnen maar drie films tegelijkertijd spelen, dus dat is altijd inschatten en afwegen en dat is vooral buikgevoel.

Dus jullie gebruiken daar geen formule of zo voor?

Nee we gebruiken geen algoritmes, dus dat is wel moeilijker soms. Maar tegelijkertijd is de persoon die de programmatie voor de twee cinema’s opstelt iemand die dat al jaren doet. Die heeft er een beter zicht op van wat gaat scoren. Ook uw eigen voorkeur moet je soms aan de kant zetten want het is niet omdat ik een film heel goed vind, dat die ook gaat aanslaan bij het publiek. En omgekeerd zeker ook en het is dan niet aan ons als cinema om die dan niet te vertonen. Programmeren is iets artistiek inhoudelijk, maar wel ook heel pragmatisch.

Houden jullie rekening met de films die in andere zalen in Antwerpen spelen?

Bij bepaalde filmtitels wel, er zijn films die maar een beperkt potentieel hebben en als we dan weten dat die in een andere zaal speelt, dan hoeft die niet per se ook bij ons vertoond te worden. Langs de andere kant zijn er ook filmtitels die overal werken. Call me by Your Name bijvoorbeeld, die heeft overal in Antwerpen gespeeld, dat is een film die je gewoon moet spelen. Er is uiteindelijk ook een publiek dat zo een film graag in de Cartoon’s wilt zien en dan staat dat los van wat de andere zalen doen.

De cinema in Brugge is natuurlijk een ander verhaal, daar is één cinema in de binnenstad (Lumière) en dan is er nog een Kinepolis in de rand, dat is het cinemalandschap van Brugge. In Antwerpen zit je met Kinepolis, UGC, Cinema Zuid, Filmhuis Klappei, de studio, dus dan moet je je echt positioneren en zeggen van “oké dat is nu echt een Cartoon’s film, hier staan wij voor, dat gaan we draaien”.

Denken jullie ook wanneer jullie de films uitkiezen van “we willen een zo divers mogelijk aanbod”, of is het puur winst georiënteerd?

Het speelt zeker mee, je hoopt natuurlijk dat zoveel mogelijk mensen naar de zalen komen. Maar je wilt ook natuurlijk dat uw programmatie de echte filmliefhebbers weet te lokken. Je hebt een cinefiel die iedere week naar de cinema gaat, maar er zijn ook mensen die maar één keer per jaar gaan. Wij hebben altijd Europese films, maar je wilt ook Vlaamse films tonen, en in Amerika worden er ook wel eens goede films gemaakt, dus dan zit je daarmee te schuiven. Dus het hangt echt af van het moment, de releasekalender, waar dat alle distributeurs hun films plaatsen, we kunnen er niet rond.

Is er een reden waarom Call me by your name en Lady Bird en andere Oscarfilms maar zoveel maanden later in België uitkomen dan in de Verenigde Staten?

De cinefiel heeft inderdaad al voor de Oscars van die films gehoord, die weet dat die gaan uitkomen, die weet bijvoorbeeld “dat is de nieuwe film van Luca Guadagnino, gebaseerd op een boek, en Timothée Chalamet is een jonge opkomende acteur van wie we nog veel gaan horen”. Maar het grote publiek reageert pas van zodra er een Oscarbeeldje, een prijs aan vasthangt. Zeker in die periode, veel distributeurs houden die films dan bewust wat tegen tot na de Oscar uitreiking. Call me by your name was net de week voor de Oscars in Belgische zalen, Lady Bird pas in april. Dat is nu iets wat wel meer en meer onder druk komt te staan, en zeker in het geval van Lady Bird, dan is die film al vier maanden oud, de Blu-ray, dvd release is al geweest en die zijn heel makkelijk te kopiëren en dan staat zo een film dus al op het internet vooraleer hij in de Belgische zalen te zien is.

Je hoort ook al maandenlang op het internet over zo een film gesproken worden en dan moet je zo lang wachten vooraleer het zover is.

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Klopt. Dat is soms niet evident, maar nu in de Verenigde Staten zijn in oktober alweer heel wat prestigieuze films uitgekomen, maar in Vlaanderen is er dan weer Girl, met gigantische lokale impact. Dus daar ga je dan geen andere goede film tegenover zetten, want dat is een beetje zelfmoord voor uw cijfers. Als je weet, iedereen probeert een beetje te sturen, in de markt is er ook maar weinig ruimte.

Passen jullie een verschillende strategie toe voor het programmeren van week en weekend?

Het weekend is sowieso het belangrijkste moment, dan hebben de mensen opeens tijd. Dus de grote kanonnen staan altijd op zaterdagavond. Er is ook een ander publiek, in de week heb je meer de cinefielen, die een cinemabezoek echt standaard inplannen in hun week en echt weken op voorhand kijken van “ah dan komt die film uit”. In het weekend is er een algemener publiek.

Hoe verlopen de onderhandelingen met distributeurs?

Distributeurs bellen soms om te vragen van “zeg wanneer gaan jullie die film spelen?” dat is dan een discussie die we hebben, dat zijn natuurlijk langdurige partners. Soms komt er dan een minder goede film uit, maar die spelen we dan ook omdat je weet van volgende keer hebben ze misschien een film die wel de Gouden Palm wint of zo, dus daar moet je ook wel rekening mee houden. Je kijkt altijd naar wat mogelijk is en gaat altijd op zoek naar een compromis.

Hoe worden de opbrengsten verdeeld tussen jullie en de distributeur? En betalen jullie om een film te spelen?

Op basis van ticketverkoop. Het meest gangbare scenario is dat in de eerste drie weken dat een film bij ons speelt, 50% van de opbrengsten naar ons en 50% naar hen gaat. Dan 45, 40, 35… natuurlijk bij bepaalde films voelen distributeurs al dat de ticketverkoop goed gaat zijn, en dan zeggen ze van “nee, het zou vier weken moeten zijn”, of “we weten ook niet wat het gaat geven, dus hij mag starten aan pak 40%”. Dus zo na onderhandelen is daar enigszins wat marge in, maar het is sowieso zo dat als een film goed doet, we daar sowieso allebei van profiteren.

Ik had ergens in een paper gelezen – maar ik vond het nogal ongeloofwaardig – dat distributeurs een minimumbedrag aan cinema-uitbaters betalen als er niet genoeg volk komt opdagen om winstgevend te zijn.

Nee dat is niet gangbaar in België. Het is eerder omgekeerd. Het is zelfs zo bij bepaalde éénmalige vertoningen, schoolvoorstellingen of speciale restauraties, dat er gewoon een vaste prijs aan de distributeur betaald moet worden, bijvoorbeeld €700 en dan moet je maar zien dat je de zaal vol krijgt.

Gebruiken jullie altijd alle drie de zalen?

Aangezien we maar drie zalen hebben en veel meer films, gebruiken we altijd al onze zalen. Ik kan me wel inbeelden dat multiplexen die met veel meer zalen zitten, soms de keuze maken ze niet allemaal te gebruiken. Dat is natuurlijk ook energie en zo die je verbruikt, een periode waarin het minder is, ik denk aan het begin van de zomer, of een avond dat de Rode Duivels spelen, dan heeft het denk ik niet zo veel zin om 24 zalen open te stellen. De enige uitzonderingen bij ons zijn zaalverhuren en dan schrappen we één voorstelling, dat is dan ook niet op een zaterdagavond of zo, eerder op een maandagmiddag.

Houden jullie bij het maken van de planning rekening met dingen zoals het weer of de Rode Duivels?

Ja, mooi weer is natuurlijk moeilijk. Het is te zeggen, je stelt uw verwachtingen bij, het is niet dat we andere dingen gaan spelen. Je programmeert gewoon films en hoopt dat er toch wat volk op afkomt.

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Tijdens de zomer hebben we bijvoorbeeld Stalker van Tarkovsky gespeeld, een restauratie van een klassieker. Dat is dan wel het moment, want in de zomer komen er minder recente films uit, of toch minder films op niveau. En dan weet je van “oké er is sowieso een publiek dat gaat komen om deze film eens in de cinema te zien.” Dus zo proberen we toch nog interesse te wekken in cinema als het aanbod wat tegen zit. Ook in het verleden is er altijd wel een zomerprogramma geweest, of in Lumière was dat dan, een actie met terugkeer-tickets. Dus zo proberen we toch ofwel via prijs ofwel via aanbod de verminderde interesse wat tegen te gaan. Het is iets seizoensgebonden.

Spelen jullie films altijd gedurende een onafgebroken periode?

Het is moeilijk om dat niet te doen, je kan geen films schrappen uit uw programmatie om dan terug te zetten. Omdat dat een verwarrend verhaal is naar uw consument toe en ook naar de distributeurs. In communicatie is dat ook moeilijk. De bedoeling is eigenlijk dat een film in het begin een heel aantal voorstellingen krijgt per week en dat we dan beginnen af te bouwen. Naar het einde toe nog maar twee keer, nog maar één keer op zondagavond… Afhankelijk van bezoekcijfers en ook de drukte van films die uitkomen. Als we elke week een film zoals Girl zouden hebben, dan hebben we te weinig plaats.

Kijken jullie ook naar bezoekcijfers van in Amerika, of in Italië of zo waar een film eerder is uitgekomen om jullie te laten leiden?

Dat geeft ergens een aanduiding dat er potentieel is bij een film. Natuurlijk heb je van die Franse komedies die in Frankrijk waanzinnig veel volk lokken, maar in België, vooral dan in Vlaanderen, gewoon helemaal plat op hun buik gaan. Bij de Noorderburen van hetzelfde, Nederlandse films pakken heel moeilijk in België. Dus ja dat is niet altijd even representatief, maar het geeft wel aan wat de kwaliteit van een film is, maar er zijn zeker grote culturele verschillen. Het is wel zo, alle cinema’s in België delen hun cijfers met elkaar via MatchBox, dat is een database van bezoekcijfers, release data en filmtitels. Dus dan kunnen we wel zien van “Ah bij ons was een film zeer goed en in de UGC nog zoveel keer beter”, dus dan weet je wel dat het echt een film met enorm veel potentieel is. Of “oei, bij ons was het niks, maar het heeft eigenlijk nergens echt gepakt”, dus dat is dan ook geruststellend.

Komt het soms voor dat een film bij jullie niet scoort en bijvoorbeeld in Gent wel?

Ja klopt. Bijvoorbeeld Girl werkt heel goed in Antwerpen, maar in Gent werkt het nog zoveel keren beter, want dat is een Gentse regisseur en Gentse productie. Of Cargo, een film van Gilles Coulier, dat is een film die in Lumière toch ook wel een pak meer bezoekers heeft weten te lokken dan bij ons, omdat dat een West-Vlaamse film is. Dus dat speelt soms wel mee. Of in Antwerpen hebben we meer een cultureel publiek, van echt hoger opgeleiden, waar een bepaalde film beter kan werken dan elders, ik denk aan Anomalisa een poppenfilm voor volwassen, dus echt iets vrij unieks. Dat was echt een hit in Cartoon’s en minder in Brugge.

Hebben jullie ook speciale Halloweenfilms of zo?

Ja we proberen dat wel steeds meer. Vroeger moest je als cinema gewoon je deuren open zetten en er zou wel volk afkomen, dat was zo een beetje uw enige rol. Maar nu met de alsmaar stijgende concurrentie, kunnen mensen thuis met hun home cinema een gelijkaardige ervaring creëren. Dus je moet echt inspelen op het evenement-aspect, je moet een andere ervaring kunnen aanbieden dan die je op alle andere locaties kan verkrijgen. Dus dan programmeer je iets met Halloween, of vorig jaar kwam Isle of Dogs van Wes Anderson uit, toen hebben we daarvoor drie restauraties van zijn eerdere films gespeeld, in samenwerking met de Skoop in Gent. Zo proberen we toch iets anders aan te bieden dan alle anderen. Onze concurrentie beperkt zich ook niet alleen tot film, bijvoorbeeld de laatste game

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van Rockstar Redemption is nu net uitgekomen, daarvan zijn 17 miljoen stuks verkocht in de eerste twee weken, dus dat zijn heel wat mensen die dit weekend thuis zitten en aan het gamen zijn. Dat is gewoon vrije tijd die gespendeerd wordt aan andere dingen, en bijgevolg zitten er minder mensen in de cinema. Dus we proberen toch zoveel mogelijk iets te creëren wat niet te reproduceren valt, en dat is dan vooral met éénmalige voorstellingen, exclusieve films.

Een reeds bestaand programma neemt als voorwaarde ‘geen twee films mogen op hetzelfde uur beginnen’, hoe staat u daar tegenover?

Aangezien wij maar drie zalen hebben, is het nooit extreem druk aan de kassa. Maar ik kan me inbeelden dat bij Kinepolis of zo met 10.000 zitplaatsen, het aan te raden kan zijn om niet alles gelijktijdig te laten beginnen. Aan de andere kant, zijn er ook wel echt mensen die naar onze cinema komen om bijvoorbeeld 20h met de gedachte “er zal wel iets spelen”. Om nog eens terug te komen op Girl, die laten we nu wel altijd een kwartier eerder starten omdat we weten dat daar best wel wat volk op afkomt.

Is er nog iets anders wat u graag had verteld?

Het enige wat ik eigenlijk nog niet heb toegelicht – en dat is wel iets vrij uniek – is dat wij werken met een maandprogrammatie in plaats van de gangbare wekelijkse planning die je bij de meeste andere cinema’s ziet. Want als kleine cinema is het ook moeilijk om elke week opnieuw die planning op te stellen, je moet dan weer met al die distributeurs bellen en onderhandelen. Zeker met dat we maar drie zalen hebben, krijg je dan al snel te horen van “Ah we hebben maar zoveel vertoningen meer…” en dat is een soort van carrousel, dus om dat een beetje te vermijden. Nu kunnen we dan zeggen van “Ja kijk hij speelt misschien niet zoveel in de eerste week, maar in de derde week speelt hij wel nog altijd om 20h.” Dat is een geruststellend gevoel, we hebben dan ook veel minder commotie. Dat maakt het voor ons ook beter om dat rustig allemaal op te stellen.

En daar wordt nooit iets aan veranderd? Als een film echt maar weinig bezoekers trekt of zo?

Nee, omdat dat ook in het programmaboekje wordt gedrukt. Het is dan wel onmogelijk om als er echt een hit is, die film dan extra voorstelling te geven. Maar langs de andere kant zijn de kleinere films wel altijd groter dan in de wekelijkse programmatie. Naar het publiek toe is dat ook wel leuk omdat zij dan weten van “Oh ik kan volgende week ook nog komen kijken.” Wij hebben ook een publiek dat zoals bij een theater echt kijkt van “die voorstelling die dan speelt, dat is in eind november, we gaan dat nu al in plannen”, of “ik ga die film dan dinsdag over drie weken zien.” Soms kunnen we op die manier films ook een zeker carrière geven, de speelduur, om het publiek te trekken dat ze waard zijn. Dat is wel iets dat eigen is aan de Lumière en Cartoon’s. Er zijn in Wallonië wel cinema’s die dat voor zes weken doen, maar dat lijkt me al helemaal moeilijk om zo ver op voorhand te voorspellen. Want inderdaad soms valt een film tegen en dan zit die daar nog wel in uw programmatie voor de rest van de maand. Dat is soms moeilijk. Het is wel gemakkelijker om bij sommige titels in te schatten, bijvoorbeeld als die al tien filmprijzen heeft gewonnen, het filmfestival van Gent heeft geopend, er is veel om te doen… dan weet je dat daar een publiek op gaat afkomen.

Dank u wel!

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B.2 Interview Patrick Deboes, Sphinx Gent Goeiemiddag, hoe stellen jullie de programmatie in de Sphinx op?

Wij hebben nu een programma gecreëerd. Ongeveer vijf jaar geleden heb ik mij daar op toegelegd. Ik ben sowieso al 20-30 jaar hier bij de Sphinx aan de slag en al lange tijd ook verantwoordelijk voor de filmprogrammatie. Wij hebben het hier ook specifiek over cinema, want filmprogrammatie is ook heel belangrijk voor televisie, dvd-verkopen… maar cinema is toch wel een erg specifieke context. Dat is mijn hoofdbezigheid van de laatste decennia en waar ik zelf ook heel veel over heb nagedacht en met veel mensen voor heb samengezeten, om dus te kijken of dat niet op een meer winstgevende manier mogelijk is.

Hoe zijn jullie tot deze huidige manier van programmeren gekomen?

Om dat te begrijpen, moet je eigenlijk de geschiedenis van cinema kennen. Cinema dateert van 1895, de eerste commerciële uitbatingen waren er rond de jaren ‘10-‘20, het absolute topmoment was de jaren ‘30, dus voor de opkomst van de televisie. Want met de televisie ontstaat er een concurrentiële vorm. Decennium per decennium zie je de cinema evolueren. Begin jaren 70 zijn de multiplexen ontstaan. Daarmee zijn de jaren ‘60-‘70 het einde van het oude centrumcinema model. Initieel werd een film op één locatie op alle momenten altijd vertoont. Dus je had het systeem van de centrumzalen. Waarbij een film op de toplocaties in het centrum van de stad gedurende bijvoorbeeld één week, twee weken vertoond werd. Op ieder moment kon je dan naar die ene film gaan kijken, want cinema’s hadden toen ook maar één zaal. Dan in week twee, drie gaat de film een stapje verder naar de buitenrand van de stad. Tegen de vijfde week zit die film dan echt in de periferie, en zo verschuift een film gedurende zijn loopbaan steeds verder van het stadscentrum, en hoe verder, hoe goedkoper ook de tickets. Dit is het model van de centrumzalen tegenover de zalen in de periferie. Hierbij zijn de zalen in het centrum ook de mooiste, prestige zalen, bijvoorbeeld in Gent het Capitool, waar films altijd in première gaan. Dan tegen het einde was de film ook te zien in pakweg Gentbrugge en Zwijnaarde. In feite had je op dat moment in iedere straat quasi een cinema. In het centrum van Gent had je de Luhmerstheim groep, in Antwerpen groep Heylen, en zo had je in iedere grootstad wel een groep die eigenlijk heerste over de cinema in het centrum, een cinemabaron als het ware. Die Lumertsheim groep had zoveel kapitaal, zoveel macht dat die in de eerste week als iedereen die film wou vertonen, ook de film kon kopen en zo week na week met de première kon gaan lopen. Dat is een hele andere tijd natuurlijk. Geleidelijk aan zie je dus dat er een verschuiving gebeurt, met het ontstaan van multiplex bijvoorbeeld. Waarbij de kleine zalen verbouwd en uitgebreid werden. Typisch voor Heylen was dat ze van het balkon een tweede zaal maakten en in bijvoorbeeld de kelder een derde zaal. Dan zijn ze ook begonnen met daadwerkelijk een cinema te bouwen met zicht op vijf zalen. Multiplexen zijn ontstaan als reactie op het dalen van de rendabiliteit. Cinema is in de loop der tijd telkens geëvolueerd op zoek naar meer rendabiliteit, in concurrentie met andere vrijetijdsbestedingen. Vroeger gingen mensen naar de cinema, niet alleen om een film te zien, maar bijvoorbeeld ook voor nieuwsbeelden. In de oorlog gingen mensen naar de cinema om te zien waar de Duitsers zaten. Dus gaandeweg krijgen we meer en meer zalen, andere vormen van cinema’s. Veel concurrenten ook die zalen gaan verkleinen. Om economische redenen vooral, met dat er meer en meer concurrentie op vlak van het audiovisuele is gekomen, dus dat is zeer belangrijk.

Het tweede punt, is dat je toch in het vertonerslandschap twee strekkingen hebt. Commerciële vertoners en meer de culturele vertoners, de groep waartoe wij behoren. Allebei verschillend uitbatingsvormen. En dat zie je in verschillende landen.

Wat maakt de situatie in Vlaanderen zo uniek in vergelijking met andere landen en gewesten?

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Hier in Vlaanderen zitten we met een heel uniek cinemalandschap, dat niet te vergelijken is met Wallonië, zelfs niet met Brussel, zeker niet met Nederland, Engeland… Ieder land heeft een heel ander cinemalandschap, als gevolg van het beleid dat er op vlak van cinema gevoerd wordt. In ieder land is dat beleid ook anders. In Vlaanderen zijn er erg weinig arthouse cinema’s omdat er zo weinig steun is vanuit de overheid. Die zijn er wel in Wallonië, wel in Nederland, wel in Engeland… omdat er daar wel meer subsidies worden uitgegeven aan arthouse cinema’s. In Vlaanderen zijn ze er nog altijd niet achter dat kwalitatieve cinema’s, die het ook hebben over cinema als kunstvorm, op zijn minst een beetje overheidssteun nodig hebben om rendabel te kunnen zijn. Als het gaat over de verkoop van tickets, schiet je als arthouse cinema sowieso 20-30 procent te kort. In Nederland zijn ze dat ook echt gaan benoemen als economisch deficiënt zijn.

Waaraan ligt dat dan?

Omdat cinema’s hoge kosten hebben: apparatuur, personeel, zaal… Die kosten zijn zo hoog dat je die niet kan dekken met een programma dat eigenlijk een beetje cultureel ambitieus is. Wat niet wilt zeggen dat daar geen belangstelling voor is, die is er wel degelijk, wij kunnen ook volle zalen halen. Maar als je kijkt naar de uitbating, dan kost dat veel. Daar moet je rekening mee houden, ieder land is echt een ander verhaal.

Nog één dingetje aan toevoegen, ik ben afgestudeerd in de moraalwetenschappen en uiteraard was ik geïnteresseerd in film en dan via mijn familie interesse in zaken voeren. Initieel ben ik dan als werknemer in de cinema begonnen, later als zaakvoerder. Van in den beginne was ik gefascineerd door de dynamiek tussen enerzijds het cultureel-sociale pakket en anderzijds het economische pakket, het bedrijfsmatige. Uiteindelijk ben je toch met een bedrijf bezig, wij hebben in de loop der jaren, vrij snel, na vijf jaar of zo, ook een vzw op gericht. Dus alles wat je hier ziet wordt eigenlijk samengesteld door een vennootschap en een vzw. Die vzw heeft in de loop der jaren wel wat subsidies verkregen, de vennootschap ook, dan van andere bronnen, van Europa, dat zijn subsidies die niet toekomen aan vzw’s. Dus wij worden ondersteund via het Europa Cinemas Netwerk. Deze cinema is dus het resultaat van het bedrijf en de vzw die samenwerken. Cinema is nog altijd een cultureel product en een sociaal gegeven, een film die gaat over de tongen, dan willen meer mensen die komen zien… En dan heb je nog natuurlijk het economische aspect. Dus heel dat proces fascineert mee.

Cinema kan dus niet gezien worden als iets dat puur winstgericht is?

Ja, je moet vaak omgekeerd of averechts denken. Je kunt als je het hebt over het cultureel en sociale gebeuren, dat niet economisch benaderen. In het filmaanbod heb je verschillende types van films. Je hebt grote en kleine films, pak een blockbuster uit Amerika en een kleine Europese of Aziatische film. Als je die kleine film – die voor 10 procent van je opbrengst zorgt – in je grote filmzaal gaat spelen, dan gaat die in week één misschien 500 man trekken, terwijl als je die in die kleine zaal speelt, die misschien 2000 mensen gaat lokken. Niet alle films werken in alle zalen. Het is dus niet zo dat iedere film meer opbrengt bij een grote dan bij een kleine aanbieder. Dat is omdat je met een speciale sociale dynamiek zit, vergelijkbaar met producten in een grootwarenhuis en bij de buurtwinkel. Bijvoorbeeld gewoon bier verkoopt veel beter in het grootwarenhuis, maar het kan zijn dat een speciaal bier dan juist veel meer verkoopt in die kleine winkel. Dus zo moet je soms averechts denken om tot een goed resultaat te komen. Je moet ook in de tijd denken, sommige producten gaan in de eerste week bijna geen volk lokken, maar werken dan wel als je ze langer laat lopen. Het is zo, de toppers in mijn aanbod die kan ik er vrijwel altijd onmiddellijk uithalen, die zijn gemakkelijk te voorspellen. Maar ik heb ook die kleinere titels nodig voor het totaalaanbod, dat is immers wat de zaak aantrekkelijk maakt. Die kleine titels, die kleine procenten, laat die weg en mijn top film gaat ook minder presteren. De economische logica is dus niet altijd zomaar rechtlijnig door te trekken. Dus dat is soms een moeilijke redenering.

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Als we dan de efficiëntie van de zaak bekijken, dan is die kleine film ook belangrijk voor de globale aantrekkelijkheid van het bedrijf, wat voor mij erg belangrijk is.

Een bijkomend punt, nu heb ik het meer over wat wij doen, minder over wat multiplexen doen. Er zijn verschillende soorten vertoners, daar bestaan ook beleidsteksten over. In België hebben we vier categorieën. In de eerste zitten de multiplexen: Kinepolis, UGC, die ook zelfs internationaal zitten. De tweede categorie zijn dan de kleine, landelijke commerciële filmvertoners; ik denk dan aan een cinema in Roeselare, Dendermonde, De Panne… die ofwel één ofwel soms twee of drie locaties hebben, maar toch commerciële vertoners zijn. Zij zijn landelijk actief, gefocust op de lokale gemeenschap en hebben de laatste jaren ook wel begrepen dat ze ook iets meer kwaliteitsfilms kunnen vertonen, dus toch ergens verwant met wat wij doen. Dan de derde categorie, de grote culturele vertoners, De Zuid in Antwerpen, Z in Leuven, die nog een striktere culturele opdracht hebben wij. Daaronder in de vierde categorie heb je culturele cinema, in Vlaanderen weinig actief de laatste decennia, die men nu toch terug probeert te activeren vanuit het beleid.

Hoe gaan jullie om met die financiële uitdagingen, hoe proberen jullie toch rendabel te zijn?

De opbrengsten – de totale omzet van een cinema – wordt maar voor een klein gedeelte gerealiseerd door de ticketverkoop. Wij hebben bijvoorbeeld een café, dat rendabel is, dat zorgt voor 20-30% van de omzet en dat draagt bij aan het budget van de cinema.

Hoe verlopen de onderhandelingen met de distributeurs?

De verhouding tussen de exploitanten en de distributeurs is weer iets speciaals. Distributeurs bieden premières van films aan. Daarnaast hebben we ook de evenementen – ik wijk nu weer af – daarin hebben wij drie takken: filmfestivals (gedurende bepaalde periode), film specials (éénmalig) en filmreeks (loopt met bepaalde regelmaat). Elke cinema heeft verschillende distributeurs en zal zijn aanbod samenstellen uit die selectie. In de oude tijd gingen mensen niet naar de film, maar naar de cinema, de kassa was doorlopend open en mensen konden op ieder moment van de dag binnenstappen en een film beginnen kijken. Nu kon het zijn dat je midden in een film binnenkwam, dan bleven mensen gewoon zitten tot de programmatie rond was; er speelden altijd drie films, onafgebroken na elkaar. Dat was nog zo in de jaren 60-70.

Nu heb je een rooster, zeven dagen per week, vijf zalen, drie vertoningen per dag. Dat moet je dus overzichtelijk houden. We komen van het model – wat Studio Skoop nog steeds gebruikt – waarbij een première zeven dagen op zeven een middagvoorstelling op woensdag en in het weekend en een iedere dag avondvoorstelling kreeg. Dan bekijk je op maandag de resultaten van het weekend en op basis daarvan heronderhandel je met distributeurs en geef je de film bijvoorbeeld één voorstelling minder. In week drie nog wat minder, week vijf nog maar 4 voorstellingen…

Waarom zijn jullie van dit oude systeem afgestapt?

Ik had een paar vaststellingen gemaakt. Ten eerste zag ik dat de laatavondvoorstellingen minder en minder goed gingen. Een tweede vaststelling was dat wij vrij veel evenementen hebben en die draaien wel goed, plus die versterken de gewone vertoningen ook. Wij hebben bijvoorbeeld soms een seniorenvoorstelling in de namiddag, daar wordt dan achteraf over gesproken en zo lokken we meer mensen naar onze zaal voor die film de komende dagen. Alles heeft ergens wel een invloed op het geheel, het omgekeerde ook. Een derde ding, we zien dat zalen zoals de Lumière in Brugge voor een maand hun planning opstellen, maar die werken met een heel variabel pakket, waarbij iedere dag anders is. Ze zetten de kijker aan om sneller naar de cinema te komen, door aan te kondigen dat een film bijvoorbeeld maar voor drie weken speelt. Dus ik zie de problemen en ik zoek naar oplossingen.

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En die oplossing is dan dat computerprogramma?

Correct, ik doe eigenlijk iets wat volgens mij vrij uniek is. Want ik ben afgestapt van wat ik een horizontale programmering noem, wat wij eerst hadden, waarbij een film eerst op iedere dag om 20h te zien is. Dus als een film dan niet zo goed scoorde, verloor die zijn voorstellingen om 20h, maar werd die bijvoorbeeld wel nog om 22h30 gespeeld, dat dan iedere dag – dus horizontaal. Het model zoals in een grootwarenhuis met hoge en lage schappen en de lucratieve in het midden. Ik vind dat eigenlijk stom, je moet films een kans geven. Ik stelde vast dat we een deel films hadden die het heel goed deden, en dan een deel die het slecht deden en de middenmoot die valt er door deze manier van programmeren zowat uit. Dus dan zie je dat je niet goed bezig bent. Als je alleen afhankelijk bent van die toppers. Plus wat stel ik ook vast, dat telkens in het begin films te veel vertoningen hebben, en dan als ze een tijdje al spelen te weinig of te slechte vertoningen. Dan ben ik een jaar aan het negotiëren gegaan met distributeurs om hen te overtuigen van dat ik eens iets anders wilde proberen. Wat bij de meeste ook wel gelukt is, hoewel het af en toe nog opkomt. Dat wilt dus zeggen als ik een film drie vertoningen geef, dat dat er dan een om 14h30 is, een om 20h en een om 22h30, en niet drie om 22h30. Zo geef je mensen dus de kans om een film op verschillende momenten te zien; dan wel niet elke dag, maar het laat toch meer opties open. Iedere dag is ook een ander verhaal. Je hebt eigenlijk drie periodes gedurende de week: woensdag en donderdag zijn de aanloop, vrijdag, zaterdag, zondag is het weekend en maandag, dinsdag is de uitloop. Donderdag is echt een top dag bij studenten omdat die op vrijdag naar huis gaan. Zondag heb je ’s avonds dan weer weinig volk, maar daar scoren de namiddagvoorstellingen wel heel goed. Maandag is de minste dag, maar bij ons is dat de sociale maandag, dus dan zijn de tickets ook goedkoper, de mensen zijn daar ook wel van op de hoogte. Dus dat zijn ook allemaal dingen die je in rekening moet nemen. Dat maakt het natuurlijk niet gemakkelijk.

Hoe heeft u dit dan veranderd?

Onze letterlijke functie is het goed exploiteren van films, ervoor zorgen dat zoveel mogelijk mensen een bepaalde film kunnen zien. Dus ik zeg: “Dan moet je verticaal gaan denken”. Op een bepaald moment hebben we dan gezegd van “We gaan alles anders doen,” en ben ik gewoon op gevoel wat films beginnen plaatsen in de rooster. Dat was zeer vermoeiend en heeft zelfs geleid tot verschillende burn-outs. Maar het was een noodzakelijke leerschool. Geleidelijk aan heeft dat geleid tot wat ik ons bedrijfsgeheim noem. Om dus per zaal te gaan denken, wat wel belangrijk is bij een kleine cinema. De zaal op de tweede verdieping is niet zo makkelijk bereikbaar voor oudere mensen, sommige zalen liggen dicht bij elkaar en als daar dan te veel geluid van komt, kan dat storend zijn. Dus zeer specifieke kenmerken, dat zat met de planning eigenlijk allemaal verkeerd. Wat daar uitgekomen is, is een programmatie waarbij ik modules maak per zaal. Ik stel me dat voor als zo een houten puzzel van blokjes. Ik had dat al geprobeerd, was me niet gelukt en toen heb ik de sleutel gevonden: modellen bedenken per zaal. Hoe kan ik per zaal verschillende puzzels maken. Ik heb verschillende categorieën van films gecreëerd (A, B, C, M, E, F, X), van elke categorie nog subcategorieën, dat allemaal op basis van ervaring. A’s spelen bijvoorbeeld altijd, maar dan heb je ook de A die niet op dinsdag speelt bijvoorbeeld. B is wanneer ik twee films in één zaal ga combineren, C’s drie. Maar de modules zien er totaal anders. Dus ik ben vertoningen verticaal gaan verdelen. Ik doe vandaag nog altijd hetzelfde, programmeren met variabele roosters. Dus geen vaste, dat wilt zeggen dat niet iedere dag er hetzelfde uitziet. En ik vervang die roosters iedere week op basis van mijn weekendresultaten. Ik vind dat om twee redenen belangrijk: door te kijken naar mijn weekendresultaten weet ik wat goed is en kan ik bijpassen, dat is essentieel. Ten tweede doordat ik een weekprogramma maak, kan ik heel kort op de bal spelen met evenementen. Als iemand mij maandag vraagt om een speciale vertoning donderdag, dan kan ik dat inplannen. Dat heeft twee jaar geduurd vooraleer dat op punt stond. De roosters die ik gebruik hebben een min of meer vast profiel en dat geeft een soort van rust. Wat niet altijd terug te

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vinden valt bij Lumière bijvoorbeeld. Ik vind dat wel nodig. Eén van de voordelen is dat het helemaal niet stoort als er veel evenementen zijn, dat valt niet op dat we daar dingen voor moeten verschuiven.

Hoe ziet uw computerprogramma er concreet uit?

Vroeger werkte ik gewoon met één Excel bestand en dan was ik daar soms de hele nacht mee bezig. Toen heb ik een programmeur, een wiskundige aangesproken om te programmeren in Excel. Nu zijn dat 15 Excels die met elkaar communiceren. Ik heb dan nog altijd die discussie met de distributeurs en als zij het ergens niet mee eens zijn, kan ik dat vlot aanpassen. Week na week moet je op maandagochtend met de distributeurs overleggen. Omdat we werken op basis van huurcontracten moet dat soms bijgepast worden. Nu is ons programma super verfijnd, zodat ik bijvoorbeeld naar klanten die slecht ter been zijn, een programma kan sturen met dan daarbij in welke zaal welke film speelt. Het gaat nog verder, klanten kunnen vragen om een bepaalde film op een bepaald moment te spelen. Daarachter zit nu ook nog onze weekplanning, die nu loopt tot september volgend jaar, met daarin alle evenementen en premières gepland. Dus elke week gebruik ik dat als basis voor de programmatie. Alles wat wij aanbieden wordt dan verwerkt in de rooster. Wij hebben ongelofelijke concurrentie en we kunnen nu moeilijk zeggen of we het beter of slechter doen dan de Skoop, want we verdelen eigenlijk de films over de twee zalen en soms zit daar dan een top titel bij die wij niet hebben. Maar toch de laatste twee, drie jaar zijn onze omzetcijfers wel mooi. Blijkbaar hebben de mensen daar dus geen probleem mee, met onze innovatieve manier van programmeren. Initieel kregen we daar soms wel commentaar op, onze website was toen ook nog niet zo overzichtelijk. Nu is die helemaal aangepast, ook zodat mensen die op de smartphone gemakkelijk kunnen bekijken. Dat geeft een vrij helder overzicht van wat er vandaag op het programma staat. Maar je kan ook naar een bepaalde film gaan en kijken op welke momenten hij nog speelt.

U spreekt over een app, vindt u het dan belangrijk om als cinema mee te gaan met de nieuwe technologieën?

Natuurlijk, door het ontstaan van die nieuwe mogelijkheden hebben we te maken met een toegenomen complexiteit van de wereld. Dat is enorm geëvolueerd, we worden almaar afhankelijker van die complexiteit. We hebben er dan voor gekozen om die complexiteit te aanvaarden, de grote uitdaging was hoe dat zo helder mogelijk te doen. Je hebt nieuwe mogelijkheden die de digitale cinema en de digitale kassa aanbieden, en dan moet je die ook gebruiken vind ik. De weergave van onze huidige planning kan duidelijk weergeven worden op onze nieuwe website, maar in een krant bijvoorbeeld gaat dat er heel complex uitzien. Nu, mensen raadplegen veel minder de krant als ze naar de cinema gaan. Voor de digitale cinema moest je inderdaad altijd dezelfde film in dezelfde zaal spelen omdat die op van die grote, zware (20-30 kg), opgerolde kopieën zaten en dat was dan zeer moeilijk om een film, wat toch erg fragiel is, te verplaatsen, om daar op het plateau te leggen en af te spelen. Een fysiek zeer zware operatie dus. Maar nu met de digitale cinema, kun je gewoon in één keer een film in alle zalen waar je wilt dat die deze week speelt, instellen – omdat we een centrale hebben waarmee we alle zalen bedienen, wat niet iedere cinema heeft. De digitale cinema heeft heel wat nieuwe mogelijkheden gecreëerd, die we ook moeten gebruiken. Studio Skoop doet dat veel minder. Uiteindelijk heeft de manier van werken te maken met de fysieke drager, met het stelsel van de distributeurs, met de tickets. Vroeger had je per film en per zaal een rol tickets, die dan gecontroleerd konden worden door overheden voor taksen en ook door distributeurs. Dat bepaalde de stijfheid van de programmatie. In mijn systeem werden we knettergek met dat oude systeem van tickets. Daarom hebben we een computerkassa geïnstalleerd, die gewoon één soort tickets maakt en de computer kan dat dan onmiddellijk opslaan en bij het dag- en weektotaal tellen.

Dank u wel!

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B.3 Interview Walter Vander Cruysse, Studio Skoop Gent Hoe bepalen jullie welke films wanneer in de cinema spelen? Hebben jullie daar een bepaald programma voor?

Het lijkt mij erg moeilijk om dat door een computerprogramma te laten bepalen. We bekijken de films die gaan uitkomen en op basis daarvan stellen we dan onze programmatie op. Nu, het is niet altijd mogelijk om een film op voorhand te bekijken en dan moeten we dus een inschatting maken van de kwaliteit. Meestal doen we dat dan op basis van recensies, die gepubliceerd worden op professionele sites. Of via buitenlandse tijdschriften en kranten waar de film wel al in de zalen is of vroeger aan de pers is voorgesteld, toch echt de professionelere kanalen waar je al recensies kunt vinden ook al is die film nog niet in de zalen.

Welke factoren, kenmerken van de film spelen mee bij het opstellen van de programmatie?

Enkel de kwaliteit van de film zelf, hoewel dat als je een film niet op voorhand gezien hebt, nogal moeilijk in te schatten is. Dan kijken we bijvoorbeeld naar een wat de regisseur al eerder uitgebracht heeft. Een mooi voorbeeld daarvan is Widows, die film hebben we niet op voorhand kunnen zien, maar gezien de vorige films die McQueen gemaakt heeft kunnen we wel verwachten dat het een kwaliteitsfilm is, en de recensies die vandaag (woensdag 28 november 2018) zijn uitgekomen in de kranten wijzen daar ook naar. Mensen zijn laaiend enthousiast over die film. Voor het kiezen van de uren over het algemeen, krijgt een film die wij goed inschatten bij ons een volledige programmatie, dat wilt zeggen een namiddagvoorstelling en twee avondvoorstellingen. Als we denken dat een film niet echt het publiek van de laatavond gaat kunnen bereiken, omdat die bijvoorbeeld meer op senioren gericht is, dan krijgt die enkel een namiddagvoorstelling en één avondvoorstelling, dus om 20h. Bij jeugdfilms is het nogal logisch dat die de eerste vertoning in de namiddag krijgt. Sporadisch gebeurt het wel eens dat als die jeugdfilm een gedubde en een originele versie heeft, we die originele versie dan om 17h programmeren. Films vallen weg uit ons aanbod op basis van de cijfers en de nieuwe films die uitkomen. Voor die nieuwe films moeten we plaatsmaken en de slachtoffers zijn dan de films die op dat moment het slechtst scoren.

Kijken jullie ook naar wat andere cinema’s hier in Gent spelen?

Kinepolis is voor ons geen concurrent, omdat die toch grotendeels een ander publiek aanspreekt. Onze rechtstreekse concurrent is eigenlijk de Sphinx, en als zij een film spelen, gaan we dat niet doen en omgekeerd. Omdat ik toch vind dat een arthouse cinema een eigen smoel moet hebben. Wij hebben identiek hetzelfde publiek als de Sphinx. Mensen die hier komen of naar de Sphinx gaan, omdat ze zich bijvoorbeeld niet kunnen vinden in de manier van uitbaten van de Kinepolis, die zeggen altijd gewoon van “we gaan niet naar de Kinepolis, maar naar de Sphinx of Skoop”, en dat maakt hen dan ook niet uit. Gent is een kleine stad – in vergelijking met de rest van België nu niet – en er is in mijn ogen een te beperkt cinema-gaand publiek om een film in beide arthouse zalen te spelen. Op enkele uitzonderingen na.

Trekt het weekend een ander publiek aan?

Nee, het is gewoon drukker. Net zoals de vakanties, uitgezonderd de zomervakantie – en dan nog. Als er echt een film is die de mensen willen zien dan maakt het niet uit dat het zomer is, dan kan een film nog zeer goed zijn. Dat hebben de Belgische distributeurs nog niet genoeg door.

Gebruiken jullie altijd alle zalen?

Ja. Wat wel soms is, is dat een reguliere film vervalt omdat er dan een evenement plaatsvindt in die zaal. Die evenementen zijn ook wel altijd film-gerelateerd.

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Starten alle films op exact hetzelfde uur?

Dat hangt af van de speelduur. Momenteel starten al onze films om 19h45 en niet om 20h omdat we zitten met allemaal films tussen de 2h10 en 2h50 en anders kunnen we geen laatavondvoorstelling meer houden. Dus niet om het rustiger te houden aan de kassa.

Tonen jullie films altijd van zodra ze uit zijn? Of wachten jullie soms?

Ja, we wachten niet. Ons programmatiemodel is dat we echt mee moeten zijn met Belgische nationale releases. Dan komen ook de recensies uit en dat is echt heel belangrijk. We zien dat ook onmiddellijk, als een film uitkomt en de reviews zijn niet goed, dan moeten we die er snel weer uitgooien.

Bepalen jullie aan het begin van het seizoen alle films?

Nee, dat is onmogelijk. Behalve de grote commerciële films zijn er maar een paar data gepland, en dan nog. In 50% van de gevallen wijzigt dat toch. Een James Bond bijvoorbeeld, daar houden ze zich wel aan. Maar over het algemeen, het komt vaak voor dat distributeurs dan beslissen van “ah die film gaat toch twee maanden vroeger uitkomen”, of “vijf maanden later.” Dat verschuift allemaal nog enorm, dus ver op voorhand plannen is moeilijk. Voor kleinere films is het zo, distributeurs bestellen een bepaalde film, maar ze zijn natuurlijk afhankelijk van levering van het materiaal en dat kan soms uitlopen. Vaak is het dan zo dat we zelfs al ons tweemaandelijkse krantje gedrukt hebben en dat er dan toch nog een film is die daaruit wegvalt omdat we pas te horen krijgen nadat het in druk is, dat er toch weer een datum verplaatst is. Het is nu zelfs zo dat we nog op zoek zijn naar een film voor januari. Soms kunnen we wel op voorhand plannen, maar dat is eerder uitzonderlijk.

Is winst de enige drijfveer?

We zijn een arthouse, we moeten een heel brede kijk hebben, we hebben bijvoorbeeld altijd veel Europese films, Amerikaanse onafhankelijke films. Of nu Widows van Steven McQueen is daar een mooi voorbeeld van, die heeft een film voor 20th Century Fox gemaakt, dus toch echt een grote studio en die spelen we dan ook. Maar spelen we soms films waarvan we denken “Dat gaat geen volk trekken?” Ja, omdat we soms vinden dat een film toch gezien moet worden, toch moet spelen. Een film kan spelen tussen één filmweek en 2,5 jaar, dat is bij ons de film die ooit het langst gespeeld heeft. Dus het publiek beslist.

Het is uiteraard de bedoeling dat een cinema winst maakt. Zeker in België en zeker in Vlaanderen. In Vlaanderen is er geen enkele cinema die gesubsidieerd is. In Wallonië hebben de arthouses die er zijn wel subsidies en in vergelijking met ons veel subsidies, maar in vergelijking met het buitenland is dat niet het geval. Dat is ook de reden waarom er in Vlaanderen enorm weinig arthouse cinema’s zijn en in Nederland zijn er tussen de 50 en de 70, hier 10. Dat is de schuld van de Vlaamse politiek die niet geïnteresseerd is cultuur. Behalve de prestigieuze dingen zoals de opera en de grote toneelhuizen, maar al de rest heeft het niet gemakkelijk.

De onderhandelingen met de distributeurs, hoe verlopen doe?

Dat is ongeveer bij elke distributeur hetzelfde, je vraagt de film en of je krijgt hem, of je krijgt hem niet. De winstverdeling is ook meestal gelijkmatig, tussen de 50 en 45% wordt gefactureerd aan de distributeur. Afhankelijk van of het een grote of een kleine film is. Dat is echt op basis van de opkomst, elke filmweek – die loopt van woensdag tot dinsdag – afhankelijk van verkoop. Op het einde van de week wordt dan gekeken naar het laatste gekochte ticket van een filmreeks. Want een ticketreeks is een hele week verbonden aan dezelfde film, je mag die dan ook niet voor meerdere films gebruiken. Natuurlijk nu met de nieuwe digitale kassa – wij hebben nog een analoge kassa met het ouderwetse

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ticketsysteem – is dat anders, maar daar zijn ook weer systemen dat alle verkochte tickets voor een bepaalde film samentelt.

De rol van een programmeur is vooral in de gaten houden van wat gaat er op de markt komen, de desbetreffende distributeur benaderen en proberen te overtuigen van met jou in zee te gaan en niet met uw concurrent. Veel onderhandelingen lopen heel vlot, quasi automatisch. Er zijn natuurlijk discussies want het blijft een commerciële omgeving, maar dat is ook logisch.

Dank u wel!

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B.4 Interview An Van de Casseye, Euroscoop Genk Goeiemorgen, hoe verloopt het opstellen van de filmprogrammatie bij jullie, wordt dat centraal voor alle cinema’s geregeld?

Ja ik stel de programmatie op voor alle cinema’s. Iedere stad is erg verschillend. Hier in Genk spelen we ook Mooov, het alternatieve circuit, in Maasmechelen of Lanaken moeten we dat bijvoorbeeld niet doen. En dan hebben we de Siniscoop in Sint-Niklaas, dat is een hele moeilijke want daar vinden ze alles goed. Dus dan wil ik alles tonen, maar dat gaat natuurlijk niet.

Spelen jullie films altijd aan één stuk door?

Ja.

Hoe verlopen de onderhandelingen met de distributeurs?

Ik hoop – dat weet ik natuurlijk niet – dat wij toch betere voorwaarden kunnen krijgen dan kleine cinema’s; omdat we toch veel zalen hebben, wij zijn namelijk de derde grootste in België.

Gebruiken jullie een computerprogramma?

Ja maar je moet wel zelf de films er nog in zetten. Ons softwareprogramma is al heel uitgebreid. Ideaal zou zijn een computerprogramma dat ziet van “ah deze film was vorige week om 20h het beste, dan moet die in een zaal van 400 spelen, want 200 is te klein.” Maar dat is de theorie, bijvoorbeeld ik steek films voor oude mensen het liefst in zalen waar ze niet ver naar moeten wandelen. In Maasmechelen zijn er trappen, dus dan plaats ik de films met een ouder publiek niet in de zalen waarvoor ze de trappen af moeten gaan.

Ook moet er voor ieder publiek iets zijn. Een betere film, een kinderfilm… Het kan zijn dat een film niet veel bezoekers heeft maar dat we toch kiezen om die nog te spelen.

Gebruiken jullie altijd alle zalen? Ongeacht de kosten die daaraan verbonden zijn?

Ja, dat is een beetje kosten-baten; maar we willen wel een zo goed mogelijk aanbod geven. Maar je hebt natuurlijk wel kosten die daaraan verbonden zijn: de projector, de verwarming, personeel…

Spelen jullie soms een film in meerdere zalen tegelijkertijd?

Ja.

Is er invloed van wat andere cinema’s spelen? Hoe bepalen jullie welke films spelen?

Nee. Soms kijken we op voorhand zelf de film om te zien of die goed is. Maar bij veel films is dat niet nodig, de nieuwe Fantastic Beasts speelt nu bijvoorbeeld, die moet je niet gezien hebben om te weten dat daar volk op gaat afkomen.

Beslissen jullie soms om films die jullie van plan waren te spelen toch niet te vertonen omdat de andere films het zo goed doen?

Dat ligt delicaat, want als het een afspraak is met een distributeur moet je dat natuurlijk nakomen. Dat gebeurt heel uitzonderlijk.

Spelen jullie soms films waarvan jullie minder overtuigd zijn als deel van een deal met een bepaalde distributeur? In ruil om ook zijn grote film te mogen spelen bijvoorbeeld?

Dat hebben we nooit, in kleinere bioscopen is dat misschien meer. Wij spelen sowieso de meeste films. Distributeurs komen met een voorstel en leggen hun argumenten voor om hun films in onze zalen te

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krijgen en dan moet ik soms zeggen van “sorry maar ik heb geen plaats.” De winstverdeling is op basis van percentages. Niet iedere film is exact hetzelfde, daar zit wel wat marge op. De eerste week is de film meestal duurder dan na x-aantal weken.

Onze programmatie voor de namiddag is anders dan ’s avonds. Op woensdagnamiddag en in het weekend spelen we bijvoorbeeld veel kinderfilms en grote animatiefilms, maar ook Niet Schieten bijvoorbeeld omdat daar ook veel volk op afkomt. Op weekdagen spelen we meestal maar één kinderfilm, voor als er dan toch kinderen om de een of andere reden thuis zouden zijn. Dat is dan meestal de laatste nieuwe. Voor de rest speel ik dan de films die we ook ’s avonds vertonen. Laatavond is ook weer een ander publiek, horrorfilms werken dan bijvoorbeeld beter.

Hartelijk bedankt!

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C. Output R C.1 Original Model Note: WBO_2014 stands for weekend box office results 2014.

Call: lm(formula = WBO_2014$weekend_1 ~ WBO_2014$Star + WBO_2014$Genre_Action + WBO_2014$Genre_Adventure + WBO_2014$Genre_Animation + WBO_2014$Genre_Bi ography + WBO_2014$Genre_Comedy + WBO_2014$Genre_Crime + WBO_2014$Genre_Drama + WBO_2014$Genre_Family + WBO_2014$Genre_Fantasy + WBO_2014$Genre_Myster + WBO_2014$Genre_Romance + WBO_2014$Genre_SciFi + WBO_2014$Genre_Thriller + WBO_2014$Sequel + WBO_2014$US + WBO_2014$Local + WBO_2014$Reviews + WBO_2014$Long + WBO_2014$Adaptation + WBO_2014$Budget + WBO_2014$Buzz + WBO_2014$award_1 + WBO_2014$season_1 + WBO_2014$Holiday_1)

Residuals: Min 1Q Median 3Q Max -691714 -38500 -4830 31486 872259

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1168.36 59836.45 0.020 0.98443 WBO_2014$Star 92.08 45.14 2.040 0.04209 * WBO_2014$Genre_Action 15753.32 22673.37 0.695 0.48764 WBO_2014$Genre_Adventure 43184.73 23391.70 1.846 0.06569 . WBO_2014$Genre_Animation -35967.41 26532.41 -1.356 0.17608 WBO_2014$Genre_Biography 18420.37 24846.40 0.741 0.45896 WBO_2014$Genre_Comedy 5079.03 16336.11 0.311 0.75605 WBO_2014$Genre_Crime -24504.90 20459.00 -1.198 0.23180 WBO_2014$Genre_Drama -27515.48 16683.56 -1.649 0.09997 . WBO_2014$Genre_Family -50138.11 28953.23 -1.732 0.08419 . WBO_2014$Genre_Fantasy 7198.55 28341.11 0.254 0.79964 WBO_2014$Genre_Mystery -26443.76 28256.90 -0.936 0.34999 WBO_2014$Genre_Romance -1516.56 17526.04 -0.087 0.93109 WBO_2014$Genre_SciFi 74894.91 30547.18 2.452 0.01469 * WBO_2014$Genre_Thriller 24124.93 20098.42 1.200 0.23080 WBO_2014$Sequel 175915.64 24867.40 7.074 7.96e-12 *** WBO_2014$US -18825.99 17125.26 -1.099 0.27237 WBO_2014$Local 75228.31 26150.45 2.877 0.00426 ** WBO_2014$Reviews 707.50 8853.82 0.080 0.93635 WBO_2014$Long -1713.24 19563.04 -0.088 0.93026 WBO_2014$Adaptation 9289.82 15609.91 0.595 0.55214 WBO_2014$Budget 1218.75 245.39 4.967 1.06e-06 *** WBO_2014$Buzz 558413.94 45458.55 12.284 < 2e-16 *** WBO_2014$award_1 -20330.58 24173.31 -0.841 0.40089 WBO_2014$season_1Spring -42083.11 18033.00 -2.334 0.02017 * WBO_2014$season_1Summer -15143.38 20035.75 -0.756 0.45026 WBO_2014$season_1Winter 3418.62 16474.79 0.208 0.83573 WBO_2014$Holiday_1 30658.50 16562.32 1.851 0.06498 . --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 120700 on 358 degrees of freedom Multiple R-squared: 0.7135, Adjusted R-squared: 0.6919 F-statistic: 33.02 on 27 and 358 DF, p-value: < 2.2e-16

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C.2 Second Model Call: lm(formula = WBO_2014$weekend_1 ~ WBO_2014$Star + WBO_2014$Genre_Adventure + WBO_2014$Genre_Animation + WBO_2014$Genre_Drama + WBO_2014$Genre_SciFi + WBO_2014$Sequel + WBO_2014$US + WBO_2014$Local + WBO_2014$Budget + WBO_2014$Buzz + WBO_2014$season_1)

Residuals: Min 1Q Median 3Q Max -686389 -36281 -4287 28983 908614

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 6521.93 15181.26 0.430 0.66773 WBO_2014$Star 77.93 43.19 1.805 0.07196 . WBO_2014$Genre_Adventure 44019.82 22498.36 1.957 0.05114 . WBO_2014$Genre_Animation -50139.02 24997.96 -2.006 0.04561 * WBO_2014$Genre_Drama -25415.27 13762.30 -1.847 0.06558 . WBO_2014$Genre_SciFi 74677.94 29297.36 2.549 0.01121 * WBO_2014$Sequel 177756.69 24059.74 7.388 9.89e-13 *** WBO_2014$US -13931.68 16037.78 -0.869 0.38558 WBO_2014$Local 77408.04 25346.50 3.054 0.00242 ** WBO_2014$Budget 1362.35 226.52 6.014 4.32e-09 *** WBO_2014$Buzz 556366.01 43345.87 12.836 < 2e-16 *** WBO_2014$season_1Spring -39317.74 17572.16 -2.238 0.02585 * WBO_2014$season_1Summer 1601.87 17654.77 0.091 0.92775 WBO_2014$season_1Winter 5840.08 15992.41 0.365 0.71519 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 120500 on 372 degrees of freedom Multiple R-squared: 0.7034, Adjusted R-squared: 0.693 F-statistic: 67.85 on 13 and 372 DF, p-value: < 2.2e-16

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C.3 Significance second order interactions Pr(>|t|) (Intercept) 0.528945 WBO_2014$Sequel 6.29e-06 *** WBO_2014$Buzz 0.002404 ** WBO_2014$Star 0.044908 * WBO_2014$Budget 0.019791 * WBO_2014$Genre_Action 0.320545 WBO_2014$Genre_Animation 0.985483 WBO_2014$Genre_Comedy 0.174485 WBO_2014$Genre_Crime 0.618867 WBO_2014$Genre_Family 0.504280 WBO_2014$Genre_Mystery 0.828864 WBO_2014$Genre_Thriller 0.286101 WBO_2014$US 0.420109 WBO_2014$Reviews 0.478220 WBO_2014$Long 0.991852 WBO_2014$season_1Spring 0.938793 WBO_2014$season_1Summer 0.616518 WBO_2014$season_1Winter 0.869511 WBO_2014$Genre_Biography 0.891835 WBO_2014$Local 0.344208 WBO_2014$Genre_Romance 0.912878 WBO_2014$Adaptation 0.030719 * WBO_2014$Sequel:WBO_2014$Star 1.94e-06 *** WBO_2014$Star:WBO_2014$Budget 2.34e-06 *** WBO_2014$Buzz:WBO_2014$Star 0.970323 WBO_2014$Sequel:WBO_2014$Genre_Action 0.081792 . WBO_2014$Sequel:WBO_2014$Genre_Animation 0.093455 . WBO_2014$Sequel:WBO_2014$Genre_Comedy 6.85e-05 *** WBO_2014$Sequel:WBO_2014$Genre_Crime 5.39e-10 *** WBO_2014$Sequel:WBO_2014$Genre_Family 5.63e-05 *** WBO_2014$Sequel:WBO_2014$Genre_Mystery 2.69e-05 *** WBO_2014$Sequel:WBO_2014$Genre_Thriller 0.000961 *** WBO_2014$Sequel:WBO_2014$US 7.21e-06 *** WBO_2014$Sequel:WBO_2014$Reviews 1.79e-05 *** WBO_2014$Sequel:WBO_2014$Long 1.53e-05 *** WBO_2014$Sequel:WBO_2014$Buzz 1.29e-07 *** WBO_2014$Sequel:WBO_2014$season_1Spring 3.24e-06 *** WBO_2014$Sequel:WBO_2014$season_1Summer 1.20e-06 *** WBO_2014$Sequel:WBO_2014$season_1Winter 0.000123 *** WBO_2014$Genre_Mystery:WBO_2014$US 0.145740 WBO_2014$Buzz:WBO_2014$US 0.049626 * WBO_2014$US:WBO_2014$season_1Spring 0.489433 WBO_2014$US:WBO_2014$season_1Summer 0.654817 WBO_2014$US:WBO_2014$season_1Winter 0.831366 WBO_2014$Genre_Biography:WBO_2014$Local 0.104818 WBO_2014$Genre_Crime:WBO_2014$Local 6.72e-05 *** WBO_2014$Genre_Mystery:WBO_2014$Local 0.177371 WBO_2014$Local:WBO_2014$Genre_Romance 0.014342 * WBO_2014$Genre_Thriller:WBO_2014$Local 0.000930 *** WBO_2014$Local:WBO_2014$Adaptation 1.79e-05 *** WBO_2014$Buzz:WBO_2014$Local 9.07e-10 *** WBO_2014$Buzz:WBO_2014$Reviews 0.004144 ** WBO_2014$Buzz:WBO_2014$Adaptation 0.099783 . WBO_2014$Budget:WBO_2014$season_1Spring 9.99e-05 *** WBO_2014$Budget:WBO_2014$season_1Summer 0.592729 WBO_2014$Budget:WBO_2014$season_1Winter 0.751822 WBO_2014$Buzz:WBO_2014$Genre_Biography 0.120666 WBO_2014$Buzz:WBO_2014$Genre_Crime 0.270836 WBO_2014$Buzz:WBO_2014$Genre_Family 0.051496 .

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WBO_2014$Buzz:WBO_2014$Genre_Mystery 0.001330 ** C.4 Final Model > summary(model_Q)

Call: lm(formula = WBO_2014$weekend_1 ~ WBO_2014$Sequel + WBO_2014$Buzz + WBO_2014$Budget + WBO_2014$Star + Sequel_Comedy + Sequel_Mystery + Sequel_US + Sequel_Reviews + Sequel_Long + Crime_Local + Romance_Local + Thriller_Local + Adaptation_Local + Buzz_Local + Spring_Sequel + Autumn_Sequel + Summer_Sequel + +Star_Action + Star_Family + Star_SciFi + Star_Sequel + Star_US + Star_Reviews + Star_Buzz + Star_Award + Summer_Star + Winter_Star + Star_Holiday)

Residuals: Min 1Q Median 3Q Max -310973 -29505 -6499 16909 445316

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 4025.18 5873.77 0.685 0.493612 WBO_2014$Sequel -983982.39 130106.37 -7.563 3.37e-13 *** WBO_2014$Buzz 379718.94 33437.39 11.356 < 2e-16 *** WBO_2014$Budget 900.78 146.30 6.157 2.00e-09 *** WBO_2014$Star 476.05 224.50 2.121 0.034653 * Sequel_Comedy 493549.38 44157.01 11.177 < 2e-16 *** Sequel_Mystery -225386.76 70155.02 -3.213 0.001435 ** Sequel_US 500767.59 43332.86 11.556 < 2e-16 *** Sequel_Reviews 125649.20 21103.45 5.954 6.26e-09 *** Sequel_Long 253066.92 42488.56 5.956 6.19e-09 *** Crime_Local -548159.57 99036.18 -5.535 6.04e-08 *** Romance_Local -118354.04 31806.71 -3.721 0.000230 *** Thriller_Local -224188.03 63107.75 -3.552 0.000433 *** Adaptation_Local 217717.21 53265.50 4.087 5.39e-05 *** Buzz_Local 1925105.35 280469.06 6.864 2.98e-11 *** Spring_Sequel -479248.79 56178.22 -8.531 4.20e-16 *** Autumn_Sequel 225015.34 45706.69 4.923 1.30e-06 *** Summer_Sequel -199190.51 48126.12 -4.139 4.36e-05 *** Star_Action 235.39 59.93 3.928 0.000103 *** Star_Family 1086.25 189.29 5.739 2.04e-08 *** Star_SciFi 702.01 74.54 9.417 < 2e-16 *** Star_Sequel -904.24 82.32 -10.984 < 2e-16 *** Star_US -146.07 81.27 -1.797 0.073123 . Star_Reviews -79.92 34.33 -2.328 0.020467 * Star_Buzz 850.53 162.27 5.242 2.73e-07 *** Star_Award -222.14 56.47 -3.934 0.000101 *** Summer_Star -249.22 72.53 -3.436 0.000660 *** Winter_Star 188.87 64.53 2.927 0.003645 ** Star_Holiday 340.57 59.53 5.721 2.24e-08 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 78680 on 357 degrees of freedom Multiple R-squared: 0.8787, Adjusted R-squared: 0.8692 F-statistic: 92.34 on 28 and 357 DF, p-value: < 2.2e-16

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C.5 Statistical Tests on Final Model > jarque.bera.test(residuals(model_Q))

Jarque Bera Test data: residuals(model_Q) X-squared = 624.33, df = 2, p-value < 2.2e-16

> vif(model_Q) WBO_2014$Sequel WBO_2014$Buzz WBO_2014$Budget WBO_2014$Star 91.481235 2.129788 2.411350 91.800526 Sequel_Comedy Sequel_Mystery Sequel_US Sequel_Reviews 3.068280 1.581927 7.877409 100.495117 Sequel_Long Crime_Local Romance_Local Thriller_Local 3.390819 1.580361 1.436578 1.915107 Adaptation_Local Buzz_Local Spring_Sequel Autumn_Sequel 1.814358 2.751553 3.011470 3.923920 Summer_Sequel Star_Action Star_Family Star_SciFi 4.350331 3.405298 1.889508 2.864506 Star_Sequel Star_US Star_Reviews Star_Buzz 5.396764 11.533488 104.355922 5.370256 Star_Award Summer_Star Winter_Star Star_Holiday 2.011970 2.162290 2.598142 2.530860

> bptest(model_Q)

studentized Breusch-Pagan test data: model_Q BP = 140.1, df = 28, p-value < 2.2e-16

> resettest(model_Q)

RESET test data: model_Q RESET = 1.6679, df1 = 2, df2 = 355, p-value = 0.1901

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C.6 Regression with GLS > summary(model_gls) Generalized least squares fit by REML Model: weekend_1 ~ Sequel + Buzz + Budget + Star + Sequel_Comedy + Sequel _Mystery + Sequel_US + Sequel_Reviews + Sequel_Long + Crime_Local + Romance_Local + Thriller_Local + Adaptation_Local + Buzz_Local + Sprin g_Sequel + Autumn_Sequel + Summer_Sequel + +Star_Action + Star_Family + Star_SciFi + Star_Sequel + Star_US + Star_Reviews + Star_Buzz + Star _Award + Summer_Star + Winter_Star + Star_Holiday Data: NULL AIC BIC logLik 9405.369 9521.701 -4672.684

Coefficients: Value Std.Error t-value p-value (Intercept) -4250.2 6658.93 -0.638265 0.5237 Sequel -807415.5 143596.30 -5.622816 0.0000 Buzz 361323.8 38020.33 9.503438 0.0000 Budget 1133.0 164.18 6.900818 0.0000 Star 10565.2 7505.17 1.407719 0.1601 Sequel_Comedy 312396.0 49199.82 6.349535 0.0000 Sequel_Mystery -161450.3 78127.96 -2.066486 0.0395 Sequel_US 451117.9 48149.35 9.369137 0.0000 Sequel_Reviews 87125.9 22985.54 3.790467 0.0002 Sequel_Long 328040.9 46718.39 7.021666 0.0000 Crime_Local -531347.6 110450.43 -4.810734 0.0000 Romance_Local -115816.1 35547.75 -3.258043 0.0012 Thriller_Local -237838.5 70072.39 -3.394183 0.0008 Adaptation_Local 245298.1 58318.15 4.206204 0.0000 Buzz_Local 1967742.8 310852.91 6.330141 0.0000 Spring_Sequel -342729.5 62631.88 -5.472125 0.0000 Autumn_Sequel 268711.5 50349.03 5.336974 0.0000 Summer_Sequel -136019.8 53779.86 -2.529195 0.0119 Star_Action 142.4 67.08 2.123404 0.0344 Star_Family 1078.3 211.54 5.097283 0.0000 Star_SciFi 359.8 83.11 4.329532 0.0000 Star_Sequel -585.7 93.44 -6.268625 0.0000 Star_US -132.9 89.81 -1.479641 0.1399 Star_Reviews -6.7 14.15 -0.476290 0.6342 Star_Buzz 555.8 169.22 3.284373 0.0011 Star_Award -146.3 63.23 -2.313661 0.0213 Summer_Star -347.7 80.21 -4.335082 0.0000 Winter_Star 120.3 72.04 1.670058 0.0958 Star_Holiday 306.5 66.02 4.642906 0.0000

Correlation: (Intr) Sequel Buzz Budget Star Sql_Cm Sql_My Sql_US Sequel -0.091 Buzz -0.173 0.006 Budget -0.313 0.094 -0.321 Star -0.190 0.057 -0.187 -0.094 Sequel_Comedy 0.012 -0.411 -0.066 0.035 -0.090 Sequel_Mystery -0.032 0.052 -0.139 0.168 0.067 0.110 Sequel_US 0.122 -0.309 -0.017 -0.268 -0.060 0.115 -0.312 Sequel_Reviews 0.042 -0.939 -0.016 -0.057 -0.023 0.343 -0.010 0.095 Sequel_Long 0.076 0.132 -0.024 -0.130 -0.065 0.380 0.130 0.134 Crime_Local 0.008 0.043 -0.021 0.013 0.005 -0.049 0.015 -0.019 Romance_Local -0.080 0.008 0.006 0.016 0.046 -0.005 0.021 -0.016 Thriller_Local -0.010 0.007 0.010 0.017 0.012 0.011 0.019 0.005 Adaptation_Local 0.022 0.024 -0.004 -0.002 0.004 -0.028 0.006 -0.010 Buzz_Local -0.018 -0.056 -0.046 0.043 -0.039 0.026 -0.030 -0.014

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Spring_Sequel 0.023 0.091 0.047 -0.079 0.058 -0.396 -0.104 -0.147 Autumn_Sequel 0.001 -0.070 -0.014 0.002 -0.049 -0.024 -0.373 0.167 Summer_Sequel 0.002 0.283 0.022 -0.019 0.009 -0.481 -0.049 -0.165 Star_Action 0.043 0.026 0.056 -0.159 0.067 0.102 -0.066 0.074 Star_Family 0.108 -0.274 -0.004 -0.192 -0.133 0.164 -0.081 0.518 Star_SciFi -0.003 -0.129 -0.006 0.009 -0.100 0.155 -0.090 0.070 Star_Sequel 0.021 0.310 0.119 0.135 0.184 -0.220 0.280 -0.370 Star_US 0.081 -0.116 0.034 -0.169 -0.066 -0.021 -0.021 -0.005 Star_Reviews -0.174 0.082 0.114 0.091 -0.360 0.038 -0.027 0.063 Star_Buzz 0.232 0.124 -0.513 0.015 0.171 -0.115 0.106 -0.056 Star_Award 0.033 0.065 -0.028 0.030 0.229 -0.064 0.058 -0.056 Summer_Star -0.027 0.058 -0.045 0.115 0.018 -0.078 -0.024 -0.014 Winter_Star -0.020 -0.098 0.048 0.060 -0.045 0.097 -0.050 0.023 Star_Holiday 0.006 -0.226 0.098 -0.117 -0.033 0.185 0.001 0.037 Sql_Rv Sql_Ln Crm_Lc Rmnc_L Thrl_L Adpt_L Bzz_Lc Sprn_S Sequel Buzz Budget Star Sequel_Comedy Sequel_Mystery Sequel_US Sequel_Reviews Sequel_Long -0.255 Crime_Local -0.035 0.005 Romance_Local 0.006 -0.016 0.240 Thriller_Local 0.000 0.003 0.202 0.209 Adaptation_Local -0.021 0.005 -0.475 -0.308 -0.232 Buzz_Local 0.043 -0.025 -0.145 -0.379 -0.617 0.007 Spring_Sequel -0.165 -0.260 -0.004 -0.022 -0.050 0.007 0.071 Autumn_Sequel -0.125 -0.010 -0.024 -0.050 -0.060 -0.011 0.119 0.483 Summer_Sequel -0.394 -0.182 0.000 -0.025 -0.056 0.001 0.062 0.610 Star_Action -0.050 0.061 0.017 0.001 0.012 0.018 0.000 -0.009 Star_Family 0.117 0.196 0.050 0.070 0.073 0.012 -0.098 -0.031 Star_SciFi 0.136 -0.257 -0.043 0.009 0.010 -0.042 0.048 -0.193 Star_Sequel -0.278 0.007 0.032 0.040 0.072 0.020 -0.103 -0.021 Star_US 0.128 -0.033 0.090 0.006 -0.170 0.023 0.362 0.047 Star_Reviews -0.099 0.091 -0.067 -0.050 0.105 -0.033 -0.301 -0.102 Star_Buzz -0.103 -0.010 0.050 0.010 -0.042 0.055 -0.002 0.096 Star_Award -0.054 0.045 0.050 -0.033 -0.063 0.091 0.048 0.059 Summer_Star -0.069 0.048 0.068 -0.016 0.023 0.040 0.013 0.047 Winter_Star 0.073 -0.038 -0.093 0.103 0.160 -0.128 -0.072 0.006 Star_Holiday 0.235 -0.055 -0.174 0.024 0.026 -0.079 0.018 -0.018 Atmn_S Smmr_Sq Str_Ac Str_Fm Str_SF Str_Sq Str_US Str_Rv Sequel Buzz Budget Star Sequel_Comedy Sequel_Mystery Sequel_US Sequel_Reviews Sequel_Long Crime_Local Romance_Local Thriller_Local Adaptation_Local Buzz_Local Spring_Sequel Autumn_Sequel Summer_Sequel 0.495 Star_Action 0.123 -0.016

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Star_Family 0.095 -0.060 0.043 Star_SciFi 0.087 -0.029 -0.247 0.078 Star_Sequel -0.296 0.059 -0.215 -0.318 -0.295 Star_US -0.011 0.018 -0.161 0.219 0.128 -0.133 Star_Reviews 0.001 0.000 -0.007 -0.094 -0.054 -0.111 -0.709 Star_Buzz -0.064 0.076 -0.105 -0.024 -0.174 0.016 -0.090 -0.245 Star_Award -0.070 -0.007 0.181 -0.036 -0.370 0.175 -0.137 -0.092 Summer_Star 0.113 -0.147 -0.025 -0.073 -0.031 0.209 -0.044 -0.109 Winter_Star 0.135 -0.036 0.079 -0.048 0.219 0.080 0.027 -0.214 Star_Holiday -0.105 -0.057 -0.225 0.095 0.168 -0.154 0.079 -0.062 Str_Bz Str_Aw Smmr_St Wntr_S Sequel Buzz Budget Star Sequel_Comedy Sequel_Mystery Sequel_US Sequel_Reviews Sequel_Long Crime_Local Romance_Local Thriller_Local Adaptation_Local Buzz_Local Spring_Sequel Autumn_Sequel Summer_Sequel Star_Action Star_Family Star_SciFi Star_Sequel Star_US Star_Reviews Star_Buzz Star_Award 0.171 Summer_Star 0.001 -0.016 Winter_Star -0.203 -0.467 0.224 Star_Holiday -0.129 -0.041 -0.512 0.049

Standardized residuals: Min Q1 Med Q3 Max -5.447980810 -0.306407828 -0.006118794 0.236308413 5.218390519

Residual standard error: 87928.66 Degrees of freedom: 386 total; 357 residual

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C.7 Regression Model Following Weeks > summary(model_week2)

Call: lm(formula = WBO_2014$week_2 ~ WBO_2014$week_1)

Residuals: Min 1Q Median 3Q Max -407129 -15634 -9555 836 785243

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 9.279e+03 6.261e+03 1.482 0.139 WBO_2014$week_1 6.725e-01 2.699e-02 24.915 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 99780 on 337 degrees of freedom (47 observations deleted due to missingness) Multiple R-squared: 0.6481, Adjusted R-squared: 0.6471 F-statistic: 620.7 on 1 and 337 DF, p-value: < 2.2e-16

> summary(model_week3)

Call: lm(formula = WBO_2014$week_3 ~ WBO_2014$week_2)

Residuals: Min 1Q Median 3Q Max -388288 -10478 -5466 1830 462179

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 5.174e+03 4.489e+03 1.153 0.25 WBO_2014$week_2 6.446e-01 2.237e-02 28.815 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 68030 on 299 degrees of freedom (85 observations deleted due to missingness) Multiple R-squared: 0.7352, Adjusted R-squared: 0.7343 F-statistic: 830.3 on 1 and 299 DF, p-value: < 2.2e-16

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> summary(model_week4)

Call: lm(formula = WBO_2014$week_4 ~ WBO_2014$week_3)

Residuals: Min 1Q Median 3Q Max -281778 -8095 -3194 1480 672324

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.918e+03 4.076e+03 0.716 0.475 WBO_2014$week_3 6.476e-01 2.633e-02 24.600 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 59120 on 270 degrees of freedom (114 observations deleted due to missingness) Multiple R-squared: 0.6915, Adjusted R-squared: 0.6903 F-statistic: 605.2 on 1 and 270 DF, p-value: < 2.2e-16

> summary(model_week5)

Call: lm(formula = WBO_2014$week_5 ~ WBO_2014$week_4)

Residuals: Min 1Q Median 3Q Max -234291 -6233 -2364 336 220244

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.303e+03 2.616e+03 0.88 0.38 WBO_2014$week_4 7.025e-01 2.074e-02 33.87 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 35710 on 234 degrees of freedom (150 observations deleted due to missingness) Multiple R-squared: 0.8306, Adjusted R-squared: 0.8299 F-statistic: 1147 on 1 and 234 DF, p-value: < 2.2e-16

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> summary(model_week6)

Call: lm(formula = WBO_2014$week_6 ~ WBO_2014$week_5)

Residuals: Min 1Q Median 3Q Max -190040 -7069 -4987 4879 127316

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 6.468e+03 1.962e+03 3.296 0.00116 ** WBO_2014$week_5 5.131e-01 1.882e-02 27.265 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 24530 on 199 degrees of freedom (185 observations deleted due to missingness) Multiple R-squared: 0.7888, Adjusted R-squared: 0.7878 F-statistic: 743.4 on 1 and 199 DF, p-value: < 2.2e-16

> summary(model_week7)

Call: lm(formula = WBO_2014$week_7 ~ WBO_2014$week_6)

Residuals: Min 1Q Median 3Q Max -107757 -5835 -3166 -971 119448

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 3.169e+03 2.205e+03 1.437 0.153 WBO_2014$week_6 6.011e-01 3.352e-02 17.932 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 24360 on 169 degrees of freedom (215 observations deleted due to missingness) Multiple R-squared: 0.6555, Adjusted R-squared: 0.6535 F-statistic: 321.6 on 1 and 169 DF, p-value: < 2.2e-16

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> summary(model_week8)

Call: lm(formula = WBO_2014$week_8 ~ WBO_2014$week_7)

Residuals: Min 1Q Median 3Q Max -65615 -5051 -2314 32 131171

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 2.086e+03 1.889e+03 1.104 0.272 WBO_2014$week_7 7.281e-01 3.639e-02 20.008 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 18980 on 141 degrees of freedom (243 observations deleted due to missingness) Multiple R-squared: 0.7395, Adjusted R-squared: 0.7377 F-statistic: 400.3 on 1 and 141 DF, p-value: < 2.2e-16

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C.8 ANOVA test demand model ANOVA: single factor OLS model

SUMMARY Groups Count Sum Average Variance OLS estimation 53 7221985,679 136263,8807 5,19E+10 Box office results 53 14356873 270884,3962 2,02E+11

Analysis of Variance Source of Sum of Degrees of Mean of F- F critical Variation Squares freedom Squares statistic P-value value Between groups 4,80251E+11 1 4,80251E+11 3,780848 0,054544 3,932438 Within groups 1,32103E+13 104 1,27022E+11

Total 1,36905E+13 105

ANOVA: single factor estimated GLS model

SUMMARY Groups Count Sum Average Variance Box office 2,02E+1 results 53 14356873 270884,3962 1 109149299, 4,09E+1 GLS 53 7 2059420,749 2

Analysis of Variance Source of Sum of Degrees of Mean of F- F critical Variation Squares freedom Squares statistic P-value value Between 8,47699E+1 39,4949 7,80821E- 3,93243 groups 3 1 8,47699E+13 8 09 8 Within groups 2,2322E+14 104 2,14634E+12

Total 3,0799E+14 105

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D. Demand division special cases D.1 Kids’ movies in school week Wednesday Thursday Friday Saturday Sunday Monday Tuesday 2:30 p.m. Early afternoon 14,179% 0,746% 0,746% 16,418% 16,418% 0,746% 0,746% 5 p.m. Late afternoon 7,463% 1,493% 7,463% 11,940% 11,940% 1,493% 1,493% 8 p.m. Evening 0,746% 0,746% 1,493% 1,493% 0,746% 0,746% 0,746% 10:30 p.m. Late evening 0,000% 0,000% 0,000% 0,000% 0,000% 0,000% 0,000%

D.2 Kids’ movies in holiday week Wednesday Thursday Friday Saturday Sunday Monday Tuesday 2:30 p.m. Early afternoon 11,628% 3,876% 3,876% 11,628% 11,628% 3,876% 3,876% 5 p.m. Late afternoon 11,628% 3,876% 3,876% 7,752% 7,752% 3,876% 3,876% 8 p.m. Evening 0,775% 0,775% 1,550% 1,550% 0,775% 0,775% 0,775% 10:30 p.m. Late evening 0,000% 0,000% 0,000% 0,000% 0,000% 0,000% 0,000%

D.3 Horrors and thrillers Wednesday Thursday Friday Saturday Sunday Monday Tuesday 2:30 p.m. Early afternoon 0,741% 0,741% 0,741% 1,481% 1,481% 0,741% 0,741% 5 p.m. Late afternoon 1,481% 1,481% 2,222% 3,704% 6,667% 1,481% 1,481% 8 p.m. Evening 2,963% 2,963% 8,889% 13,333% 4,444% 2,222% 2,963% 10:30 p.m. Late evening 2,963% 2,963% 11,852% 11,852% 2,222% 2,222% 2,963%

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E. Program in Cplex /********************************************* * OPL 12.8.0.0 Model * Author: Maud Vandevenne * Creation Date: 11-nov.-2018 at 19:45:59 *********************************************/ int NbScreens = ...; int NbWeeks = ...; int NbMovies = ...; float avTicketPrice = ...; float MarketShare = ...; range Screens = 1..NbScreens; int Capacity[Screens] = ...; range Days = 1..7; //day 1 = Wednesday range Times = 1..4; //2:30 p.m., 5 p.m., 8 p.m., 10:30 p.m. range Weeks = 0..NbWeeks-1; range Movies = 1..NbMovies; int US[Movies] = ...; int Local[Movies] = ...; int HorrorThriller[Movies] = ...; int AnimationFamily[Movies] = ...; int Romance[Movies] = ...; int Comedy[Movies] = ...; int Action[Movies] = ...; int Holiday[Weeks] = ...; int Release[Movies] = ...; string outputRange; int numRows; float ProfitSplit[Movies][Weeks] = ...; float WeekDemand[Movies][Weeks] = ...; float WeeklyDivision1[Days][Times] = ...; float WeeklyDivision2[Days][Times] = ...; //For kids movies float WeeklyDivision3[Days][Times] = ...; //Kids movies during the holidays float WeeklyDivision4[Days][Times] = ...; //Horror films and thrillers float expDemand [Movies][Weeks][Days][Times]; float expProfit[Movies][Weeks][Days][Times][Screens]; dvar boolean k[Movies][Weeks][Days][Times][Screens]; execute Demand{ for(var w in Weeks) for(var d in Days) for(var t in Times) for(var m in Movies) if(HorrorThriller[m] == 1){ expDemand[m][w][d][t] = WeekDemand[m][w]*WeeklyDivision4[d][t]; } else if(AnimationFamily[m] == 1){ if(Holiday[w] == 1){ expDemand[m][w][d][t] = WeekDemand[m][w]*WeeklyDivision3[d][t]; } else { expDemand[m][w][d][t] = WeekDemand[m][w]*WeeklyDivision2[d][t]; } } else { expDemand[m][w][d][t] = WeekDemand[m][w]*WeeklyDivision1[d][t]; } for(var w in Weeks) for(var d in Days) for(var t in Times)

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for(var m in Movies) for(var s in Screens) if (expDemand[m][w][d][t] <= Capacity[s]) expProfit[m][w][d][t][s] = expDemand[m][w][d][t]*avTicketPrice*ProfitSplit[m][w]; else { expProfit[m][w][d][t][s] = Capacity[s]*avTicketPrice*ProfitSplit[m][w]; } }

dexpr float Objective = sum(m in Movies, w in Weeks, d in Days, t in Times, s in Screens) (expProfit[m][w][d][t][s]*k[m][w][d][t][s]); maximize Objective;

subject to{ forall(w in Weeks, d in Days, t in Times, s in Screens) ctOneMoviePerScreen: sum(m in Movies) k[m][w][d][t][s] <= 1; forall(m in Movies, w in Weeks, d in Days, t in Times, s in Screens) ctReleaseWeek: if(Release[m]>w){ k[m][w][d][t][s] == 0; }

forall(m in Movies, w in Weeks: w > 0) ctConsecutiveWeeks: if(Release[m]= sum(d in Days, t in Times, s in Screens) k[m][w][d][t][s]; }

forall(m in Movies, d in Days: d > 1) ctConsecutiveDAYS1: if(Release[m]<0) { sum(t in Times, s in Screens) k[m][0][d-1][t][s] >= sum(t in Times, s in Screens) k[m][0][d][t][s]; } forall(m in Movies, w in Weeks: w > 0) ctConsecutiveDAYS2: if(Release[m]= sum(t in Times, s in Screens) k[m][w][1][t][s]; } forall(m in Movies, w in Weeks: w > 0, d in Days: d > 1) ctConsecutiveDAYS3: if(Release[m]= sum(t in Times, s in Screens) k[m][w][d][t][s]; }

forall(m in Movies, w in Weeks) ctMustPlayInReleaseWeek: if(Release[m]==w){ sum(d in Days, t in Times, s in Screens) k[m][w][d][t][s] >=1; }

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forall(w in Weeks, d in Days, t in Times) ctDiversity1: sum(m in Movies, s in Screens) k[m][w][d][t][s]*US[m] <= 0.8*NbScreens; forall(w in Weeks, d in Days, t in Times) ctDiversity2: sum(m in Movies, s in Screens) k[m][w][d][t][s]*Local[m] <= 0.5*NbScreens; forall(w in Weeks, d in Days, t in Times) ctDiversity3: sum(m in Movies, s in Screens) k[m][w][d][t][s]*Romance[m] <= 0.5*NbScreens; forall(w in Weeks, d in Days, t in Times) ctDiversity4: sum(m in Movies, s in Screens) k[m][w][d][t][s]*Comedy[m] <= 0.5*NbScreens; forall(w in Weeks, d in Days, t in Times) ctDiversity5: sum(m in Movies, s in Screens) k[m][w][d][t][s]*Action[m] <= 0.5*NbScreens;

forall(m in Movies, w in Weeks, d in Days, t in Times) ctAllDifferentMovies: sum(s in Screens) k[m][w][d][t][s]<= 1;

forall(w in Weeks, d in Days) ctKidsMoviesHoliday: if(Holiday[w]==1){ sum(m in Movies, s in Screens) k[m][w][d][1][s]*AnimationFamily[m] >= 0.5*NbScreens; } forall(w in Weeks) ctKidsMoviesWednesday: if(Holiday[w]==0){ sum(m in Movies, s in Screens) k[m][w][1][1][s]*AnimationFamily[m] >= 0.5*NbScreens; } forall(w in Weeks) ctKidsMoviesSaturday: if(Holiday[w]==0){ sum(m in Movies, s in Screens) k[m][w][4][1][s]*AnimationFamily[m] >= 0.5*NbScreens; } forall(w in Weeks) ctKidsMoviesSunday: if(Holiday[w]==0){ sum(m in Movies, s in Screens) k[m][w][5][1][s]*AnimationFamily[m] >= 0.5*NbScreens; } } tuple Cinema{ int mm; int ww; int dd; int tt; int ss; int value; }

{Cinema} FilmSet = { | a in Movies, b in Weeks, c in Days, d in Times, e in Screens}; execute WriteOutput{ numRows = FilmSet.size; outputRange = "'Sheet8'!A2:" + "F" + (numRows + 1); writeln(outputRange); }

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/********************************************* * OPL 12.8.0.0 Data * Author: Maud Vandevenne * Creation Date: 26-dec.-2018 at 16:33:14 *********************************************/ SheetConnection sheet("C:\\Users\\...\\Box office 2015.xlsx");

NbMovies from SheetRead(sheet, "'Week demand'!BK2"); NbWeeks from SheetRead(sheet, "'Week demand'!BK3");

WeekDemand from SheetRead(sheet,"'Week demand'!V2:AP132");

WeeklyDivision1 from SheetRead(sheet, "'Day Time-1'!B2:H5"); WeeklyDivision2 from SheetRead(sheet, "'Day Time-2'!B2:H5"); WeeklyDivision3 from SheetRead(sheet, "'Day Time-3'!B2:H5"); WeeklyDivision4 from SheetRead(sheet, "'Day Time-4'!B2:H5");

NbScreens from SheetRead(sheet, "'Theater info'!B4"); avTicketPrice from SheetRead(sheet, "'Theater info'!B5"); MarketShare from SheetRead(sheet, "'Theater info'!B6");

Capacity from SheetRead(sheet, "'Theater info'!B2:K2"); US from SheetRead(sheet, "'Week demand'!AT2:AT132"); Local from SheetRead(sheet, "'Week demand'!AU2:AU132"); HorrorThriller from SheetRead(sheet, "'Week demand'!BH2:BH132"); AnimationFamily from SheetRead(sheet, "'Week demand'!BG2:BG132"); Romance from SheetRead(sheet, "'Week demand'!BE2:BE132"); Comedy from SheetRead(sheet, "'Week demand'!AX2:AX132"); Action from SheetRead(sheet, "'Week demand'!AW2:AW132"); Holiday from SheetRead(sheet, "'Week demand'!V134:AP134");

ProfitSplit from SheetRead(sheet, "'ProfitSplit'!W5:AQ135"); Release from SheetRead(sheet, "'Week demand'!AR2:AR132");

SheetConnection file("C:\\Users\\...\\Cplex\\Solutions\\Solutions_5.xlsx"); FilmSet to SheetWrite(file, outputRange); Objective to SheetWrite(file, "'Sheet8'!H1");

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F. Movie schedules F.1 Five screens, week 3 F.1.1 Model without added constraints Wednesday Thursday 2:30 5 8 5 8 p.m. p.m. p.m. 10:30 p.m. 2:30 p.m. p.m. p.m. 10:30 p.m. Screen 1 1 1 1 42 1 1 1 48 Screen 2 1 1 1 48 1 1 1 42 Screen 3 1 1 1 48 1 1 1 1 Screen 4 1 1 1 42 1 1 1 1 Screen 5 1 1 1 1 1 1 1 1

Friday Saturday 2:30 5 8 5 8 p.m. p.m. p.m. 10:30 p.m. 2:30 p.m. p.m. p.m. 10:30 p.m. Screen 1 1 1 1 1 1 1 1 1 Screen 2 1 1 1 1 1 1 1 1 Screen 3 1 1 1 1 1 1 1 1 Screen 4 1 1 1 1 1 1 1 1 Screen 5 1 1 1 1 1 1 1 1

Sunday Monday 2:30 5 8 5 8 p.m. p.m. p.m. 10:30 p.m. 2:30 p.m. p.m. p.m. 10:30 p.m. Screen 1 1 1 1 42 1 1 1 42 Screen 2 1 1 1 42 1 1 1 48 Screen 3 1 1 1 42 1 1 1 48 Screen 4 1 1 1 42 1 1 1 48 Screen 5 1 1 1 1 1 1 1 1

Tuesday 2:30 5 8 p.m. p.m. p.m. 10:30 p.m. Screen 1 1 1 1 48 Screen 2 1 1 1 48 Screen 3 1 1 1 42 Screen 4 1 1 1 42 Screen 5 1 1 1 1

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F.1.2 Model with consecutive weeks Wednesday Thursday 2:30 5 8 5 8 p.m. p.m. p.m. 10:30 p.m. 2:30 p.m. p.m. p.m. 10:30 p.m. Screen 1 1 1 1 42 1 1 1 42 Screen 2 1 1 1 48 1 1 1 48 Screen 3 18 1 1 48 1 1 1 48 Screen 4 18 14 44 42 44 44 8 42 Screen 5 44 44 44 42 44 44 44 42

Friday Saturday 2:30 5 8 5 8 p.m. p.m. p.m. 10:30 p.m. 2:30 p.m. p.m. p.m. 10:30 p.m. Screen 1 1 1 8 1 18 8 22 1 Screen 2 1 1 8 1 18 8 22 1 Screen 3 1 1 22 1 18 22 36 1 Screen 4 44 14 36 22 8 27 8 22 Screen 5 48 44 44 42 44 42 42 42

Sunday Monday 2:30 5 8 5 8 p.m. p.m. p.m. 10:30 p.m. 2:30 p.m. p.m. p.m. 10:30 p.m. Screen 1 18 22 1 42 1 1 1 48 Screen 2 18 22 1 42 1 1 1 42 Screen 3 18 36 1 48 1 1 1 42 Screen 4 8 22 44 42 1 44 8 42 Screen 5 44 44 44 42 44 48 44 42

Tuesday 2:30 5 8 p.m. p.m. p.m. 10:30 p.m. Screen 1 1 1 1 48 Screen 2 1 1 1 48 Screen 3 1 1 1 48 Screen 4 48 44 8 42 Screen 5 44 44 44 42

F.1.3 Model with consecutive weeks and all other constraints Wednesday Thursday 2:30 5 8 5 8 p.m. p.m. p.m. 10:30 p.m. 2:30 p.m. p.m. p.m. 10:30 p.m. Screen 1 14 44 3 48 42 42 1 42 Screen 2 1 1 1 42 45 45 3 48 Screen 3 18 14 44 45 1 1 44 45 Screen 4 27 18 22 4 3 3 22 3 Screen 5 44 13 24 39 21 21 24 21

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Friday Saturday 2:30 5 8 5 8 p.m. p.m. p.m. 10:30 p.m. 2:30 p.m. p.m. p.m. 10:30 p.m. Screen 1 44 1 1 8 8 1 22 1 Screen 2 45 48 8 1 18 8 8 8 Screen 3 1 14 22 4 13 22 36 4 Screen 4 3 18 24 22 14 36 24 22 Screen 5 21 27 36 36 44 14 44 36

Sunday Monday 2:30 5 8 5 8 p.m. p.m. p.m. 10:30 p.m. 2:30 p.m. p.m. p.m. 10:30 p.m. Screen 1 18 22 1 42 44 44 1 42 Screen 2 8 8 44 48 45 45 3 48 Screen 3 22 36 45 45 1 1 44 43 Screen 4 27 14 3 49 3 3 22 45 Screen 5 44 42 24 4 21 21 24 4

Tuesday 2:30 5 8 p.m. p.m. p.m. 10:30 p.m. Screen 1 45 45 1 48 Screen 2 44 44 3 42 Screen 3 1 1 42 45 Screen 4 3 3 22 4 Screen 5 21 21 24 21

F.2 Five screens, week 17 F.2.1 Model without added constraints Wednesday Thursday 2:30 5 8 5 8 p.m. p.m. p.m. 10:30 p.m. 2:30 p.m. p.m. p.m. 10:30 p.m. Screen 1 67 76 87 96 99 76 87 96 Screen 2 67 76 87 96 99 76 87 96 Screen 3 67 67 81 96 91 76 81 96 Screen 4 67 67 76 96 76 76 76 91 Screen 5 61 77 73 82 72 67 73 77

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Friday Saturday 2:30 5 8 5 8 p.m. p.m. p.m. 10:30 p.m. 2:30 p.m. p.m. p.m. 10:30 p.m. Screen 1 99 76 76 77 76 76 77 77 Screen 2 99 76 76 77 76 76 77 77 Screen 3 91 67 72 77 67 67 77 77 Screen 4 76 67 76 77 74 74 73 77 Screen 5 76 74 69 73 74 69 68 69

Sunday Monday 2:30 5 8 5 8 p.m. p.m. p.m. 10:30 p.m. 2:30 p.m. p.m. p.m. 10:30 p.m. Screen 1 76 77 87 96 99 76 87 96 Screen 2 76 67 87 96 99 76 87 96 Screen 3 72 76 87 96 91 76 81 96 Screen 4 74 74 76 96 76 76 76 96 Screen 5 74 50 74 87 76 76 74 87

Tuesday 2:30 5 8 p.m. p.m. p.m. 10:30 p.m. Screen 1 99 76 87 96 Screen 2 99 76 87 96 Screen 3 91 76 81 96 Screen 4 76 76 76 96 Screen 5 76 72 73 82

F.2.2 Model with consecutive weeks Wednesday Thursday 2:30 5 8 5 8 p.m. p.m. p.m. 10:30 p.m. 2:30 p.m. p.m. p.m. 10:30 p.m. Screen 1 67 76 87 96 99 76 87 96 Screen 2 67 76 87 96 99 76 87 96 Screen 3 67 67 81 96 99 76 87 96 Screen 4 44 67 76 96 99 96 72 91 Screen 5 55 55 55 102 102 102 55 102

Friday Saturday 2:30 5 8 5 8 p.m. p.m. p.m. 10:30 p.m. 2:30 p.m. p.m. p.m. 10:30 p.m. Screen 1 99 76 72 77 67 67 55 77 Screen 2 99 76 72 77 67 67 55 77 Screen 3 99 67 55 77 44 67 55 77 Screen 4 87 67 55 55 55 55 55 55 Screen 5 99 55 50 55 55 44 55 55

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Sunday Monday 2:30 5 8 5 8 p.m. p.m. p.m. 10:30 p.m. 2:30 p.m. p.m. p.m. 10:30 p.m. Screen 1 67 55 87 96 99 76 87 96 Screen 2 67 55 87 96 99 76 87 96 Screen 3 44 55 87 96 99 76 87 96 Screen 4 55 55 91 96 76 76 72 96 Screen 5 55 42 55 105 112 112 48 104

Tuesday 2:30 5 8 p.m. p.m. p.m. 10:30 p.m. Screen 1 99 76 87 96 Screen 2 99 76 87 96 Screen 3 99 76 81 96 Screen 4 76 76 77 96 Screen 5 112 112 50 112

F.2.3 Model with consecutive weeks and all other constraints Wednesday Thursday 2:30 5 8 5 8 p.m. p.m. p.m. 10:30 p.m. 2:30 p.m. p.m. p.m. 10:30 p.m. Screen 1 67 76 93 96 106 76 87 99 Screen 2 76 94 87 112 99 96 93 96 Screen 3 94 67 88 99 91 99 88 106 Screen 4 44 88 96 108 76 87 77 104 Screen 5 73 102 73 60 114 106 83 60

Friday Saturday 2:30 5 8 5 8 p.m. p.m. p.m. 10:30 p.m. 2:30 p.m. p.m. p.m. 10:30 p.m. Screen 1 99 76 72 77 67 72 72 77 Screen 2 112 87 77 81 72 67 55 88 Screen 3 106 67 55 88 44 55 73 82 Screen 4 76 94 73 55 73 74 61 55 Screen 5 55 55 61 78 94 61 112 73

Sunday Monday 2:30 5 8 5 8 p.m. p.m. p.m. 10:30 p.m. 2:30 p.m. p.m. p.m. 10:30 p.m. Screen 1 67 67 87 96 99 76 87 112 Screen 2 72 55 93 112 110 96 93 96 Screen 3 44 73 96 99 105 113 88 99 Screen 4 74 44 77 108 76 92 77 108 Screen 5 94 61 83 106 55 94 78 106

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Tuesday 2:30 5 8 p.m. p.m. p.m. 10:30 p.m. Screen 1 99 76 93 112 Screen 2 106 96 87 96 Screen 3 112 99 88 99 Screen 4 96 106 77 108 Screen 5 55 67 83 60

F.3 Three screens, week 3 F.3.1 Model without added constraints Wednesday Thursday 2:30 5 8 5 8 p.m. p.m. p.m. 10:30 p.m. 2:30 p.m. p.m. p.m. 10:30 p.m. Screen 1 1 1 1 48 1 1 1 1 Screen 2 1 1 1 42 1 1 1 1 Screen 3 1 1 1 1 1 1 1 1

Friday Saturday 2:30 5 8 5 8 p.m. p.m. p.m. 10:30 p.m. 2:30 p.m. p.m. p.m. 10:30 p.m. Screen 1 1 1 1 1 1 1 1 1 Screen 2 1 1 1 1 1 1 1 1 Screen 3 1 1 1 1 1 1 1 1

Sunday Monday 2:30 5 8 5 8 p.m. p.m. p.m. 10:30 p.m. 2:30 p.m. p.m. p.m. 10:30 p.m. Screen 1 1 1 1 48 1 1 1 42 Screen 2 1 1 1 48 1 1 1 48 Screen 3 1 1 1 1 1 1 1 1

Tuesday 2:30 5 8 p.m. p.m. p.m. 10:30 p.m. Screen 1 1 1 1 42 Screen 2 1 1 1 48 Screen 3 1 1 1 1

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F.3.2 Model with consecutive weeks Wednesday Thursday 2:30 5 8 5 8 p.m. p.m. p.m. 10:30 p.m. 2:30 p.m. p.m. p.m. 10:30 p.m. Screen 1 18 1 1 48 1 1 1 1 Screen 2 18 14 8 48 1 1 8 1 Screen 3 18 44 22 42 44 44 22 42

Friday Saturday 2:30 5 8 5 8 p.m. p.m. p.m. 10:30 p.m. 2:30 p.m. p.m. p.m. 10:30 p.m. Screen 1 1 1 22 8 36 18 36 8 Screen 2 1 14 22 22 36 18 24 22 Screen 3 44 22 44 36 44 18 44 42

Sunday Monday 2:30 5 8 5 8 p.m. p.m. p.m. 10:30 p.m. 2:30 p.m. p.m. p.m. 10:30 p.m. Screen 1 22 22 1 42 1 1 1 42 Screen 2 24 27 1 48 1 1 8 48 Screen 3 44 44 42 42 44 44 22 48

Tuesday 2:30 5 8 p.m. p.m. p.m. 10:30 p.m. Screen 1 1 1 1 42 Screen 2 1 1 8 42 Screen 3 44 44 22 42

F.3.3 Model with consecutive weeks and all other constraints Wednesday Thursday 2:30 5 8 5 8 p.m. p.m. p.m. 10:30 p.m. 2:30 p.m. p.m. p.m. 10:30 p.m. Screen 1 18 14 1 48 1 1 1 45 Screen 2 13 18 22 45 45 45 22 42 Screen 3 44 8 21 24 21 21 21 47

Friday Saturday 2:30 5 8 5 8 p.m. p.m. p.m. 10:30 p.m. 2:30 p.m. p.m. p.m. 10:30 p.m. Screen 1 1 14 36 4 36 36 24 8 Screen 2 3 18 24 8 27 27 36 22 Screen 3 21 8 44 36 44 44 44 36

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Sunday Monday 2:30 5 8 5 8 p.m. p.m. p.m. 10:30 p.m. 2:30 p.m. p.m. p.m. 10:30 p.m. Screen 1 13 18 1 42 1 1 1 42 Screen 2 14 27 3 45 3 45 22 45 Screen 3 44 44 21 24 21 21 21 24

Tuesday 2:30 5 8 p.m. p.m. p.m. 10:30 p.m. Screen 1 1 1 1 42 Screen 2 3 45 22 49 Screen 3 21 21 39 4

F.4 Three screens, week 17 F.4.1 Model without added constraints

Wednesday Thursday 2:30 5 8 5 8 p.m. p.m. p.m. 10:30 p.m. 2:30 p.m. p.m. p.m. 10:30 p.m. Screen 1 67 67 77 96 76 76 77 96 Screen 2 44 67 76 96 76 76 76 91 Screen 3 67 44 77 91 76 76 77 81

Friday Saturday 2:30 5 8 5 8 p.m. p.m. p.m. 10:30 p.m. 2:30 p.m. p.m. p.m. 10:30 p.m. Screen 1 76 67 77 77 77 77 74 77 Screen 2 76 67 74 77 74 74 73 77 Screen 3 76 76 73 73 74 74 73 73

Sunday Monday 2:30 5 8 5 8 p.m. p.m. p.m. 10:30 p.m. 2:30 p.m. p.m. p.m. 10:30 p.m. Screen 1 76 74 76 96 76 76 77 96 Screen 2 74 74 76 96 76 76 76 96 Screen 3 74 71 72 91 76 76 76 91

Tuesday 2:30 5 8 p.m. p.m. p.m. 10:30 p.m. Screen 1 76 76 77 96 Screen 2 76 76 76 96 Screen 3 76 76 77 91

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F.4.2 Model with consecutive weeks Wednesday Thursday 2:30 5 8 5 8 p.m. p.m. p.m. 10:30 p.m. 2:30 p.m. p.m. p.m. 10:30 p.m. Screen 1 44 67 77 96 87 76 77 96 Screen 2 44 67 87 96 76 87 81 96 Screen 3 44 44 55 99 99 99 55 99

Friday Saturday 2:30 5 8 5 8 p.m. p.m. p.m. 10:30 p.m. 2:30 p.m. p.m. p.m. 10:30 p.m. Screen 1 87 67 55 72 67 67 55 72 Screen 2 76 67 55 55 44 44 48 55 Screen 3 99 44 48 55 44 48 42 55

Sunday Monday 2:30 5 8 5 8 p.m. p.m. p.m. 10:30 p.m. 2:30 p.m. p.m. p.m. 10:30 p.m. Screen 1 44 67 87 96 87 76 77 96 Screen 2 44 48 81 96 87 76 55 96 Screen 3 48 42 55 112 99 99 55 112

Tuesday 2:30 5 8 p.m. p.m. p.m. 10:30 p.m. Screen 1 87 76 77 96 Screen 2 76 87 72 96 Screen 3 112 112 55 96

F.4.3 Model with consecutive weeks and all other constraints Wednesday Thursday 2:30 5 8 5 8 p.m. p.m. p.m. 10:30 p.m. 2:30 p.m. p.m. p.m. 10:30 p.m. Screen 1 67 67 77 96 87 76 88 96 Screen 2 44 94 76 112 93 105 72 113 Screen 3 83 44 83 114 106 94 73 106

Friday Saturday 2:30 5 8 5 8 p.m. p.m. p.m. 10:30 p.m. 2:30 p.m. p.m. p.m. 10:30 p.m. Screen 1 87 67 55 72 67 67 73 60 Screen 2 93 94 73 60 44 55 61 55 Screen 3 106 44 61 73 78 78 60 78

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Sunday Monday 2:30 5 8 5 8 p.m. p.m. p.m. 10:30 p.m. 2:30 p.m. p.m. p.m. 10:30 p.m. Screen 1 67 67 88 96 87 76 77 96 Screen 2 44 61 112 99 76 94 111 112 Screen 3 78 78 55 106 106 99 55 110

Tuesday 2:30 5 8 p.m. p.m. p.m. 10:30 p.m. Screen 1 87 76 77 96 Screen 2 108 112 88 99 Screen 3 106 94 83 106

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G. Movie titles Nr. Movie title Nr. Movie title 1 Avengers: Age of Ultron 43 The Farewell Party 2 Furious 7 44 Tomorrowland 3 The Longest Ride 45 Far from the Madding Crowd 4 Ouija 46 She's Funny that way 5 Home (2015) 47 Trois souvenirs de ma jeuness (My Golden Days) 6 Dark Places 48 San Andreas 7 Suite Francaise 49 Ventoux 8 Big Game 50 Spy 9 The Divergent Series: Insurgent 51 Danny Collins 10 Run All Night 52 Fasandraeberne (The Absent One) 11 Bloed, Zweet & Tranen 53 Belle 12 Focus (2015) 54 The Cut 13 Shaun the Sheep Movie 55 Jurassic World 14 Cinderella (2015) 56 Comme un avion (The Sweet Escape) 15 Pourquoi j'ai pas mangé mon père 57 Man Up 16 Foute Vrienden 58 The Dark Horse 17 Taxi (2015) 59 The Pasta Detectives 2 18 Tinker Bell & the Legend of the NeverBeast 60 Poltergeist (2015) 19 Kingsman: The Secret Service 61 The Age of Adaline 20 Still Alice 62 Kidnapping Mr. Heineken 21 La Tierra Roja 63 Valley of Love 22 The Second Best Exotic Marigold Hotel 64 Paul Blart: Mall Cop 2 23 Big Hero 6 65 The Duke of Burgundy 24 Entre amis 66 Zurich 25 American Sniper 67 Inside Out 26 The Water Diviner 68 Entourage 27 Bibi & Tina - Der Film 69 The Gunman 28 La famille Bélier 70 En équilibre 29 Fifty Shades of Grey 71 Difret 30 Charlie's Country 72 Terminator: Genisys 31 The SpongeBob Movie: Sponge Out of Water 73 Les profs 2 32 Selma 74 Lee & Cindy C. 33 Atlantic 75 Victoria 34 Jimi: All Is by My Side 76 Minions 35 Halbe Brüder 77 Magic Mike XXL 36 Child 44 78 Amy 37 Papa ou maman 79 Dior and I 38 De Ontsnapping 80 Tu dors Nicole 39 Macondo 81 Ant-Man 40 Journal d'une femme de chambre 82 Insidious Chapter 3 41 Lost River 83 Woman in Gold 42 Mad Max: Fury Road 84 La isla minima

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Nr. Movie title Nr. Movie title 85 Ooops! Noah is Gone 109 Sinister 2 86 Survivor 110 We are your Friends 87 Pitch Perfect 2 111 Dheepan 88 Southpaw 112 Le tout nouveau testament 89 While we're young 113 Vacation 90 Mr. Holmes 114 Rak ti Khon Kaen 91 Pixels 115 Hitman: Agent 47 92 Paper Towns 116 Ricki and the Flash 93 Self/Less 117 Galloping Mind 94 The Little Prince 118 Solace 95 Alting bliver godt igen (Everything will be 119 Straight Outta Compton fine) 96 Mission Impossible - Rogue Nation 120 Café Derby 97 Irrational Man 121 Marguerite 98 Fidelio: Alice's Odyssey 122 Testament of Youth 99 Ted 2 123 Knight of Cups 100 Mustang 124 Keet & Koen en de speurtocht naar Bassie & Adriaan 101 Theeb 125 La dame dans l'auto avec des lunettes et un fusil 102 The Man from U.N.C.L.E. 126 Fly Away Solo 103 Une famille à louer 127 Maze Runner: The Scorch Trials 104 American Ultra 128 Everest (2015) 105 Paradise Trips 129 Life 106 La Belle Saison 130 Cafard 107 A Brilliant Young Mind 131 Schneider vs. Bax 108 Fantastic Four

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