Pre-Study of Traffic Planning Optimizer for Potential Implementation At

Pre-study of trafﬁc planning optimizer for potential

implementation at TUIﬂy Nordic

Per Enqvist, Senior Advisor Clement Berguerand, Supervisor at TUIﬂy Nordic

Department of Mathematics

Royal Institute of Technology

December 2010

Pre-study of trafﬁc planning optimizer for potential

implementation at TUIﬂy Nordic

Mercedes Inal

Department of Mathematics

Master of Science

Optimization in the airline industry is hard to accomplish, there are many areas of equal importance all of which with important and often contradicting parameters to take into account in order to achieve a model that represents real world situations. This report gives a unique account of the planning process as it is observed for the duration of this project at TUIfly Nordic. It is far from a complete documentation of airline planning processed but it is an insight as to how one such process takes place. With focus on the scheduling process for air-traffic program it was found that a risk management analysis into the operating parameters of a schedule is necessary and that before researching the implementation of third-party software, an inventory of the software available at the time of writing is more beneficiary to the current work flow.

Keywords: Airline optimization, air-trafﬁc planning, TUIﬂy Nordic. ABSTRAKT

Förstudie av ett optimerings program för ﬂygtraﬁk

planering och dess potentiella implementation hos

TUIﬂy Nordic

Mercedes Inal

Department of Mathematics

Master of Science

Optimering i flygbranschen är svårt att åstadkomma, det finns många områden av lika stor bety- delse alla med viktiga och ofta motstridiga parametrar att ta hänsyn till för att uppnå en modell som representerar verkligheten. Denna rapport ger en unik bakgrund av flygtrafikplanerings processen som observerats för det här projektet på TUIfly Nordic. Det är långt ifrån en komplett dokumenta- tion av hur flygbolag utför sin flygtrafikplanering, men det är en insikt i hur en sådan process sker. Med fokus på schemaläggnings processen för flygtrafiken konstaterades det att en riskanalys av driftparametrar som påverkar ett flygtrafikschema är nödvändigt och att innan forsking läggs ner för investering av tredje partens programmvara är det rekommenderat att utföra en undersökning i tillgänglig programvara som kan underlätta det nuvarande arbetsflödet.

Nyckelord: Airline optimization, air-trafﬁc planning, TUIﬂy Nordic. ACKNOWLEDGMENTS

I would like to acknowledge the people at TUIﬂy Nordic for their knowledge and insights that by far make up the body of this project it has been a great experience. Thank you to my supervisors for good advice and at times pushing the project forward once more, Clement Berguerand for reviewing this document and for ripping it to shreds as every good advisor should do to a thesis draft, Anne-Lie Bråholm and Marcus Karlsson the Planners. There are several more that have helped me along the way and I am grateful to have met all of you and appreciate all the help you have given me along the way.

My most heartfelt thank you to my senior supervisor Lecturer and researcher at KTH Per Enqvist for his patience, countless amounts of advice in hard times and general therapy.

Finally, I would like to offer condolences to a great teacher, Professor Ulf Jönsson, you will be missed. Contents

Table of Contents v

List of Figures vii

List of Tables viii

1 Introduction 1 1.1 Structure...... 2 1.2 Literature ...... 2 1.3 Purposeandproblemdescription ...... 3 1.3.1 Background...... 4 1.3.2 Limitationsanddemands...... 5 1.4 TUIAGGroup ...... 6 1.4.1 TUIﬂyNordic...... 6 1.4.2 Touroperator...... 7 1.5 CoststructureforTUIﬂyNordic ...... 8

2 Planning process 10 2.1 Timeframeandinvolvedparties...... 11 2.2 Longtermfleetplan...... 12 2.3 Productandcontent...... 14 2.4 TUIgether...... 14 2.5 Planningandproduction ...... 14 2.6 Longandmediumtermtrafficplanning ...... 15 2.6.1 Maintenance ...... 16 2.6.2 Slotsandtrafficrights ...... 19 2.6.3 Resourceplanning ...... 19 2.7 Handling ...... 21 2.8 CrewPairing ...... 22 2.9 Dutylimitations...... 25 2.10Pricing...... 26

v CONTENTS vi

3 System processes 28 3.1 Systemsdescription...... 29 3.1.1 IDPS ...... 30 3.1.2 SabreRocadeSuite...... 34

4 Airline Optimization 38 4.1 Scheduleplanning...... 39 4.1.1 Scheduledesign ...... 40 4.1.2 Fleetassignment ...... 41 4.1.3 Aircraftrouting...... 43 4.1.4 Crewscheduling ...... 44 4.2 Backgroundforcombinedmodel ...... 47 4.3 Simultaneous aircraft routing and crew scheduling ...... 48 4.4 Solutionsuggestionfor an extended simultaneousaircraft routing and crew schedul- ingmodel ...... 53

5 Analysis 55 5.1 Softwareneed...... 56 5.2 Keyperformanceindicators...... 58 5.3 Riskmanagement...... 60 5.4 Multiobjectiveoptimization...... 63

6 Discussion 66 6.1 Checklistforminimumsoftwareperformance ...... 66 6.2 ConclusionsfortheneedsofTUIﬂyNordic ...... 67 6.3 Futureresearch ...... 68

Bibliography 70 List of Figures

1.1 TheTUIsmiley...... 7 1.2 Cost structure comparison between short and long haul ﬂights...... 8

2.1 PlanningprocessatTUIflyNordic ...... 10 2.2 Timeframe in detail, overlooking several departments ...... 11 2.3 Airplanesinproductionduring2010and2011...... 12 2.4 Decisionchainduringfleetplanningstage ...... 13 2.5 Inputstotheair-trafficprogram...... 15 2.6 Resourcecalculationchain ...... 20 2.7 Example of a current timelinefor flight deck planning...... 21 2.8 Pairingdemands...... 23 2.9 Inputsintocrewpairingstage...... 24 2.10 Rulesandregulationshierarchy...... 25

3.1 SystemoverviewatTUIﬂyNordic ...... 28 3.2 Systems interconnectivity and modules currently at use atTUIﬂyNordic ...... 29 3.3 IDPSmoduleoverview ...... 30 3.4 ScreenshotofArsisinterface...... 32 3.5 ScreenshotofOpsconinterface ...... 33 3.6 ScreenshotofAirpasinterface ...... 34 3.7 RM5moduleoverview ...... 35 3.8 ScreenshotoftheRM5module:PARinterface...... 36

4.1 Scheduledesign...... 39

5.1 SystemsToday ...... 56 5.2 Simpleﬂightscheduleexample...... 59 5.3 Riskparameters...... 61 5.4 Riskassessmentmodel ...... 62 5.5 Riskmanagementactions...... 63 5.6 Marginalallocationexample ...... 65

vii List of Tables

1.1 CurrentﬂeetatTUIﬂyNordic...... 7 1.2 Exampleofrotationswithvariouscrewdemands...... 9

5.1 Keyperformanceindicators...... 58

1 Legsﬂownbyaircrafttypepermonth...... 73

viii 1

Introduction

It’s not always easy to appreciate the workload that goes into planning an airplane route when you are stranded in an airport on the far-side of the planet waiting for that delayed flight home at the end of your two week vacation. However the effort behind planning aircraft rotations is an intricate process and it is a daily challenge to accommodate the needs of passengers in a changing environment, that make that two week vacation in, for instance Thailand, possible. The airline business is a competitive and ever-changing world where charter companies have to keep up with the rapid pace, forcing them to always provide the customers with high quality service and secure products at the same time. Air-traffic planning starts well over a year and a half before execution date and the process operates within very tight margins so that even small improvements in aircraft rotations efficiency can directly result in substantial cost reductions and savings. A fully operational air-traffic schedule is the result of compromises and sometimes negotiations between various and often opposing constraints. These constraints will be studied throughout the stages of this project. TUIfly Nordic has a growing need to rapidly evaluate and compare multiple traffic planning scenarios in order to determine and provide the customer (for TUIfly Nordic this customer is the Tour Operator) with an optimal choice of air-traffic schedule. There is also a need to generate economical estimates for each scenario that yield alternatives or contingency plans if one scenario should fail. Multiple traffic scenarios would also permit the planning department to measure gains or losses while following key performance indicators. The objective of this thesis is to provide an answer to this need. The methodology used to reach this solution begins with evaluating the current process followed by a recommendation that could allow for implementation of additional software to accommodate this need. This software shall present any consequences, economical or other, in a correct and easy way while also bridging the gap between certain planning process stages.

1 1.1. STRUCTURE 2

1.1 Structure

The structure of this thesis is divided into three parts. The first part (Chapters 1, 2 and 3), is comprised by an introductory chapter that include several sections describing the background and purpose of the problem presented by TUIfly Nordic. Chapter 1 also describes the limitations and needs set by the airline business in general and the company specifically. A brief background history of TUIfly Nordic is given but the weight of this chapter lies in the cost structure section explaining how small changes to the air-traffic planning stage can result in large savings. Chapter 2 provides a detailed description of the processes that are directly connected to the air-traffic planning stage at TUIfly Nordic. The descriptions of these processes are based primarily on observations throughout the commitment of the thesis. While some sections of this industry are widely approached by scientific research (such as aircraft fleet planning and crew scheduling) others are left uncharted. The focus of this thesis lies in the early stages of planning the aircraft rotations but the influences exerted by the surrounding process are taken into account as they have a tremendous impact on the schedule making. This chapter will start describing the typical outlook and timeline for a winter season air-traffic planning, and, from there, describe each process as is currently carried out at TUIfly Nordic. Chapter 3, will give an overview of the software processes concerning the planning stage, currently operative in TUIfly Nordic. The second part (Chapter 4) is a mathematical approach to describe some of the planning processes that are represented in Chapter 2. This is a technical and mathematical introduction into the stages of the planning process, describing some of the difficulties of dealing with charter airline planning compared to regular airlines. The third part (Chapter 5) of this thesis is dedicated to an analysis made on an observational basis depicting the problem areas and where future awareness is recommended. These concern the set up of key performance indicators, the nature of these indicators and risk management. The purpose of describing the risks of air-traffic planning is to create a base for future research into optimizing the planning process at TUIfly Nordic. This chapter will be followed by conclusions reached concerning the needs and requirements of TUIfly Nordic (Chapter 6).

1.2 Literature

Literature available in the field of airline optimization is abundant, although the literature can be said to concentrate towards certain hot spots in the airline industry, such as crew operations scheduling, as most researchers state that this is the area where most savings can be made in the airline industry, see S Lavoie et al. [1], G. Desaulniers et al. [2]- [3] and N. Kohl et al. [4]. The majority of literature in this field are mathematically oriented (for further reading, see Chapter 6), and as such do not cover the inner operations of an airline. Information regarding where and why the planning process for an air-traffic schedule is hard, is non-existent. 1.3. PURPOSE AND PROBLEM DESCRIPTION 3

The reason for this is mainly that regular airlines are studied and not charter airlines, the described models are based on a fixed traffic schedule, where the daily operations are often repetitive through out the week and year and there is no seasonal structure (summer schedule, winter schedule) with changing destinations to take into account. Daily scheduling, or modeling on a fixed set of data is both simpler and computationally more efficient. Articles and papers concerning this subject, Barnhart et al. [5] describe the planning process in four hierarchical steps where each step is the input for the next, Schedule planning- flight schedule (based on passenger and travel statistics and demands forecasts) → Fleet planning (matching the flight segments to specific types of aircraft, according to capacity and flight characteristics e.g. distance) → Aircraft routing (maintenance operations scheduling in order to meet flight regulations and demands while assuring the best working conditions for the fleet) → Crew scheduling (allocating tasks according to the flight schedule to the available crew members assuring that labour, operational and governmental regulations are met, this stage is divided into two interconnected subproblems the crew rotations scheduling and crew rostering, i.e. rotations planning and individual assignment of each rotation). Few articles bring insight into the workings of bringing forth an optimal flight schedule. N. Kohl et al. [6], grant some perspective into the disruptions (e.g. crew sickness, bad weather and technical problems) that occur during airline operations, mentioning that “...airlines have become more concerned with developing an optimal flight schedule, allowing little slack to accommodate variations from the optimal solution”.

1.3 Purpose and problem description

The airline business is one of the hardest industries to produce growth in, the competition from surrounding airlines are rough and operative expenses can easily carry away. There is a constant demand for improvement and the need to turn a profit is key to most airlines survival. Meeting this demand is handled individually depending on which company is under scrutiny. However, most often, the airline businesses are old fashioned and established in their internal processes, meaning that introducing changes into the systems and processes in use often becomes a challenge. Most charter airlines are part of holding companies, where there is often a parent company holding the majority of shares within the ownership while there can be several smaller airlines holding minority shares, so called associate companies. It is commonplace within such business model that mergers and acquisitions occur on a frequent rate. This is especially noticeable in the rapidly changing environment of the airline business. With mergers of this size come internal changes (e.g. management, operational structure, financial and software structure) appropriate for the integration of the acquired company, these changes are requested by the parent company, and not all of which will be considered agreeable with existing operations. TUIfly Nordic is part of the larger TUI AG, and was acquired in the year 2000. The operations of the Planning department at 1.3. PURPOSE AND PROBLEM DESCRIPTION 4

TUIfly Nordic is to a certain extent a consequence of the merger of the company with the German TUI AG, such that in order to maintain an interconnecting financial system the German one was adopted, and following this software came several other that communicate with it. The Planning department feels that the traffic planning software that is currently in use is not optimal. Therefore, research was put into possible changes that could improve the course of the planning process, such as better software assistance. High demands are placed on such a change, such as the need for it to operate stably without contributing to already existing system difficulties. These difficulties are mainly caused by the lack of fast and reliable optimization softwares, resulting in manual and time consuming evaluations that unfortunately leave room for mistakes. This is a wide area to research which is not easy with minimum of resources dedicated for this project. Due to time constraints and other constraints due to company considerations, the purpose of this project has been to assess the needs and requirements to obtain a more optimal air-traffic planning process for the Planning department. Among these needs defining the problem areas of operations today and assessing where support is needed, whether this is with new software or by some other measure, were looked at. Clarity into this matter will be provided in the oncoming sections about the planning and systems processes. But firstly a more detailed background will be provided in the next section.

1.3.1 Background

The main program used throughout the TUI group is a financial database called Airpas. Airpas is a German developed and based program that handles on a daily basis the financial information of the flights (e.g. fuel costs, landing taxes, handling costs. Airpas is part of a larger group of programs all directly connected to a larger database called IDPS, which provides airline operations services. These programs all communicate internally with each other, which can create an interconnectivity problem when external (third party software) programs are introduced. The full extent of this will be further explained in the systems section 3. It might seem redundant to introduce an external element into a system that is already connected with all programs a charter company can possibly have use for, but this assumption would have to take into account that all programs work satisfactorily. Opinions range wide in concerning this as the views of the users can differ from the developers in terms of the optimality of their product. But it is the view of this project that a perfect trade off is rarely the case, and that optimum performance is not met at TUIfly Nordic. Furthermore, if a system does not work satisfactorily why not just stop using it? The answer to this question is both easy and yet hard to obtain. It has been mentioned that airline companies are well established in their ways. This can be interpreted as an unwillingness to change old ways of thinking, however although this statement certainly holds a partial truth, it does not fully address the issue. 1.3. PURPOSE AND PROBLEM DESCRIPTION 5

Behind planning air traffic rotations lies long experience on behalf of the people laying the traffic program and an absence in computer assistance. The easy answer to the previous question is that a company will be unwilling to disregard an investment. For instance, researching and introducing a program that performs a requested task is a long and most often expensive process. Several alternatives have to be explored with regards to constraints that are presented by the in- house systems, thus resulting in a cluster of operational software that together operate far from optimality. The difficulties are twofold, one is that because of several software restrictions creating multiple scenarios for a traffic plan is virtually impossible. The main reason for this connected to the absence of computer assistance as scenarios have to be drawn up by hand and incorporating all necessary inputs for a feasible air traffic scenario is difficult. Despite of this reason, scenarios are created and continuously evaluated by hand taking account a medley of inputs ranging from crew resources to maintenance. Although not highly detailed, these handmade scenarios provide a base on which the traffic program will later be built upon. This leads to the second cause of concern. The second reason is that the schedule building software currently in use does not allow for parameter variability nor does it produce reports needed to evaluate or compare planning parameters. These parameters,(described in Chapter 2) provide the necessary information needed to decide upon an optimal traffic plan. The lack of software causes a domino effect throughout the planning process. Starting with the fact that laying down a complete traffic program is a very time consuming process. Bear in mind that only one schedule is being created at todays pace, several handmade scenarios are created and re-created, evaluated and discussed, where the outcome is one flyable and rather satisfactory schedule. Furthermore, there is no delimitation that marks the completion of the traffic schedule. Therefore, handing over of the traffic schedule for construction of rotations for flight deck and cabin crew, a process called crew pairing, takes place at a rather late stage. An assortment of changes, based often on requests from the Tour Operator, is being made at all times before, up until and during the execution of the schedule. Of course the traffic program has to be flexible so that at any stage if a need for change should arise it can always be solved or accommodated for (as long as it yields a benefit), but today’s methods do not allow for an easy and systematical evaluation of the impacts of a certain alteration. A bottleneck problem is therefore created because of the lack of quick handling and evaluation tools.

1.3.2 Limitations and demands

There are limiting factors, and several requirements when it comes to introducing a new system into an already complicated compilation of existing systems. Difﬁculties include communication with existing systems without causing compatibility problems, training of personal to operate the new software and budget. 1.4. TUI AG GROUP 6

Incorporating a system can have widespread effects through several departments of a company. The influences vary in complexity, ranging from amount of personal that is affected by the change to the amount of training required to operate the new system. Level of training depends on the number of personnel which will be directly, and perhaps also indirectly, involved with usage of the program, therefore the program should preferably be user friendly. Commonly, there are several budgetary limitations to introducing a new system. Depending on the extent of usability derived from a multi applicational program (air-traffic scheduling, crew scheduling), the budget allowed for implementation might vary. For instance, if the initiation, licensing fees and training costs are sufficiently small then the cost might be abided by a depart- mental budget. If the costs are larger than requisite budget then the purchase might need Board approval. Limitations of economic nature is one of greatest importance when analyzing potential software, as costs have to be balanced with potential savings. Whether the costs of introducing a new software are large or small, the return on this investment is regarded to be more important in the long run above the break-even point. Future profitability and an improvement is considered and it is assessed whether it is a worthy investment or not.

1.4 TUIAGGroup

TUI AG (German: Touristik Union International) was created in 1968, and back then TUI was an association of medium sized travel and tourism companies. During the nineties the company rein- vented itself to expand from tourism to incorporate shipping and logistics, by acquiring the mining industry Preussag AG and transportation company Hapag-Lloyd AG groups, in 1998. Hapag-Lloyd AG later Hapag Touristik Union (HTU) was renamed and became TUI Group in 2000, which operated as the soul subsidiary of Preussag AG. During the early twenty-first century, the company reorganized by selling off many of its industrial branches and purchasing several major travel and transportation firms, and in 2002 the company was renamed from Preussag AG to TUI AG. Today TUI AG is one of the worlds largest tourist firms with interests across Europe, owning travel agencies, hotels, airlines, cruise ships and retail stores. Subsidiaries include TUI AG Air- lines, the largest holiday fleet in Europe. Its common brand TUIfly encompasses 7 airlines, among those TUIfly Nordic which will discussed in the next section (1.4.1).

1.4.1 TUIﬂy Nordic

TUIﬂy Nordic is a charter airline based in Sweden, operating holiday ﬂights from airports in Swe- den, Denmark, Norway and Finland. The airline originates from Transwede Airways AB, an airline founded in 1985 by Thomas Johansson. The charter division was acquired by Swedish tour operator Fritidsresor in 1996 and renamed Blue Scandinavia. Britannia Airways took control of the company when Fritidsresor was acquired by Thomson (a UK based travel operator part of 1.4. TUI AG GROUP 7

the larger International Thomson Organization of Canadian until 2000) in 1998. The airline was renamed Britannia AB and later Britannia Nordic.

Figure 1.1 The TUI smiley, appears in all airline logos. Image courtesy of Google.

Preussag AG (later TUI AG) acquired the Thomson group in 2000. Because of a new marketing strategy put forward by the TUI Group the subsidiary airlines were to add a “-fly” suffix to their company name. Due to this strategy in November 2005, the airline was rebranded as Thomsonfly and in May 2006 it became TUIfly Nordic. The TUI smiley, appearing on all airline and Tour Operator logos, is illustrated in figure 1.1.

Aircraft In fleet Seats Notes Boeing 737-800 5 189 All fitted with winglets. Boeing 757-200 3 (1 leased) 235 All fitted with winglets. Boeing 767-300ER 3 (2 leased) 291 (328 during summer) All fitted with winglets. Boeing 747-400 (1 leased) 582 All fitted with winglets. Total 11 Last updated: August 30, 2011

Table 1.1 Current ﬂeet at TUIﬂy Nordic.

TUIfly Nordic fleet can be seen in table 1.1, currently the average age of the fleet is 13 years. During the winter of 2010, TUIfly Nordic leased one Boeing 757-200 and two Boeing 767-300ER aircraft from Thomsonfly for various charter routes. One Boeing 747-400 is leased from Corsair for long haul flights during the winter season 2010/2011. A note for further reading, there is a distinction made between TUI Nordic and TUIfly Nordic. While TUI Nordic represents the entire Scandinavian department of the TUI Group AG (that in- cludes all Tour Operators and the airline), TUIfly Nordic is the airline, and is also sometimes referred to as BLX the ICAO code (International Civil Aviation Organization).

1.4.2 Tour operator

The Tour Operator for TUIfly Nordic are Fritidsresor in Sweden, Finnmatkat in Finland and Star Tour in both Denmark and Norway. The Tour operator, referred to as T/O handles flight planning (popular destinations for the coming year, hotel capacities, amount of beds available for each destination) for TUIfly Nordic and other contracted airlines. The basic flight plan is established using historical data, based on sales statistics. The process that follows takes into account customer 1.5. COST STRUCTURE FOR TUIFLY NORDIC 8

wishes for extended travel opportunities to several locations. A dialog is maintained between the T/O and travel partners about market changes, so that shifts of the internal ﬂeet are for instance made to adapt to increases by redirecting ﬂights from where demand is lower.

1.5 Cost structure for TUIﬂy Nordic

To understand where the stakes are when it comes to improving traffic planning, the cost structure has to be studied. The division of costs for both long and short haul flights can be observed in figure 1.2. The corresponding values are taken from 2011/2012 winter season pricing (add reference here).

Figure 1.2 Cost structure comparison between short and long haul ﬂights. Illustration: Miriam Danielsson at TUIﬂy Nordic, reformatted by Mercedes Inal.

It can be seen that fuel and crew related costs are the two main components of the cost structure, followed by outgoings such as leasing of aircraft and maintenance. It can be noted that the distributional variations in costs between long and short haul are minor. Cost concerning naviga- tion, which are determined by the airspace flown over and distance, differ mostly for short haul flights compared to long haul. Onboard sales are incomes and thus marked negative (these show an increase on the short haul as well). Maintenance costs don’t vary significantly, but the small difference can be explained by regular short haul flights gathering more flight legs (i.e. Stockholm to Las Palmas is considered one leg, the return trip is also one leg, the combination is a rotation) and block hours, thus demand- ing more frequent maintenance checks (this will be explained further in Chapter 2, section 2.6.1 Maintenance). As mentioned previously fuel and crew related costs dominate the budget, the magnitude of these are predominantly unavoidable. Fuel unit costs are to an extent fixed or more precisely 1.5. COST STRUCTURE FOR TUIFLY NORDIC 9

regulated by long and short term agreements with the provider. Crew costs however depend on several factors, some of which are direct (DOC-Direct operational costs) and others which are not directly linked to the operations features and are thus said to be indirect (IOC-Indirect operational costs). More on this will follow in the pricing section. When consideration is given to several parameters at the beginning of the traffic planning process, there might be a considerable increase to gain economically. Planning for several scenarios, and being able to review the economical differences that can occur, can reveal at an early stage where costs become unmanageable. This might also show where revenue can be increased by producing more efficient rotations, should the schedule allow for feasible connections. Minor improvement in the traffic schedule can lead to significant crew cost reductions. Crew costs can be measured by so called production days, which simply implies days where production is carried out (this is also an estimate for crew supply and demands, for further reading section 2.8). Optimization of the air-traffic schedule in combination with crew scheduling can result in reductions of production days where possible, the outcome being savings in superfluous costs caused by undesirable scheduled rotations. This can be achieved partly by automatizing some of the manual labour that is the process today and by creating a more efficient evaluation process.

Departure Arrival ARN Time HKT Time ARN Time HKT Time Flight leg Pilots Cabin Rest days 15.30 21.30 02.30 08.30 ARN-HKT 2 8 3 02.30 08.30 14.30 20.30 HKT-ARN 2 8 3 19.30 06.30 06.30 12.30 ARN-HKT 3 8 3 08.30 14.30 20.30 02.30 HKT-ARN 2 8 3

Table 1.2 Example of rotations with various crew demands.

Table 1.2 shows an example of arrival and departure times from Arlanda, Sweden (ARN) to Phuket, Thailand (HKT). Since aircraft are not grounded for duration of the required rest period for the crew, a crew is assumed in place to cover the return journey in this example (this is most often the case, but deviations occur and flying in crew for the return journey is needed). There are several limitations for flight time, for instance depending on departure time there are rules that dictate the amount of available pilots for one flight. In the case of the example in table 1.2, three pilots are required for departures after 5pm, for the long haul flight. There are situations when extra production days and thus costs are generated, due to certain required rules and regulations, because of the layout of the air-traffic schedule. 2

Planning process

There are several steps over which the air traffic schedule is planned and brought into production. An overview of this process, as it is carried out today at the main office of TUIfly Nordic, can be seen in figure 2.1. This process starts for each season approximately a year and a half previous to carry out date and carries on until execution of the traffic program (without necessarily involving all of the functions, each seen in the flow chart). Several seasons are therefore constructed simul- taneously resulting in an overlapping process. The focus of this project will be on how to optimize long term traffic planning and the factors that contribute to its taking shape.

Figure 2.1 Current planning process at TUIﬂy Nordic head ofﬁce. Illustration: Mercedes Inal.

Figure 2.1 shows the trafﬁc schedule for a season, taking shape from an early on and continu- 10 2.1. TIMEFRAME AND INVOLVED PARTIES 11

ous ﬂeet planning stage, with constant interactions and assessments from resource planning and business control. This is followed by the planning of crew rotation schedules and pricing of the seasons schedule, which is presented to the Tour Operator for decision and sales. How these stages are implemented and inﬂuenced will be discussed in the coming sections, starting with a process timeframe and an overview of all involved parties.

2.1 Timeframe and involved parties

The timeframe for a typical winter season planning process can be seen in figure 2.2. The complexities of planning an operational air-traffic program can be assessed from this flowchart. On top of the input variables that directly affect scheduling, which will be discussed in the long and medium term traffic planning section, there are decision factors along the way influenced by the several departments that provide their own input into the layout of the traffic plan. There is a constant process of analysis, evaluation, change and then re-evaluation taking place, which is mainly market driven.

Figure 2.2 Planning process timeline for a typical winter season. This shows at which point each department gets involved throughout the creation of the trafﬁc schedule to its production. Illustration: Alexander Huber.

As seen in figure 2.2, the process from development to production is covered in five sequential 2.2. LONG TERM FLEET PLAN 12 steps. A concept program is prepared by the T/O for the coming season and uploaded into the flight database, FDB. The flight database is an internal system used through out TUIfly Nordic, it contains past statistics and capacities for available departure and travel destinations. This is a historically set program, operating with constantly updated information. Information such as allotments, airlines (external capacity), shared fleets and cost reference data that supplies standard costs to the customer. The flight database also relays information about the seat sales (volume) which are important, for instance when ordering hotel rooms. Before this concept program there is a long term fleet planning stage, which will be described along with each step in the next few sections.

2.2 Long term ﬂeet plan

The ﬂeet planning stage is a continuous long term process partially due to the fact that it is bound to the leasing contracts of the airplane ﬂeet or delivery direct from the factory. These contracts are negotiated for over a long period of time, that stretch from anywhere between 7 to 12 years. The

Figure 2.3 Airplanes in production during 2010 and 2011. Illustration: Mercedes Inal. main purpose of fleet planning is to provide the necessary fleet capacity in order to accommodate the tour operators needs throughout the year. TUIfly Nordic provides an airplane fleet that covers up to 60% of what the tour operator sells. The rest is hired from external companies to supply demand. 2.2. LONG TERM FLEET PLAN 13

The main reason for this is because expansion with profit in the airline industry is hard to achieve, having a large airplane fleet can be unsustainable should the economic climate prove harsh. This is of course due to the fact that an aircraft is by no means a small expense and furthermore a grounded aircraft is a colossal expense, resulting in a massive loss of revenue. Since this is an unattractive option, long term planning and external seasonal capacity purchasing become necessary to ensure that all aircraft at hand are used with maximum efficiency. This process holds a lot of interest, since it affects all major areas of air-traffic planning. To carry out a need presented by the tour operator a certain number of aircraft are required. A decision is made about either an expansion or a reduction of the fleet is required, varying from one season to another, this in turn decides the layout of the traffic program over the aircraft rotations. Figure 2.3 shows the fleet as it has been throughout year 2010 and how it will be in the year 2011. To generate the seat production seen in the graph, data on amount of legs flown per aircraft (see table 1, Appendix A) is used, multiplied by seat numbers given in table 1.1, calculations also assume that the aircraft fly with maximum take-off weight (MTOW, maximum fuel capacity, passengers and cargo). Flight legs are derived from information sheets acquired from a given air-traffic schedule.

Figure 2.4 Fleet plan process. Illustration: Mercedes Inal following set up by Per Sylvan at TUIﬂy Nordic.

Fleet and capacity planning is a structured process; figure 2.4 shows how the fleet planning process is carried out currently. Following the example above when a need is presented, a principal decision is made by the Tour Operator, TUIfly Nordic (BLX in the flowchart) and the airline management. This decision is first discussed in TUIgether, a group consisting of BLX key functions and the T/O. Decisions here are approved or declined by the management and those in charge of product and 2.3. PRODUCT AND CONTENT 14 content. An executive group (LTM) will review the decision proposed and if favorable, negotiations with unions will take place before a final decision is reached and an investment committee based in the United Kingdom is involved. If and when the investment committee sign their approval, the matter reached the TUI Travel PLC board (PLC is an abbreviation for public limited company) and the matter is processed further. Aircraft sourcing is a long process going back and forth several times before reaching a decision appropriate for all concerns. In the mean time negotiations with a lessor market, or the TUI Travel order pool concerning which options to be chosen is undergone, and provided that interest still resides with the attributors throughout the internal process, a letter of intent is drawn up and presented to the TUI Travel PLC board, which will, if decided favorable, lead to a contract. Aircraft are also redistributed between the airlines in the TUI Group, the process of acquiring an aircraft this way is similar to the process described above.

2.3 Product and content

TUI Nordic is a charter airline, and like most charter airlines their holidays contain a lot more planned activities compared to regular ﬂights. The planning of all activities that make up the entire holiday experience (e.g. the holiday itself, hotels, activities at resorts, pick ups to and from destination etc.) is done by the product and content group.

2.4 TUIgether

TUIgether is an internal group within TUI Nordic, with representatives from all areas of TUI Nordic including the Tour Operator and the airline TUIﬂy Nordic, gathering on a weekly basis. The discussions that go on here are changes to the long and short term air-trafﬁc program and changes of commercial nature, for example whether or not incorporating a new destination should be considered.

2.5 Planning and production

Planning and production refers to a meeting held every other week, involving those that are affected by the flight schedule on an operative basis. The main purpose for the group is of an evaluational and informative nature. This meeting is held for the operative side, handling ground operations, cabin crew and maintenance. The group discusses the air-traffic program as it is built up and find solutions to problems that arise. For example, if the TUIgether group proposes a new destination 2.6. LONG AND MEDIUM TERM TRAFFIC PLANNING 15 or a change to the program, will there be enough time to reach maintenance requirements in the air- traffic schedule? This is presented to the group and possible ways of going about the issue will be regarded and if the result of this groups evaluation is favorable then the maintenance requirement will be accommodated for.

2.6 Long and medium term trafﬁc planning

When the long term fleet plan has been decided for a given season, the long term traffic program can start taking shape. With requests from fleet planning and the tour operator, expansion of the airplane fleet and additions to the destinations list, the traffic program is planned in several stages. It starts with a rough sketch that outlines several possibilities of managing the air traffic program within legal and operational boundaries as well as cost efficiency. This is a collaboration, and in large parts a compromise, between several departments whose respective inputs into the traffic planning result in a flyable air-traffic program for the season. These inputs are both external and internal and can be seen in figure 2.5.

Figure 2.5 Various inputs that determine the optimality of a trafﬁc program. Illustration: Mer- cedes Inal.

The planning process begins approximately 18 months previous to the season of interest e.g. summer or winter. Several simple scenarios are put together and evaluated without computer assistance. A scenario will consist of approximately two weeks, since it is both reasonably short period to overlook and also because two weeks cover the regular amount of vacation time chosen by travelers. The choice of period for a scenario is based on the seasonal peak, this is necessary since the work capacity needed is planned to accommodate for this period. For example this peek period can be seen in ﬁgure 2.3, between february and april 2011, where there are 11 aircraft in production. Therefore the resources available have to satisfy the need for this period. A scenario takes into account inputs from all involved parties. These are discussed and rear- ranged as to grant each input the least amount of inconvenience. This will allow for an opera- 2.6. LONG AND MEDIUM TERM TRAFFIC PLANNING 16

tionally stable traffic program. What is difficult at the long term planning stage is the amount of inputs that need to be taken into account. Combining all of the inputs into an optimal traffic program that will provide operational stability and flexibility is challenging. These parameters have a tendency to clash with one-another whenever change is required. Therefore, representatives from all parts associated with these inputs come together at TUIgether (section 2.4) meetings to discuss the traffic program. Air-traffic planning is done by few people (as such it is a group effort) where inputs are gathered from all involved parties, however the actual scheduling is done by less than a handful of people. At TUIfly Nordic these people (from here on referred to as Planners) work with programs that are not designed to optimize several inputs into the best flyable schedule. Therefore laying the body of the air-traffic schedule falls on the Planners, with their combined years of experience and abilities to see scheduling possibilities, in order to obtain a feasible schedule. Creating a flyable schedule does not only cover the rotations of the fleet, there are several other factors, the most important of which are crew rotations (also called pairings) and maintenance opportunities. Finding a middle ground for these factors makes an air-traffic program viable. If crew pairings do not hold, then the fleet rotations would not hold, for the simple reason that if there is nobody to operate the aircraft then it can not possibly fly. This means that while planning the aircraft rotations, the Planners have to take into account the rules and limitations for crew flight and duty times. While crew conditions must be taken into account in order to obtain a flyable schedule, maintenance scheduling is one of the most common optimization parameters that is sought to combine with air-traffic rotations. The importance of maintenance will be covered in the next section. Briefly however, while planning aircraft rotations it is very important to take into account the maintenance needed for each aircraft individual (maintenance is needed between flights, short checks as well as long checks) and therefore plan arrival and take off times accordingly. Longer maintenance checks need to be coordinated with the technicians in charge of maintenance in order to plan an optimal schedule, swap opportunities need to be planned in for the aircraft so each and every aircraft in the fleet rotate to home maintenance base (hangar). There have been several studies to scheduling aircraft rotations and many combine maintenance and crew pairings planning in some form into the basic fleet planning model to obtain a better overall optimization. L. W. Clarke et al [7] try to provide modeling devices to achieve an incorporation of maintenance constraints and crew scheduling into the fleet assignment model (FAM for short, is a model that optimizes the amount of aircraft in a fleet to a network of flights under several constraints, see Chapter 4) while retaining its solvability.

2.6.1 Maintenance

Safety is a crucial issue in the airline service, the objective being to keep the aircraft ﬂying for as much of its service time as possible in order to accumulate as much revenue as possible without 2.6. LONG AND MEDIUM TERM TRAFFIC PLANNING 17

compromising customer safety. With small margins and huge fixed costs (e.g. an aircraft) any incident would jeopardize the survival of airlines, especially the small ones. Maintenance is therefore an important parameter, aircrafts are heavy duty machines that do not regularly come of the construction line, since they are often leased over a period of time they are used and need regular service checks in order to fulfill safety requirements. Each aircraft model has its own maintenance program and its own range of intervals where this must be performed. These intervals can range from anywhere between five to seven weeks, and this only takes account the major maintenance checks. It is unsustainable to have the checks so far apart from both maintenance and traffic planning perspective. Maintenance checks are therefore planned as loops that are woven into the traffic schedule. Maintenance duty intervals depend on flight hours, cycles or calendar time, or what is more common, a combination of these. Since these parameters depend on the flight schedule and use of aircraft, the general rule is that maintenance need is decided by which ever limit for these parameters is reached first. This is clearly stated in the manuals that accompany each aircraft model. Information regarding maintenance was gathered by consulting the maintenance technicians and also via access to maintenance manuals provided by Boeing [8] and internal company guidebooks for maintenance handling [9]. The maintenance team at TUIfly Nordic performs mainly two types of checks, A and C, these will be described shortly. The A- and C-checks have a recommended interval, within which the check is meant to be carried out. The intervals of these checks might seem large and therefore set fairly far apart, so naturally several smaller checks are performed along the way, for instance, pre-flight inspections, 48 hour service checks and 100 hour service checks.

A-checks

A-checks are performed, depending on aircraft model, every 500-750 flight hours. These checks are specified as a routine check to ascertain the general condition and serviceability of the airplane. It is essentially a visual inspection of the exterior structure and flight control surfaces and some internal areas. Therefore the requirements of this check include that flaps and spoilers being extended and cargo, landing gear, passenger, avionics and engine cowl doors be opened. Tasks needed to be performed can be counted to the hundreds, ranging from changing filters and lubricating bolts to operational and function checks. A-checks are divided into groups, all of which are performed at defined intervals. The maintenance team for TUIfly Nordic solves these with regular numeration of A-checks, (an example will be given. For the Boeing 767-300 an 1A-check is performed every 750 flight hours, when the aircraft has collected twice the amount of flight hours a 2A check will be conducted, and so on. This has a more specific interval described in Appendix A.) 2.6. LONG AND MEDIUM TERM TRAFFIC PLANNING 18

C-checks

C-checks are performed, also depending on aircraft model approximately every 6000 ﬂight hours, there are therefore extensive and long checks performed over several weeks. The check consists of a complete and detailed area check of all interior/exterior zones of the airplane including systems, installations, visible adjacent structure and performance of special service items. The full extent of the C-check is to be performed without disturbing system integrity, removal of components or insulation unless removal is performed with planned interval.

It is not hard to understand why planning and performing regular maintenance checks are of such importance. It is however harder to grasp the complexity of planning each maintenance stop optimally without disturbing a well planned flight schedule. Its easier to state that a traffic plan is not optimally planned without proper maintenance stops. Its therefore a crucial parameter to take into account from the very beginning Airlines try to maintain a homogenous airplane fleet choosing to have several planes of the same line of model and preferably also the same type. The type of model decides seat numbers and arrangement of interior. The reason for this is so that the aircraft are easily interchangeable should the need arise without impacting crew efficiency. Various aircraft types require different training needs for flight deck and cabin crew.

Swaps and tail assignment

The home maintenance base for TUIfly Nordic is in Stockholm, Arlanda, which means its aircraft have to circle home occasionally. To achieve this, aircraft swaps are planned at certain intervals and airports, allowing one aircraft to continue the flight leg and the other one to travel back to the home base for a planned service check. When and where to place a swap, just as the assignment of the individual aircraft to a specific flight segment, so called tail assignment, is decided by when maintenance is due for the aircraft. For TUIfly Nordic the accumulated flight hours far succeed the amount of cycles and/or calendar dates for which an aircraft maintenance check is needed. The maintenance team therefore decides the swaps and aircraft assignments based on this parameter. The maintenance team at TUIfly decides the tail assigning of the aircrafts, and relays it to Germany where the operations center is based. For the time being the operations team in Germany operates the program for tail assignment (Chapter 3 will elaborate further on why the tail assignment is not directly handled by the maintenance team). Although acquisition of the software is currently in progress. 2.6. LONG AND MEDIUM TERM TRAFFIC PLANNING 19

2.6.2 Slots and trafﬁc rights

One of the major factors when planning an air traffic schedule are slots. Slots are airport-based defined timeframes where landings and take-offs are permitted. These are related to the capacity of the airport, and thus for each airport certain amount of slots are negotiated for to enable arrival and departure for the holiday destinations as well as in Scandinavia at departure/arrival. Availability of slots depend on size and location of the airports, i.e. amount of gates, runways (the length of the runway can also be a factor, depending on size of aircraft and necessary minimum take-off distance) and transport facilities. Slots retain a high market value and are considered business assets. Arrival and departure times of a designated slot can have great impact on ticket sales. Clearly owning slots on popular times of the day will attract more travelers and thus produce more profit. Owning attractive slots also provides a tradable asset for the airline and a competitive advantage against competing airlines. Slots are also a high risk parameter, desired slots are something most airlines have to negotiate for. Slots are held by the “grandfather clause”, i.e. slots remain in the possession of an airline indefinitely provided annual usage. Slot conferences take place twice a year, one conference for each season. Slot conferences are held at a relatively late stage. By this time, the traffic schedule for the season in question has been, for most part, released, priced and open for ticket sales via the tour operator, therefore it implies a risk factor. While planning the traffic layout, historical data from earlier seasons play a large role in how to plan for the upcoming season. This data retains a record of all the slots that are opened for the airline at each destination while also providing a staring point on how to build the traffic program.

2.6.3 Resource planning

Parallel to the initiation of a new long term traffic plan, the resources needed for the season are calculated by the Planning control function. This a collaboration between those building the long term traffic schedule and crew rotations and those handling finance. Because of the mutable nature of the long haul flights, this process will at an early stage help build and reshape the traffic program by evaluating simple scenarios that are for the most part prepared initially on paper. Usually scenarios are built covering a two week period, this is a reasonable time period that takes into account all the aircraft rotations that occur before a period repeats itself. MS Excel based tools are available for evaluating whether or not a traffic program is viable. For instance, these tools can help decide whether a rotation on a flight leg is within legal parameters from a crew perspective, which is something that is discussed long before the schedule even reaches the stage where crew rotations are planned. Figure 2.6 depicts the step-by-step process describing the stages of resource dimensioning. It is easy to see that resource planning is influenced by several factors, some of which are directly 2.6. LONG AND MEDIUM TERM TRAFFIC PLANNING 20

Figure 2.6 Step-by-step flow chart for the resource calculations process. Illustration: Clement Berguerand. manageable and some of which can clearly vary, such as sick-leaves for crew though statistics can help for dimensioning. Dimensioning of standby crew is of great importance, since a sick crew member needs to be temporarily filled in for, meaning this will affect the available crew resources which will also affect costs. Figure 2.7 shows a timeline of the resource planning process for the flight deck (pilots). After the first stage of scenario building and rough resource calculating while planning the traffic schedule, the needs and requirements for a season are laid out and planned in two stages. In the first stage, available resources i.e. cabin crew and pilots, are evaluated in order to determine whether action should be taken (i.e. if reduction or recruitment is necessary) or not. Pilot bidding will take place at this stage, bidding concerning allocation of base, vacation days, according to seniority etc. Following this is recognizing training needs, whether pilots and cabin crew need to be trained for a new airplane model or if new requirements need to be taken into account. 2.7. HANDLING 21

The second stage is implementing the ﬁrst one in an optimal way, such as scheduling re-training into the transition periods (shoulder periods) between seasons where a decline in production occurs. As is shown in ﬁgure 2.3.

Figure 2.7 Example of a current timeline for ﬂight deck planning. Illustration: Clement Berguerand

There is a trade-off between robustness and flexibility when planning for an air-traffic schedule. It is preferable of course that both are obtained, but this is rarely the case. To allocate or distribute resources in an efficient way, it is necessary to have the air-traffic schedule ready and the crew rotation schedules planned. This estimates that take more information into account are possible, and thus a more flexible and robust schedule is reached.

2.7 Handling

All activities that occur on ground, that is to say activities that take place at the airport from the moment of touch down to take off, are gathered and categorized as handling. These activities include passenger check in, boarding at gate, luggage loading, refueling, deicing, cleaning and catering of the aircraft. Much more can be said about the logistics that go into planning ground handling, but this will not be covered here, though it is a dimensioning parameter. Speed and precision in the sequence of handling tasks performed is key to an efﬁcient and smooth rotation. Handling times need to be taken into account when planning aircraft rotations 2.8. CREW PAIRING 22

as the arrival and departure times at a destination (also called turn-around time) can’t be planned neither to far apart, because of loss in efficiency, nor to short, to allow that everything that needs to be performed on ground is done properly. Handling is rather specific for charter flights, compared to regular flights. While turn-around times for regular flights can range from 20 − 45 min, charter flights will demand a lot more, here the turn-around times, depending on aircraft type, begin at 70 min. This is mainly due to what is offered in terms of charter service (amount of seats offered, pre-packing of tax-free items etc.) and the amount of passengers to board. Large amounts of people produce more waste, and bring on more revenue as they make use of the tax-free havens offered on board flights, as such they are provided with pre-packs (mer- chandize, bought onboard or pre-ordered, that are distributed on the journey). Everything needs to be offloaded and fresh supplies need to be reloaded, which requires time. Cleaning and catering personal need time to prepare the aircraft for the next group of passengers on route to their holiday destinations. Charter airlines utilize the aircraft at their disposal "to the limit". Handling times are pushed to the minimum today, and it is ground handling that suffers when an air traffic schedule is interrupted by delays, some large enough to make that minimum turn-around time window look very small. The effects of this delay will undoubtedly push cleaning and crew to perform their duties in a much less time. Pushing tight time limits like this often results in the flight being delayed, while also creating a snowball effect of delays. The consequences are often dealt with very fast, but if a flight is sufficiently delayed, the need might arise for a substitution charter to be hired, resulting in high expenses, though the cheapest recovery alternative is always chosen. As such, the importance of handling in the long term traffic planning stage is crucial. Planning for the right turn around times at an early stage is complicated since it greatly impacts the efficiency and robustness of the flight schedule. (The ability to counteract an arising disturbance in the handling times could have a massive effect economically.) Today, TUIfly Nordic handles the ground work by hiring third party suppliers, meaning there is no in-house catering business or cleaning crew at each airport. It is common practice that work is contracted and dealt with remotely, where quality is determined from what is demanded in an agreement and the costs of such. Section 2.10 will explain the financial sides of the planning process and why some of these costs are fixed while others are variable.

2.8 Crew Pairing

Optimization of crew pairings is frequently covered in scientific literature, given that significant reductions can be generated solving the crew pairing problem optimally. Crew pairing costs are 2.8. CREW PAIRING 23 only exceeded by fixed aircraft costs and fuel costs. G. Desaulniers et al [2] state that “The magnitude of these savings for major airlines is exemplified by the fact that a one percent decrease in the total crew costs often amount to tens of million [sic] of dollars per year in additional profit”. This is statement is also true for smaller airlines but with a smaller magnitude in profits. G. Desaulniers et al describe an implementation of a new solution method for the crew pairing problem applied to data from Air France. This is an improved method from S. Lavoie et al [1] from 1988. When creating a feasible traffic schedule, crew rotation feasibility is essential. Since there are nearly endless constraints for how the crew are allowed to work, these constraints will undoubtedly oppose the ones set for the aircraft rotations. This means that to an extent when scheduling the aircraft rotations, the crew constraints must be met in order to achieve a flyable schedule. Accounting for crew limitations early on when planning aircraft rotations simplifies the work of creating feasible crew pairings when handed over to the pairing stage of the planning process (see figure 2.2 on page 11). Planning the crew rotations is a process that takes place along side the long term traffic planning process. The reason for this is that traffic planning today is at large ruled by experience rather than optimizing mathematics. The experience yielded by the planner can incorporate far more variables than what would be deemed necessary when only planning aircraft rotations. Figure 2.8 shows some of the input parameters that are taken into account when creating viable crew pairings. Most of these parameters are difficult to evaluate using a simple mathematical algorithm (although some attempts have been made and are covered in Chapter 4), they are also much for this reason hard to program into a good software product.

Figure 2.8 Parameters affecting in the crew pairing process. Illustration: Mercedes Inal.

When the long term trafﬁc plan is almost ready, it is handed over for crew pairing constructions. One of the key parameters in optimizing crew pairings is the number of production days produced by a rotation or a duty period. Production days are as it implies, work/duty days, days which 2.8. CREW PAIRING 24

generate expenses (as well as revenues). The optimization problem that is created is how to keep the production days to a minimum while maximizing profit. There are of course constraints to this statement. It is not always possible to minimize production days without suffering expenses. As will be discussed in the next section there are several legal obligations to the crew and the work limits set for them, for example after a long haul flight or a 12 hour duty period it is required that the crew have three consecutive nights of rest. This creates costs for the airline. But these three rest days are a legal requirement and are therefore seen as a required expense. The difficulty becomes how to create rotations that are always within the legal constraints when aircraft rotations are already heavily constrained by for instance defined arrival and departure times (slots). Returning to the example, when flights to a long haul destination, with many timezone crossings, is set at twice a week, certain gaps can occur. The crew coming with the flight, on the first slot of the week, can not fly the flight back home since they require a rest period, the duty period becoming too long. The solution is that the crew stays and switches with the next crew coming in with the second arrival of the week, and man the aircraft on its return journey, (implying that the return journey has to be planned three nights ahead). Best case scenario states that this example is possible without redundant costs. If the example is extended, the slots for this destination are set on a Monday and another one on a Friday, this creates a gap of four nights between flight in and flight out for the crew arriving at the destination on the Monday and three nights for the crew arriving at home on Tuesday. This means one extra night for which the crew must be accommodated for. This also results in an increase of cost for the additional production day. The most cost effective solution will be sought, it could mean flying the crew home passively and sending one back to the destination should it be necessary. The crew pairing process at TUIfly Nordic operates currently without an automated (in-build software) optimality function, thus the pairings are created using experience and patience. The system supports legality constraints warning for illegal pairings, but other than that the process is similar to the traffic planning. Figure 2.9 shows the pairing process, and the stages of negotiations along the way.

Figure 2.9 Pairing process. Illustration: Mercedes Inal.

Crew pairing scenarios lead back to the planning controller and the trafﬁc planners to evaluate 2.9. DUTY LIMITATIONS 25

the need for an increase or a decrease in resources, the need to coordinate training programs etc. and also to make changes in the trafﬁc program whenever possible to minimize production days without for instance compromising maintenance stops. When this is ﬁnalized and a scenario is decided upon, a full season crew pairing schedule is made, which is on a regular basis validated and discussed with unions (and every 28 days forwarded to the crew rostering (individual crew assignment according to pairings) team for scheduling).

2.9 Duty limitations

For safety reasons, the ICAO (International Civil Aviation Organization) restricts flight and duty time of the crew and requires crew rest periods. Additions to these rules are given by the European Aviation Safety Agency (EU OPS 1 SUBPART Q) and at a national level for Swedish airlines (TUIfly Nordic) from the Swedish Civil Aviations Administration (since TUIfly Nordic owns a swedish AOC Aircraft Operators Certificate. The hierarchy can be seen in figure 2.10.

Figure 2.10 Rules and regulations hierarchy. Illustration: Mercedes Inal.

Breaking international and/or national rules could result in the loss of the airlines license, and are therefore reinforced by internal policies and guidelines when planning an air-trafﬁc schedule. Additions to these are regulations and contracts/agreements regarding crew operations, limits for their work periods, set by both the airline and the unions for both pilots and cabin crew. 2.10. PRICING 26

Flight Time Limitations (FTL), are important parameters for trafﬁc planning, both of which need to be monitored, for evaluative purposes and for regulation as some limits leave room for interpretation. For example limitations are yearly, monthly and weekly ﬂight time limits, guaranteed minimum rest to prevent daily and cumulative fatigue.

2.10 Pricing

Individual costs concerning individual inputs and sometimes combinations of these are evaluated frequently throughout the long term traffic planning process. These individual costs refer to maintenance expenses, airplane leases, catering and crew expenses among many other. Before the schedule is released it undergoes a pricing process. Pricing is a financial evaluation process that summarizes all expenses for a seasonal air-traffic schedule and for charter airlines is a process ending when the price-list is sent to the T/O. Although a flight schedule is expensive it will still be flown if the schedule is regarded to be the best outcome for its season, pricing is important to the Planning department as it must cover the total costs for each flight leg. The cost structure section 1.5, explained earlier that the three largest expenses are fuel, crew and maintenance. The pricing process can be separated into three parts respectively, that cover these types of expenses. The terminology used will be explained to start with. There are two types of expenses to be dealt with in the financial realm and these are Direct Operational Costs, so called DOC’s, and Indirect Operational Costs, so called IOC’s. Direct operational costs can be considered as costs connected directly to a flight segment, e.g. costs by annual agreements; fuel, handling, landing, overflights, passenger taxes, maintenance, catering, crew DOC’s (overnight stays at hotels, allowances) other DOC’s (e.g. deicing for aircrafts) and positioning flights. Indirect operational costs are expenses that are not directly connected with the actual flight of one aircraft, they exist independent of flights flown, e.g. salaries for flight deck and cabin crew, salaries for other operational staff, leasing costs and including insurance fees.

Business control

Crew costs occupy a bit more than a fourth of the total expenses, as seen earlier in figure 1.2, and thus are budgeted as a separate entity that is later incorporated into the total pricing. Creating a pricing for crew expenses is a collaboration between the resource planner and the business controller managing crew outgoings, such as salaries, hotel accommodations and allowances. Crew costs are also divided into IOC’s and DOC’s. These costs are calculated from the from the amount of resources needed according to the new pairings build from a planned season flight schedule. If resources available fall short of demand then hiring of additional crew will 2.10. PRICING 27 be needed, also if resources exceed demand then restructuring is needed and available crew might be reduced. Demand will also increase or decrease according to how many aircraft are available in the fleet. Crew IOC’s involve various costs, e.g. overtime, extra crew need to be able to manage absences and sick leave, hiring extra staff, compensation etc. These costs are based on estimations from data gathered each previous year and then added with the crew DOC’s to the pricing drawn up to the Tour Operator. Pilot and cabin crew salaries are highly regulated expenses, by among all, seniority and union agreements (IOC’s). Evaluation of hotel expenses and allowances from previous seasons as well as detailed pairings analysis allow for an estimate of expenses for the coming season (DOC’s). Estimations such as these, make it possible to account for increases (or decreases) occurring for a season by for instance determining where demands for more overnight stays are and estimating early on where pay benefits will be available for the crew. Crew expenses are drawn up to cover the two weeks where a seasonal peak occur, and then made to cover the entire season. Handling finances this way assures that the height of the season, where more resources are demanded, is accounted for and therefore financial coverage exists throughout the entire season. Crew costs are followed closely as they are the second largest expense for an airline and as such the obvious parameter to optimize in order to yield a more favorable economic outcome i.e. minimization of overall crew costs and therefor the total costs. 3

System processes

An overview of the systems that are currently integrated to the planning process at TUIfly Nordic can be seen in figure 3.1. This image illustrates which of the systems belong in the same group and which of those that are operated from the main office in Stockholm.

Figure 3.1 Systems communications and software in use for the planning process. Programs currently in use at TUIﬂy Nordic are highlighted with a darker colour. Illustration: Mercedes Inal.

These systems have different purposes and operate with different levels of complexity, which will be described in the following sections, starting with a systems description, which will explain the interconnectivity of the internal systems available to the Planning department at TUIfly Nordic. There were some difficulties finding enough information about each system, and information concerning this chapter is mostly based on own observations. For this reason information regarding 28 3.1. SYSTEMS DESCRIPTION 29

the following systems, IDPS and RM5, are limited to the views and concerns of TUIﬂy Nordic.

3.1 Systems description

There are currently two systems in use at TUIfly Nordic (that are directly connected to the Planning department), these are the IDPS systems, which will be described in section 3.1.1 and RM5 which will be described in 3.1.2. Both of these systems are designed to manage airline operations and consist of a modular interface (or applications), each module having a different purpose. The basic set up for both systems is a main database connected to all modules, with a live set up where the live program progresses. It is assumed that each system, IDPS (system 1 in figure 3.2) and RM5 (system 2 in figure 3.2), runs optimally when all of their separate modules are operated following the order for which they are designed (e.g. aircraft rotations module → crew paring module → rostering module → live module). Connecting each module in this way would eliminate interconnectivity issues and supposedly create a straightforward workflow. This is a set up not entirely followed at TUIfly Nordic. The two main reason for this are, first off, all modules available by the different systems are not necessary nor do they entirely address the needs of the air-traffic Planners or the company. Secondly, since TUIfly Nordic has used RM4 previously (updated later to RM5, this will be explained in section 3.1.2) and decided in favor of keeping the system after the company merger and running it along side the systems that were introduced by the TUI AG Group.

Figure 3.2 Systems interconnectivity and modules currently in use at TUIﬂy Nordic. Illustration: Marcus Karlsson, reformatted by Mercedes Inal.

The TUI AG Group decided that its in-house developed systems were to be used by all of their af- ﬁliate airlines. The binding module throughout the company is Opscon, a live system and Airpas, 3.1. SYSTEMS DESCRIPTION 30

a financial system. TUIfly Nordic has thus adopted these IDPS modules because of this decision, and added Arsis, an air-traffic scheduling module, because it acts like an intermediate between Opscon and Airpas. This has resulted in the presence of two systems currently in use at TUIfly Nordic. These systems do function well separately but running them together creates interconnectivity issues. There are however also exceptions to this in the TUI Group, for instance Corsairfly in France has an operations system of its own. At this stage it is important to mention that all of the modules are interconnected in different ways, basically they can read information from one-another. The set up of these systems are as follows, and can be seen in figure 3.2. The IDPS systems are concentrated to the aircraft rotations scheduling and the financial department, while the RM5 system is used to handle the construction and follow up of crew pairings as well as rostering (and day-to-day fleet follow ups via Opscon). The explanation to this will be given in the individual sections that come next. As was mentioned previously, it is assumed that using a single systems modules in the chain designed by the developer, is preferable rather than having several systems, since interconnectivity issues might arise. The reason why TUIfly Nordic is using a different set of system for crew related operations is due to research made within the company, finding the needs set up by the planning department not met by the IDPS module covering similar operations.

3.1.1 IDPS

The Integrated Disposition Planning and Statistics System, or simply IDPS, is an IT software technology which provides a broad selection of airline operations and airline management applications. IDPS is a software in-house developed by TUI Airline Management and is currently marketed by

Figure 3.3 IDPS systems hierarchy. Illustration: Mercedes Inal

GO Center, short for Group Operation Centre. GO Center is the core provider of ﬂight operations information, analysis, dispatch and ﬂight support. 3.1. SYSTEMS DESCRIPTION 31

The function of IDPS is to manage airline operations efficiently, economically and above all as safely as possible. IDPS was developed in the seventies, the software consists of a core database (fast data storage and retrieval system). There is an integrated applications suit covering all kinds of tasks within the airline operations stream such as commercial flight planning, maintenance planning, tail number assignment, crew rostering, daily crewing, operations control and back office support. The modular set up can be seen in figure 3.3 (there are more modules available but these have not been incorporated because they lack of relevance to this project.) These modules will be further explained in the sections to come, for short the purpose of each module is as follows: – Arsis: air-traffic scheduling, – Opsman: crew pairings module thats not currently at use, – Opscon: live operations operated from Germany, – Airpas: financial handling and database, – Aeromap: tail assignment also not currently in use. Support packages and contracts are available in a wide variety ranging from in-house implementation to tailored arrangements adapted to the needs of the individual airline. IDPS target group is said to be small to medium size airlines. The IDPS systems used by TUIfly Nordic will be explained below starting with the air-traffic scheduling tool, Arsis.

Arsis

The main air traffic planning tool used is an aircraft rotations planning software called Arsis, see figure 3.4. Arsis like IDPS functions as a database, which means that the program is meant to organize, store and retrieve large amounts of data within its operational use. Arsis also has several inbuilt modules such as scenario and live (see figure 3.3), and these are the only two modules currently utilized by TUIfly Nordic. Arsis does not function as a database and has therefore no history retaining possibilities i.e. statistics, the modules utilized within the program do not communicate, meaning that a scenario created in the scenario-module can not be transferred to the live-module. This creates unnecessary workload as the planner has to manually rewrite the tested scenario into the live-module. The result is an inefficient work environment which can cause hang ups and delays throughout the planning process. The importance of this system is that it is connected to its partner programs Aeromap, which is used for maintenance purposes such as tail assignment, and Opscon, which handles the live feed of the ongoing traffic program. These two are currently operated from Germany. 3.1. SYSTEMS DESCRIPTION 32

Figure 3.4 Screenshot of Arsis interface.

There is an ongoing project that aims to bring the control of Aeromap in to the hands of the technicians and maintenance team in Arlanda, Stockholm since they hold ﬁrst hand knowledge of the airplanes. Negotiations are currently in progress.

Opscon

Figure 3.5 shows the Opscon interface. The schedule is updated by GO Center in Germany every three days, and is, from that point, on out of the Planners control. Changes that occur within these three days are relayed by the Planners over to GO Center who transfers them into Opscon. Even though Opscon is operated from GO Center in Germany, the live operations are closely monitored by TUIfly Nordic. The screenshot illustrates a colour system in Opscon that simplifies operational follow-up (dull-green for flights flown on time and yellow for flights that are delayed), 3.1. SYSTEMS DESCRIPTION 33

Figure 3.5 Screenshot of Opscon interface. there is also a marked timeline (vertical green line) showing the momentary progress of the schedule, see illustration for more colour explanations. The purpose of Opscon, besides controlling the live progress of the air-trafﬁc, is to deliver journey logs, ACARS (Aircraft Communications Ad- dressing and Reporting System) and the movements of the aircraft to Airpas on an over night basis, which later sorts this information.

Airpas

The main function of the Airpas system is to calculate direct operational costs and revenues, handling the distribution of indirect costs and invoice checking, see figure 3.6. Airpas is a powerful financial tool that handles most business units that are involved in the airline sector, such as ground handling, fuel expenditure, catering/inflight sales, crew and maintenance (standard as well as indirect costs) and also administration, insurances etc. 3.1. SYSTEMS DESCRIPTION 34

Figure 3.6 Screenshot of Airpas interface.

Airpas allows for calculations and produces scenarios (for signature and pricing purposes), budget, planned and actual ﬁgures. Airpas is therefore also a good report tool, that is able to retain historical data to generate forecasts for future estimates, which are very useful for the pricing process. The data is received and updated over night as ﬂights are processed by Opscon. The program is compatible with Excel, and is therefore able to produce large report sheets that are easy to rework.

3.1.2 Sabre Rocade Suite

Sabre®Rocade airline operations suite: this system is internally referred to as RM5 (short for Resource Manager version 5). RM5 is developed by Sabre: Airline solutions a company that professionalizes in airline operations. RM5 is a multi purpose system mainly used for scheduling, crew pairing and crew follow up at TUIﬂy Nordic. 3.1. SYSTEMS DESCRIPTION 35

Positive aspects of RM5 are the report generating abilities that the system and modules offer, and some in-built optimization modules (all of which don’t always work perfectly but it is a work in progress and the RM5 developers are trying to improve these modules to better suit the wishes of their clients). RM5 is a module based system just as IDPS, see ﬁgure 3.7.

Figure 3.7 RM5 systems module overview and order of connectivity. Illustration: Mercedes Inal.

This image illustrates the order of the systems according to how they are designed to operate (i.e. ARP → PAR → ASG → DCO ⇋ ROC). Each module representing each step of the planning process up until the advanced stages and going live (daily operation). Each module has its own sub-modules that have specific purposes that assist the main module (e.g. optimization tools, crew portals). All of the modules seen in figure 3.7 are currently available for TUIfly Nordic, but all modules are not in active use (license-for-use agreements are needed). The following paragraphs will give a short description of each module seen in figure 3.7. Some information on the modules of RM5 can be found in booklets offered by Sabre Airline Solutions [10].

ARP is the RM5 equivalent to Arsis. It is an air-traffic scheduling software, with slot memory and report writing possibilities. When the air-traffic program is created in Arsis it is transferred to ARP, via so called *.ssim files. This is how the air-traffic schedule enters the RM5 system, and follows the process as described by figure 3.2. It is preferred by the Planners at TUIfly Nordic that 3.1. SYSTEMS DESCRIPTION 36

the scheduling process begins in ARP, creating a continuous ﬂow system-wise, and transferring the ready schedule from ARP into Arsis, since the only purpose of Arsis is the connection it holds to Opscon and Airpas. This is for the time being not possible as Arsis does not support ﬁle import.

PAR is the crew pairing module (equivalent to Opsman in IDPS). Crew pairings are created while planning the aircraft rotations to ascertain whether or not legality holds. PAR works similar to Arsis, work is mainly manual without access to optimality functions as of today, resulting in a dependency of the Planners experience.

Figure 3.8 Screenshot of the RM5 module: PAR interface.

PAR contains the necessary legalities involved in creating legal work days/periods with the necessary amount of rest periods demanded by regulations involved in the airline business (see section 2.9). In PAR, it is possible to create several scenarios that can be compared and evaluated in order to determine the best outcome. The PAR interface can be seen in ﬁgure 3.8. 3.1. SYSTEMS DESCRIPTION 37

CTO Crew Trip Optimizer is the in-built optimization tool for PAR. This is currently in process of being activated at TUIﬂy Nordic. Evaluations are in progress. Due to lack of relevance to this project no coverage has been made of CTO (also known as Raptor in the older RM4 version). The purpose of activating it is however connected to the problem statement, there is a need at TU- Iﬂy Nordic to assist the Planners with optimization tools in order to determine whether computer generated solutions might provide a better scheduling/pairing solution than the manual work done by the Planners and their gathered experience.

ASG short for Assign is the crew assignment module. This stage is also known as rostering. Assigning each individual crew member work duties according to the crew pairings, and all other activities (training, meetings) but also vacation and days off, making a 28 day schedule for each employee.

ARCON stands for Auto-Rostering, which is the CTO equivalent to the assign module. It is an optimization tool; optimization in the areas of crew pairings, crew assignment and rostering are heavily researched areas, some mathematical models have been developed to the extent that they can be effectively implemented in software (for further reading see Chapter 4). ARCON is currently in use at BLX for cabin crew rostering.

DCO Daily Crew Operations is the crew tracking module. This module keeps track of day-today activities of the crew. Ready rosters are uploaded into DCO and monitored combined with daily air-trafﬁc schedule activities conveyed via the ROC module.

CWP Crew Web Portal is an interface for the crew, one where they can check schedule changes, conﬁrm their check ins and check outs, leave notes and brieﬁngs.

Goody Bag is a system developed by Rainmaker, a company with close business ties to Sabre Airline Solutions. This relationship simplifies system connectivity with RM5. Goody Bag is a recently activated system at TUIfly Nordic, designed to simplify hotel bookings for crew. The basic idea is to automatize booking so that hotels can connect to a remote client and confirm amount of bookings according to the air-traffic schedule.

ROC Rocade Operations Control is the RM5 equivalent to Opscon in IDPS. This is the live air-trafﬁc schedule tracking, day-to-day operations. Opscon is connected to ROC in terms of con- veying the daily operations to RM5 via DCO module. 4

Airline Optimization

The airline business generates profits in the billions, and that only incorporates the charter airlines, expanding the field into regular air traffic and cargo flights the economic impact rises by near exponential factors. As mentioned in the introduction even the smallest improvements to any aspect of the airline process can result in substantial cost reductions. This thesis will review the charter airline business from a mathematical point of view, from a current position at TUIfly Nordic. The contribution will be two folds, first a more comprehensive real-world view will be given of the difficulties governing the traffic scheduling process from the design stage all the way to the execution stage. The purpose is to determine the objectives and constraints that arise along the way and through this determine the risk factors that need to be evaluated in order to reach an optimal air-traffic schedule. This involves several problems that will briefly be explained by mathematical models. The focus will lie on the aircraft rotations scheduling, which in the case of TUIfly Nordic has become the heart of the traffic scheduling process. Secondly, an optimization model will be assessed for modeling certain key performance indicators. Since managing the airline traffic means facing several opposing constraints, a solution is presented appropriating a multi-objective programming model for the optimal allocation of decision variables, allowing TUIfly Nordic to evaluate and asses risk factors that determine the efficiency and productivity as well as the economical impact of traffic schedule scenarios. Methods to optimize problems of a commercial nature was first implemented in the airline industry, making this a widely researched area. Heuristics combined with advanced mathematical algorithms and the advancement in computer hardware and software technology has made it possible to solve large-scale, sophisticated airline optimization problems for the past 60 years. The literature available on airline optimization is vast. A simple overview of the advances and a brief summary of the operational factors governing the airline business ranging from a managerial point of view to traffic scheduling is given by J. L. Snowdon and G. Paleologo [11]. The field of airline optimization is largely limited to regular flights, rarely covering the charter business. Charter airlines compared to regular airlines, are often categorized as small to medium 38 4.1. SCHEDULE PLANNING 39

size airlines based on their production and fleet size, two factors that matter greatly in the process of planning an air-traffic schedule. The planning processes vary greatly from airline to airline, each have their own structure and approach to the steps described in the following sections. This thesis will look into the process of TUIfly Nordic in order to evaluate the optimality of their process.

4.1 Schedule planning

There are several constraints to shaping an airline schedule, such as crew duty hours, aircraft maintenance protocols, passenger flows, ground base resources and arrival/departure time windows. Collaborating all of these objectives into a feasible flight schedule is one of the industry worlds most complex issues. Most often, the process is partitioned and optimized using heuristic models that describe a solvable/programmable model. The complexity of the problem lies in factors that can not be expressed using a simple model, there are company/union regulations, safety regulations, security concerns, market controlled parameters such as volume, density and elasticity of demand. There has never been a single optimization model that has addressed or even formulated the en- tirety of the scheduling process. Attempts have only been made to solve two or more combination at a time in a single model. An overview of the impact operations research has had on the airline industry is given by M. Clarke and B. Smith [12]. The basic steps of the air-traffic scheduling can be seen summarized as in the flow chart in figure 4.1.

Figure 4.1 Schedule design ﬂow chart.

There is a hierarchal order to the steps described in the ﬂow chart, where each step produce the input to the following step. It is possible to optimize each step and provide the result of this step to 4.1. SCHEDULE PLANNING 40 the next, and optimize again but this is not necessarily the optimal result for the problem in total. It can be mentioned that only in mathematical terms is it easy to partition the schedule process into the steps seen above. It is an appropriate way to divide a large problem into sub-problems. These steps (sub-problems) will be described in the coming sections, but since this paper will also show a real-world view of the planning process the reader should keep in mind that these steps are only a rough outline of the process, and that the step by step process that is described here is far from describing the full picture of air-trafﬁc scheduling.

4.1.1 Schedule design

The schedule design stage addresses market based planning parameters such the establishment of a service plan, frequency of flight, demand forecasts for popular travel destinations and competitive market information. Establishing profitability and seasonal demand is important as they are two criteria that largely impact the flight schedule design. The complexity of the schedule design lies in these inherently mutable parameters not only creating a large scale problem but one where information, i. e. revenues, market demands and responses from competitive airlines, are very hard to come by. Creating a feasible flight schedule that addresses the entire problem is very difficult. Support systems for this step of the schedule planning has been slow in development. In the case of TUIfly Nordic information regarding travel demand is largely provided by the Tour Operator. This data is based on statistics, destination capacities (hotels, hotel beds etc.) and estimated market incentives. This information in turn will be developed into an initial feasible flight schedule, arrival and departure times satisfying maintenance intervals and resource constraints (available aircraft, personnel). The seasonal schedules at TUIfly Nordic are generated using the schedule design of the previous season, this becomes the backbone of the new seasons schedule where seasonal changes (destinations, additional flights and aircrafts etc.) are incorporated and adjusted for. Information regarding schedule design has only been researched through overviews, these mention at most how sparse the information is, therefore literature regarding the subject of schedule design have been deemed irrelevant. Observations at TUIfly Nordic has revealed that the nature of each of these variables are operated and monitored by a large amount of people in a company contributing to the already complex problem. There is no local hub to extract data, and some variables also depend on the experience that is contributed by employers that have long served the airline industry. Therefore it is assumed that any model developed will not be flexible enough to incorporate all that is necessary in this stage of planning, there are simply too many constraints and conjectures. This is left to evaluating by processing past schedule designs, statistics and collectively evaluating the needs for coming seasons. 4.1. SCHEDULE PLANNING 41

4.1.2 Fleet assignment

The fleet assignment model determines the size of aircraft (capacity, range) needed for each flight. The objective is to maximize profitability by assigning the right aircraft type to the right flight segment. The fleet assignment complies to a large set of constraints, ranging from passenger demand, personnel on the aircraft to fuel capacity and maintenance demand. There are also operational constraints that have to be taken into account for when the aircraft arrives at its destination e.g. runway length (and availability), gate availability and noise limits to mention a few, all of which contribute to the complexity of the problem. Airlines tend to own different models of aircraft to accommodate different distance requirements for example, Boeing-737 (seating capacity 124–215, maximum range at maximum take of weight 2.800–10.200 km) for shorter flights and Boeing-747 (seating capacity 452–624, maximum range at maximum take of weight 9.800-14.800 km) for longer flights. A large airline can within its fleet of aircraft own several aircraft models, ranging from small propeller driven craft that reach short distance destinations, handling a handful of passengers to larger aircraft that transport hundreds of passengers to destinations farther away without the need for stop-overs or refueling. A small airline will however not afford an extravagantly large modeled fleet, they will have as many aircraft or more commonly fewer aircraft than can meet demand. Instead they will hire extra aircraft to fully accommodate for the travel demands. This is a safety measure, for when economical times are tough. Downsizing a fleet is a difficult task in any economical climate. The fleet assignment problem has been investigated for nearly 20 years. The literature available for the subject is vast with several optimization theories applied, the basic fleet assignment model has been researched for this section only for some mathematical background. The basic model was best described by C. A. Hane et al. [13]: they describe the fleet assignment model (FAM) as a multi-commodity flow problem with side constraints defined on a time-space network that is an integer program using branch-and-bound.

Figure 4.2 Fleet assignment model 4.2

The time-space network has a circular time line, see figure 4.2, representing a 24 hour period or a daily schedule for each aircraft fleet at each city. A node represents an event along the timeline, such as a flight arrival or departure. The assigned fleet to a flight is represented by a decision variable that connects the two nodes created by the departure and arrival. The mathematical problem can be seen in (4.1)-(4.6). 4.1. SCHEDULE PLANNING 42

minimize ∑ ∑cijXij (4.1) j∈J i∈I

subject to: ∑Xij = 1, ∀ j ∈ J (4.2) i

∑Xidot +Yiot−t − ∑Xiodt −Yiott+ = 0, ∀{iot} ∈ N (4.3) d d

∑ Xij + ∑ Yiotnt1 ≤ S(i), ∀i ∈ I (4.4) j∈O(i) o∈C + Yiott+ ≥ 0, ∀ iott ∈ N (4.5) Xij ∈{0,1}, ∀i∈ I, j ∈ J (4.6)

The following notation is required:

C = set of stations (cities) serviced by the schedule, I = set of available fleets, S(i) = number of aircraft in each fleet for i ∈ I, J = set of flights in the schedule, O(i) = set of flight arcs, for i ∈ I, that contains an arbitrary early morning time (i.e. 3AM, overnight), N = set of nodes in the network, which are enumerated by the ordered triple iot consisting of fleet i ∈ I, station o ∈ C, and t =takeoff time or landing time at o, t− = time preceding t, t+ = time following t, iotn = last node in a time line, or equivalently, the node that precedes 3AM, iot1 = successor node to the last node in a time line, and decision variables: Xiodt = Xij = 1 if fleet i is assigned to the flight leg from o to d departing at time t, and 0 otherwise; + Yiott+ = number of aircraft of fleet i ∈ I on the ground at station o ∈ C from time t to t ,

The model is without any through-flights i.e one-stop, which is an adaptation of C. A. Hane et al. [13]. Equation (4.1) minimizes the cost of assigning aircraft types to flight legs. The fleet assignment must be feasible, therefore the first constraint (4.2) states that each flight in the schedule is assigned exactly one aircraft type. The second balance constraint (4.3), ensures that itineraries of all aircraft types are circulations through the time-space network that can be repeated over multiple scheduling horizons, like a cycle. Equation (4.4) is a plane count constraint, saying that the total number of aircraft assigned can not exceed the number available in the fleet. Constraints that address issues such as maintenance demands, slot allocation, passenger and crew considerations can be added to the FAM. Since many of the processes within planning an aircraft rotations schedule are interdependent it has been attempted to combine the fleet assignment 4.1. SCHEDULE PLANNING 43

model with several other of these processes, such as schedule design, maintenance routing and crew rotation scheduling. It is considered that the optimal solution of each of these parts alone do not contribute to the optimal solution of the combined process. Others have developed the model to include time windows, the objective in mind being that improved flight connections can lead to an increase in revenues, researchers in this area are Desaulniers et al [3] and B. Rexing [14], [15]. However as complex as the fleet assignment stage is, and how flexible the model is to accommodate several constraints that inflict the schedule planning stage, it does not apply to the charter airline scene; why this is will be explained shortly. The real scheduling dilemma are the aircraft rotations, or the scheduling of each flight segment, or more commonly called a flight leg. When owning a small aircraft fleet, assigning each aircraft to a specific flight segment becomes a straightforward task. It is mentioned again that the fleet assignment model can accommodate several constraints that are important for a flight program to be feasible or flyable. However solvability will be compromised when several constraints of different nature (linear, nonlinear) are added. There are extensive legal constraints set upon a flight schedule (rules and regulations concerning aircraft, flight safety, flight path restrictions, crew and crew safety followed by pilot and cabin crew union and collective agreements), incorporating these into a model would not only be complicated and time consuming task it would also result in massive cpu times if the model re- mains solvable. No research has yet been made stating the incorporation of even a fraction of these constraints into a mathematical model. There are however softwares that incorporate these parameters as legality, appointing a penalty to an infeasible combination. It is also worth mentioning that there are not that many software available that combines the legality demands for the flight with those of the crew. A combination found very important from observations at TUIfly Nordic.

4.1.3 Aircraft routing

At the aircraft routing stage, each sequence of flights are assigned an aircraft individual. This is also called tail assignment. As mentioned before an airline fleet can consist of several aircraft types, for a small airline it is normally between 3-4 aircraft types. The reason for keeping the number of different types of aircraft low is discussed about in section 2.6.1. There are often a couple of the aircraft types each in a fleet, the aircrafts are designated individual identities, allowing the maintenance teams to keep track of each aircraft individual flight duties (i.e. block hours, maintenance intervals). The most important operational constraint to be met when determining the aircraft routing is maintenance. A simplified maintenance model is described by C.Barnhart et al. [16], notation and model is taken from J. L. Snowdon and G. Paleologo [11]). 4.1. SCHEDULE PLANNING 44

minimize ∑ csxs (4.7) s∈S subject to: ∑ xs = 1, for all ﬂights i (4.8) s∈S

∑ xs + ∑ xs + yO(v) − yI(v) = 0, for all stations v (4.9) j∈O(v) j∈I(v) j∈s j∈s y ≥ 0 (4.10) x ∈{0,1} (4.11)

The model, equations (4.7)-(4.11), describes a sequence of connected flights by one individual aircraft. The flights are defined as a string from airport a → b → c until arriving at a final airport. The following notation is given:

s = strings, cs = associated cost of strings, S = set of all augmented strings (augmented string, the minimum time necessary to perform maintenance attached to the end of the last flight in the string), v = node, i = flights, j = stations, F = setofflights, I(v) = incoming links, O(v) = outgoing links, 1 ifstringisselectedasaroute x = s 0, otherwise, ym = is number of aircraft being served at service station m

From observations at TUIfly Nordic, it is learned that the scheduled maintenance duty for each aircraft individual can vary greatly from one to another, a duty depending most often on flight hours acquired. This means that the maintenance technicians handle the tail assignment. They also handle the aircraft swaps so all aircraft circle through home base at appropriate maintenance intervals. It is preferred to maintain a homogenous spread of flight hours among the individual aircraft.

4.1.4 Crew scheduling

Allocating crew optimally is often deemed the area of airline optimization where huge savings can be made. Crew scheduling can be partitioned into three phases: crew pairing, crew assignment 4.1. SCHEDULE PLANNING 45 and recovery from irregular operations. Crew pairings will be the focus of this project, as it is connected to the planning operations at TUIﬂy Nordic, while crew assignment and recovery will only be discussed brieﬂy.

Crew pairing

The optimizationof crew pairings is a subject studied for nearly half a century. The general purpose is to achieve the minimum cost set of duties that cover each flight leg concurring with current regulations and restrictions for crew duties. Crew pairing has been described earlier i Chapter 2 and therefore only a short explanation will be given here so that the model described later can be easily followed. The generation of crew pairings typically begins with a flight schedule with flight legs and their corresponding fleet assignment. These are then decomposed for each fleet type into work duties incorporating all crew types (pilots, flight attendants). The combination of two or more of these duties is a paring and these range from 1-5 days length and start and end at the designated crew’s home base. Legality, (i.e. ICAO rules and regulations, union agreements etc.) determines the feasibility of crew pairings. There are for instance limitations for number of duties and flight time in a pairing, there are also minimum rest requirements following these limitations, all of which have to be taken into account in order to create crew pairings. European carriers must comply with ICAO, EASA and governmental regulations but the union rules can be somewhat variable, although this usually results in other additional costs. Crew salaries can also be connected to the structure of the pairings e.g. guaranteed minimum pay per duty period and overtime. Penalties are usually included into models in order not to create excessive costs concerning crew duties. Creating a model that incorporates all of these limitations is complex, it is possible to generate a full set of feasible crew pairings for a problem although it is not computationally sensible as it might take several days. It is also difficult to incorporate crew cost as these would have to be incorporated through various different linear (per-diem charges, hotel stay) and nonlinear constraints (flying time, time away from home base). The following literature was studied and is recommended for further reading on the the complexities of crew pairing, G. Desaulniers et al. [2], N. Kohl et al. [6], P. Vance et al. [17], S. Lavoie et al. [1] and J. L. Snowdon et al. [11]. A simple crew pairing model is illustrated with equations (4.12)-(4.14), followed by the notation used in this model.

minimize ∑ c jx j (4.12) j∈P

subject to: ∑ aijx j = 1, ∀i ∈ F (4.13) j∈P

x j ∈{0,1} (4.14) 4.1. SCHEDULE PLANNING 46

P = set of all feasible pairings, c j = costofpairing j, 1 ifpairing j is used x = j 0, otherwise, 1 ifpairing j covers ﬂight i a = ij 0, otherwise, F = set of all ﬂights that must be covered in the period of timeunder consideration,

This model is reminiscent of the fleet assignment model described in section 4.1.2, the objective is to minimize the total cost (equation (4.12)) with the constraint (equation (4.13)) ensuring that each flight is covered once an only once (J. L. Snowdon et al. [11]). This is a partitioning problem, the flights that are needed to be covered are the rows and the available crew pairings are represented in columns. Like the FAM, additions concerning restrictions in resources such as upper and lower bounds on crew availability and number of duties and/or days can be added and in this way significantly reduce the set of feasible pairings (the word feasible used here is not correlated with what is a legal and optimal crew pairing). Several approaches to solve this pairing generation model has been studied and used, some of these will be described in the coming sections.

Crew assignment and recovery

After pairings have been generated a crew is assigned to them, it is at this stage where the individual crew members work duties are scheduled. These assignments or rosters are done on a monthly basis. The objective is to minimize cost while taking crew preferences, vacation days, days off and language restrictions into consideration. It is not uncommon that unscheduled events occur e.g. bad weather, flight cancellation, delays, last-minute maintenance, illegal crew, sickness etc., there are endless unfortunate events that affect both flight crew and passengers. Handling of these problems are referred to as recovery from irregular operations, or simply disruption management. The purpose of having disruption management is to minimize the costs of reassigning crew or aircraft by taking into account available resources. There are different recovery focuses, there are those that handle aircraft and then there are the crew specific disruptions which are the more difficult of the two because of the many rules and limitations that exist for crew work. This section is intentionally kept short due to the lack of relevance it holds to the project, but for the curious reader there are plenty of literature approaching both rostering and recovery operations, some of these have been mentioned previously in the early chapters ( [18], [6], [4]). 4.2. BACKGROUND FOR COMBINED MODEL 47

4.2 Background for combined model

To begin assembling a model that may fit the proceedings of the Planning department at TUIfly Nordic certain presets must be declared. The beginning of this chapter has described that the difficulties of airline optimization are numerous. Combining several stages, like those described by figure 4.1, do not always yield an optimal solution. The reason for this is partially due to how the planning is laid out (something that varies from airline to airline), and also due to computational reasons. Combinatorial models tend to become computationally not trackable. Furthermore including the basic needs of TUIfly Nordic means that several constraints will have to be taken into account resulting in fewer schedule solutions. When optimizing it is commonly held that the global solution that takes account of all constraints is the optimal solution. Figure 4.3 illustrates that when there are several parameters but constraints are held few, then there is a larger set of solutions to chose from. It is important to keep in mind that mathematically optimality is defined with one solution, that is maximizing or minimizing a function with a number of constrains will yield one optimal solution. This is also the case for a schedule, there will only be one optimal schedule as a result of a given algorithm to optimize, although since each objective constraint is variable of several other constraint meaning that changing one function or adding penalties will result in a new solution, yielding in several different feasible air-traffic schedules.

Figure 4.3 Optimization using few decision variables and few constraints, resulting in a wider variety of solutions. Illustration: Mercedes Inal.

Of course when penalties or restrictions are added to the list of constraints (usually in the form of more constraints), see figure 4.4, possible solutions get restricted and there are fewer options to chose from. In the case of air-traffic scheduling, it is assumed that when a nearly infinite number of restrictions are added, a solution accommodating all of these constraints become impossible. A solution here refers to an air-traffic program that is flyable at the lowest cost. Applying the step-by-step planning seen in figure 4.1 to TUIfly Nordic will not work. The reason for this is that the planning process far succeeds this type of model setup. Fleet assignment is not complicated for a fleet of often 12 aircraft (in the case of TUIfly Nordic), it can however be relevant 4.3. SIMULTANEOUS AIRCRAFT ROUTING AND CREW SCHEDULING 48

Figure 4.4 Optimization using many decision variables and constraints, resulting in a fewer solutions. Illustration: Mercedes Inal. to use the FAM when there is a fleet of perhaps 50 or more aircraft, when an overview becomes difficult to retain. With a small fleet the setup is often simple, a Gantt chart view were each aircraft in the fleet is represented as a bar and processes are added to each aircraft according to a timeline (see previous image of Arsis, figure 3.4). The fleet assignment model is therefore not a good place to start, although the model can be extended to include upper and lower crew considerations, certain maintenance constraints, noise limitations and gate availability (L. W. Clarke et al. [7]). Adding these constraints will go a long way when creating a feasible air-traffic schedule, but to apply this model schedule design data is needed (arrival and departure times, days, routes, etc.), data that is more commonly available when creating daily schedules, i.e. regular flights. Charter airlines like TUIfly Nordic have varying seasonal schedules (one for winter and one for summer). There is also not necessarily a fix set of travel destinations, market and customer demand will be dictating factors of travel destinations and opportunities.

4.3 Simultaneous aircraft routing and crew scheduling

Aircraft routing, is a process handled by the TUIfly Nordic maintenance team, insures that each aircraft individual has the necessary maintenance opportunities. However, the Planners will plan the schedule according to this need, there are always swaps and built-in gaps that accommodate a maintenance slot. These slots can be planned as individual flights (a destination) or just left as gaps in the schedule. The air-traffic planning is generated following a pattern, one that repeats after a sufficient enough time laps (often 1.5-2 weeks). Because of this reason it is assumed that the aircraft routing model combined with crew scheduling, J-F. Cordeau et al [19] is a more appropriate model to begin with. This model incorporates maintenance requirements corresponding to routine checks every three or four days, corresponding to regular A-checks. Shorter maintenance checks are scheduled separately, usually depending on where maintenance facilities are available and longer maintenance checks, such as C-checks, are planned over longer period of times since they ground the aircraft for several weeks at a time. 4.3. SIMULTANEOUS AIRCRAFT ROUTING AND CREW SCHEDULING 49

The basic model as described by J-F. Cordeau et al [19] is represented below, some background is needed to understand the notation of this model. This formulation assumes a dated horizon where the set of flight legs can vary from day to day. Assume a planned horizon and a set L of flight legs to be flown by a single aircraft, each flight leg l ∈ L is defined by an origin o and a destination d and by fixed departure and arrival times. The model for simultaneous aircraft routing and crew scheduling is stated as follows:

x y minimize ∑ ∑ cω xω + ∑ ∑ cω yω (4.15) f ∈F ω∈Ω f k∈K ω∈Ωk i subject to: ∑ ∑ aω xω = 1, ∀i ∈ N (4.16) f ∈F ω∈Ω f i ∑ ∑ aω yω = 1, ∀i ∈ N (4.17) k∈K ω∈Ωk ij ij ∑ ∑ bω yω − ∑ ∑ bω xω ≤ 0, ∀(i, j) ∈ C (4.18) k∈K ω∈Ωk f ∈F ω∈Ω f ∑ xω = 1, ∀ f ∈ F (4.19) ω∈Ω f ∑ yω = 1, ∀k ∈ K (4.20) ω∈Ωk f xω ∈{0,1}, ∀ f ∈ F,ω ∈ Ω (4.21) k yω ∈{0,1}, ∀k ∈ K,ω ∈ Ω (4.22) (4.23)

N = setofnodes, A = set of arcs, G = (N,A) time-space network, see figure 4.5 C ⊆ A set of arcs representing short connections in the network G , F = set of aircraft, K = set of crew, Ω f = for every aircraft f ∈ F, set of feasible paths between nodes o f and d f in G f Ωk = for every crew k ∈ K, set of feasible paths in the network Gk ω = path, (i, j) ∈ A represents feasible connections between two successive flight legs, cω = cost of sending one unit flow between o f and d f , where costs are separated for x and y, xω = binary variable that represents the flow on the path concerning aircraft, yω = binary variable that represents the flow on the path concerning crew,

“. . . an arc is deﬁned between nodes i and j if the destination station of leg li is the departure station of leg l j and if the connection time between the two legs is larger than a given station 4.3. SIMULTANEOUS AIRCRAFT ROUTING AND CREW SCHEDULING 50

speciﬁc threshold that represents the minimum connection time when both legs are covered by the aircraft.” (J-F. Cordeau [19]). If a node is selected and an arc belongs to an aircraft path ω ∈ Ω f i ij the binary constants aω and bω will take the value 1. The objective of the function is to minimize the sum of all aircraft routing and crew scheduling costs, equation (4.15). Coverage for each leg by one aircraft and one crew is covered by constraints (4.16)-(4.17). Constraint (4.18) , guarantees that when connection time is too short the crew does not change aircraft. Remaining constraints (4.19)-(4.20) state that each aircraft and each crew are assigned a path.

Figure 4.5 Model network [20].

This model can be supplemented by constraints from the airline crew pairing problem, (ACPP). D. C. Flórez et al. [20] describe a ACPP model that can be incorporated into the simultaneous aircraft routing and crew scheduling model. The set up of the ACPP is similar to the model described by equations (4.15)-(4.22). Adding crew constraints, such as crew flow through the set of legs, ensuring that the crew starts and ends their service at a personal base (home base) and that certain simple flight time limitations are kept, would only add to the model representing real-world situations more accurately. The following constraints can be added, used notation will be explained. Constraint (4.24) will guarantee crew flow through the set of legs l ∈ L. The notation used through out these constraints for feasible paths for all crew, ω ∈ Ωk, is re-written as { j ∈ N|(i, j) ∈ A}. k k ∑ yij − ∑ y ji = 0, ∀i ∈ N,k = 1,...,cmax (4.24) { j∈N|(i, j)∈A} { j∈N|( j,i)∈A}

Where cmax is the maximum number of crews in the solution. The constraint (4.25) indicate that each crew can leave the home base node at most once to serve the ﬁrst leg in the pairing. k ∑ y0 j ≤ 0, ∀k = 1,...,cmax (4.25) { j∈N|(0, j)∈A} 4.3. SIMULTANEOUS AIRCRAFT ROUTING AND CREW SCHEDULING 51

A pairing has to start and end at a personal base and therefore the same city, constraint set (4.26) ensures that this holds. Some the following notations is needed for the next few constraints, B be the set of cities that are personnel bases; S and I are the sets of domestic and international cities, respectively;

k k ∑ yij − ∑ y j0 = 0, ∀i ∈{1,2},k = 1,...,cmax,b ∈ B (4.26) k k {(i, j)∈A|o j=b} {( j,0)∈A|d j =b}

Each pairing can not exceed the maximum number of duties allowed, this is enforced by the set of constraints represented by equation (4.27).

k ∑ yij ≤ dmax, ∀k = 1,...,cmax (4.27) {(i, j)∈A|i>0, j>2,ti

Limit on maximum duty time is set by constraints (4.28).

a g sd d g g k k ti +t0 −tmax − ∑ tn −t1 · hn −t2 · (1 − hn) · ymn + M · yi0 ≤ M, , < , ! {(m n)∈A|tm ti tn=ti} ∀i ∈ NL,k = 1,...,cmax (4.28)

a g sd d g g k k ti +t0 −tmax − ∑ tn −t1 · hn −t2 · (1 − hn) · ymn + M · yij ≤ M, , < , ! {(m n)∈A|tm ti tn=ti} ∀(i, j) ∈ A,i ∈ NL,t j > ti,k = 1,...,cmax

The notation is described as follows (D. C. Flórez et al. [20]): “The set NL = N|0,1,2 is comprised c of nodes that represent legs from the ﬂight schedule whereas the set NL is its complement (base nodes).”

For a pairing; sp tmax is the maximum service time f p tmax is the maximum ﬂying time p lmax is the maximum number of landings 4.3. SIMULTANEOUS AIRCRAFT ROUTING AND CREW SCHEDULING 52

For each duty; dmax is the maximum number of duties in a pairing sd tmax is the maximum service time f d tmax is the maximum ﬂying time d lmax is the maximum number of landings T is deﬁned as the minimum rest time between consecutive duties D is the set of days of the week.

d a f g “ For each leg associated to node i ∈ NL, let ti, oi, di, li, ti , ti , ti , ti , be the day of the week, origin city, destination city, number of landings, departure time, arrival time, ﬂying time, and ground time associated with the airport of the destination city, respectively. It is assumed that d a f c ti = ti = ti = li = 0, for all i ∈ NL.” Furthermore;

1 iftheﬂightrepresentedbynode i ∈ N begins in a national city h = L i 0, otherwise, 1 iftheﬂightrepresentedbynode i ∈ N belongs to day q ∈ D, r = L iq 0, otherwise.

g c “Finally, let ti (i ∈ NL) be the debriefing time (i = 0) and briefing time at a national (i = 1) and international city (i = 2).” Pairing limits and duty flying time are given by constraints sets (4.29)-(4.30) respectively.

f k t p ∑ ti · yij ≤ fmax, ∀k = 1,...,cmax (4.29) {(i, j)∈A}

f k td ∑ ti · riq · yij ≤ fmax, ∀q ∈ D,k = 1,...,cmax (4.30) {(i, j)∈A}

Constraints (4.31)-(4.32) limit the number of landings for pairings and duties.

k p ∑ li · yij ≤ lmax, ∀k = 1,...,cmax (4.31) {(i, j)∈A}

k d ∑ li · riq · yij ≤ lmax, ∀q ∈ D,k = 1,...,cmax (4.32) {(i, j)∈A} 4.4. SOLUTION SUGGESTION FOR AN EXTENDED SIMULTANEOUS AIRCRAFT ROUTING AND CREW SCHEDULING MODEL 53

Now that all the constraints are set up, this model becomes advanced enough to generate a feasible air-trafﬁc schedule, however solving this model becomes a complicated task. This model will not be solved in this report, it is not assumed impossible although not within the reach of available measures and lack of time. A solution method will however be discussed next.

4.4 Solution suggestion for an extended simultaneous aircraft routing and crew scheduling model

This section will discuss a possible solution method to a combined model of the methods described in the previous section. The purpose of which is to render the reader a brief comprehensive look into the many variables that need to be taken into account in air-traffic planning and the difficulty of reaching a complete solution. This is a rather complicated model to solve, the sets involved can be significantly large and the recommended way to solve these is through a branch-and-bound algorithm. This is a method that disregards countless amounts of fruitless solutions by setting upper and lower bounds and systematically enumerating all possibilities. Dantzig and Wolfe decomposition, a column generation method that solves linear programing problems, can be used to compute relaxed linear lower bounds. The branch-and-bound method is a two part process: first, branching means, splitting the large sets, i.e. Ω f , Ωk into smaller sets whose combined union equals the complete sets. This step has a tree-structure since it has a recursive nature, where the nodes are the subsets of the large sets. Secondly, bounding step computes the upper and lower bounds for the minimum value function of a subset of either Ω f and Ωk. The basic idea is to solve a restricted master problem with a set of subproblems through an iterative column generation process. This process starts by a set of artificial variables, ensuring that the master problem is feasible during the initial iterations. Each iteration generating new variables, through an shortest-path problem for each network (G f ( f ∈ F)and Gk(k ∈ K)), for the master problem. Arc costs in the networks reflect the current values of the dual variables (≥ 0) associated with the constraints of the restricted master problem. When new paths are added to the restricted master problem it is re-optimized and yields a new primal solution and new values for the dual-variables. Optimal solution is reached when there are no negative-cost paths identified, and thus the column generation process stops, see Dantzig-Wolfe decomposition algorithm structure in the box below. Similar methods to this is the Benders’ decomposition algorithm, the difference is that this method adds new constraints and is a row generating approach. It is likely that the shear amount of constraints posed by this model, will require an excessive amount of computing time. It is believed that although this problem offers a large amount of possible connections, due to the restricting constraints a feasible solution might not be reached. When the problem is broken down into either decomposition method, it becomes rather simple to solve with straightforward simplex or revised-simplex methods. The models are built on an iterative 4.4. SOLUTION SUGGESTION FOR AN EXTENDED SIMULTANEOUS AIRCRAFT ROUTING AND CREW SCHEDULING MODEL 54

process that yields no possible solution if all constraints are not satisﬁed. This is is assumed to be the plausible outcome when several restricting constraints are added. Dantzig-Wolfe decomposition algorithm (E. Kalvelagen [21])

{initialization} Choose initial subsets of variables. while true do {Master problem} Solve the restricted master problem π1 := duals of coupling constraints π(k) th 2 := duals of the k convexity constraint Sub problems for k=1,...,K do π π(k) Plug 1 and 2 into sub-problem k Solve sub-problem k if reduced cost (pricing) < 0 then Add proposed optimal values to the restricted master end if end for if No proposals generated then Stop: optimal end if end while 5

Analysis

From the onset of this project, the objective was to define and evaluate the needs of the planning department at TUIfly Nordic. This process has been conducted first and foremost on an observational basis. What has been brought to attention and discussed are parameters by which an air-traffic schedule can be evaluated, in order to obtain a good overall view of the efficiency and stability of the program. By evaluating parameters that are important for the planning process, future assessment for implementations to improve the planning process is made possible (by for instance software support). The processes at TUIfly Nordic have been closely observed and summarized in the previous sections. This chapter will analyze these observations. This thesis, has been divided into two parts, one part addressing the needs of TUIfly in terms of processes and KPI and one part that describes the need of the company in technical terms. In doing so, a mathematical model that could describe the traffic planning process was approached, one that would take in to account the numerous constraints of planning an air-traffic schedule. This is a large undertaking, and one that can not be approached in the period of time assigned for this project, thus restrictions have been taken and the examples are kept on a small scale. The difficulty in airline optimization is that there is never a comprehensive way to describe events that occur in the airline sector. This is an area where processes are highly interdependent and where there are too many constraints that are of an uncertain nature to account for. Large computational processes are at work for even the most heuristic of models for optimizing any part of the airline sector. This project has made an attempt to address what risks are involved in the traffic planning stage, and in what ways they are connected to one another. To begin assessing the needs of TUIfly, it was essential to find out the requirements of the Planning department. In order to do so, the internal processes have been studied and described in sections 2-3. The current structure of the software processes will be described in the next section 5.1, followed by a parameter study of the interdependency that occurs when evaluating key performance indicators.

55 5.1. SOFTWARE NEED 56

5.1 Software need

The systems network as it currently functions today and the supposed need for a solution is represented in figure 5.1. The system, in the figure referred to as System X, represents the connectivity necessary to accommodate the needs which have been addressed (these will be explained shortly) whilst causing no interference to the systems operating today. The basic need is one that optimizes and evaluates the air-traffic schedules (or scenarios) according to several key performance indicators (section 5.2).

Figure 5.1 Software need. Illustration: Marcus Karlsson additions made by Clement Berguerand.

Introducing a system like this is a complicated process and what is covered by the scope of this project will only explain the needs and requirements expressed by the planning department of TUIfly Nordic. Complications are partially due to the company currently operating within two systems. Systems that, when regarded separately, function without difficulty but combined currently prohibits their maximum performance and in turn does not entirely contribute to the benefit of the planning department. Thus, introducing a new system means one more connectivity solution has to be provided without disrupting current processes. It is also not an option to start from a clean slate by using one complete system for all operations, as neither of the systems used today (RM5 and IDPS) are considered for suspension according to TUIfly Nordic (referred to the level of 5.1. SOFTWARE NEED 57

satisfaction and the investment put into the systems in operation as of today). Preferably the ideal situation would be to find a solution offered by the manufacturers of the current systems, IDPS or RM5. This would make integration simpler and is also assumed to be more cost effective. The viewpoint of this project is one that regards the air-traffic scheduling as the focus point of all planning operations. The pre-planning needed to generate a flyable and flexible air-traffic schedule is momentous. This is mainly the reason why airline optimization is so difficult, and why, after nearly 60 years of studies in the field (J. L. Snowdon et al. [11]), a more beneficial operating systems for the airline business has not yet been generated. The parameters (e.g. crew rotations, maintenance planning, slots, legality, FDP (Flight Duty Period) ), that need to be taken into account from the very beginning of the planning process, create a situation where it is hard to retain an overview. The needs expressed by TUIfly Nordic are ones that stem from Arsis not being useful enough in terms of generating scenarios and evaluating these separately. There is also an issue of module interconnectivity mentioned in the Arsis section 3.1.1 where even if scenarios can be generated these can not be used in the live module. Presently, there is a rather limited possibility to determine whether an air-traffic schedule is efficient or stable other than by comparing changes made from previous season schedules. There is a need to control the risks involved in planning a traffic schedule while also being able to determine the costs behind the actions of long term planning; which means that for instance if a maintenance stop is pushed to the last minute, what profits are gained from the aircraft being in operation for slightly longer, what are the safety risks, is the schedule more efficient or robust that way? There are many questions, and this example only regarded one parameter being changed, there are several other parameters, some presenting less risk when shifted and others like the maintenance scenario a higher risk factor. What becomes apparent is that System X needs to be able to create scenarios and from these generate reports assessing how the changes in KPI (Key performance indicators see section 5.2) affect the schedule in terms of efficiency, cost, safety, flexibility and stability, this will in turn allow the company to create a risk assessment model. The system will have to be able to communicate with Arsis for the execution of the preferred scenarios while also being able to communicate with RM5 to accommodate for crew processes. To further explain what System X is, and what it is meant to perform, the next section will describe the key performance indicators that help evaluate the air-traffic schedule over different important categories, e.g. flexibility, cost. System Y represents a report tool, one that can generate reports disclosing costs for the crew related processes. This tool can be used to decide the price for a pairing and how to divide the cost that incur. The setup for this system was not studied as deeply, due to time constraints and due to prioritizing the need to define the purpose of System X. 5.2. KEY PERFORMANCE INDICATORS 58

5.2 Key performance indicators

Key performance indicators, are parameters by which the air-traffic schedule can be evaluated. These KPI range from the simple time table parameters such as how many rotations an aircraft does over a period of time to market driven factors, e.g. arrival and departure times. These KPI are risk factors, that can be ranked according to the effect they have on the air-traffic schedule. Determining important key performance indicators can be difficult since there are so many for the air-traffic scheduling process. The difficulty is also due to the opposing nature of these indicators and the interdependency among them, which will be discussed next. At TUIfly Nordic, these KPI have been discussed for years, which is not an uncommon situation in the airline business. The following key performance indictors, seen in table 5.1, were identified by the Planners at TUIfly Nordic during the course of this project. The table represents the first attempt to address the needs and requirements for which System X will satisfy. Table 5.1

Trafﬁc schedule scenarios

T1 T2 T3 T4

Planning parameters∗ 60% 85% 5% 34% Flexibility #Historic Slot: Total 56% 35% - 6% Stability: #FDP sensitive ﬂights 8% 10% 67% 40% Robustness ∑i Stopi×Lengthi #Rotations 42% 50% 34% -

#BLH×#Rotations 65% 30% 4% 12% Production daystot #Ferry ﬂights 1% 60% 78% 9% Cost: Rotations #Positioning ﬂights (in cycle) Rotations 56% 8% 25% 10% Price 2.3 MC 5.6MC 1MC 3.3MC Market driven Customer: Arrival & Departure (Time). 30% 50% 20% 80% Days.

∑Total: 318 328 233 191

Table 5.1 Key Performance indicators. The used values are illustrative and have no reﬂection on TUIﬂy Nordic.

illustrates how four feasible and flyable traffic scenarios are suggested and evaluated according to the KPI in order to determine which one corresponds best to the over all need (or simply which one is preferred). This method is also useful when presenting the impact of any changes to the traffic 5.2. KEY PERFORMANCE INDICATORS 59

schedule when conflicting opinions occur in terms of accommodating a change, this way allows all parties to get a clear view of the consequences, whether it is to the economy or the stability of the schedule. The percentile values are fictitious, they are a visual representation requested by TUIfly Nordic. Some of these indicators are simpler to identify than others. The simpler ones can be used to assess an air-traffic schedules efficiency from one season to the next. These have been grouped into a category named planning parameters, these are integer variables that are easily countable. Planning parameters∗: #rotations/aircraft #over-night stays/slip pattern #production days/slip pattern #three pilot positioning/slip pattern #ferry flights/rotation #crew requirement/rotation #home-base stops/rotation % FDP sensitive flights (where # stands for “amount of”) Most of these indicators are not directly measurable with current software. Some can be calculated manually which is tedious even for a two week scenario. A simple schedule, for a three day period with three types of aircraft, is illustrated in figure 5.2, this image also visualizes some of the terminology used.

Figure 5.2 Simple ﬂight schedule example. Illustration: Mercedes Inal.

Deciding what key performance indicators to focus on is a big step. The next step is deciding how 5.3. RISK MANAGEMENT 60

to rank them in terms of risk, the severity and the probability of each KPI (further reading in section 5.3). Ranking these parameters is the first stage in assessing the effects of any relative change to a flight schedule. When parameters are laid out as they are in table 5.1, other than a clear under- standing in what factors matter when building an air-traffic schedule, the table doesn’t conway any hard facts, there are no numbers or values to base a decision upon. While the planning parameters, mentioned earlier, are straightforward and basically countable, some of the other parameters are not as easy to fix with a number (like the market driven parameters or the FDP sensitive flights, Flight Duty Period). Deciding in what way to order these KPI will be a subject for future research, however a simple methodology will be suggested in the next section. For future reference this will allow TUIfly Nordic to find a method of how to measure these parameters efficiently and create a database of their own for statistical analysis of schedules for coming seasons. Furthermore, having found these KPI presented a new problem and an addition to this project, risk management. This will be discussed in the next section (5.3).

5.3 Risk management

This section will describe a risk management approach to the TUIfly Nordic problem statement. Key performance indicators are not always easy to measure as mentioned previously. When optimizing one parameter (e.g. arrival/departure time, maintenance times, turn around time) it tends to affect several others, some to the positive but most often to the negative as the parameters usually cause conflict. After setting up important KPI at TUIfly Nordic (table 5.1), a discussion was en- gaged regarding the measurability of these indicators, and within what ranges values are to be set for each parameter. Since there are no assessments made over previous schedules, other than for increase (a percentile calculation) in the planning parameters mentioned in the previous section, and no evaluating measures in Arsis, beginning assessment of these KPI became hard. Therefore much of this problem resonates in not having the necessary tools (system support) to ascertain whether or not a schedule is optimal. And optimality is decided on hard numbers, which is also a feature missing form the software today. A simple methodology has been discussed, where each KPI connected parameter is ranked according to the risk value they impose: FDP Maintenance Risk value: Risk value: 1 if0-20% 1 if0-10% 2 if20-40% 2 if10-30% 3 if40-60% 3 if30-60% 4 if60-80% 4 if60-90% 5 if80-100% 5 if90-100% 5.3. RISK MANAGEMENT 61

This is just an example, again the values do not represent TUIfly Nordic, each independent parameter must be assessed and risk values for the impact of other parameters on to it must be evaluated so that a system of risk situations can be presented. How the Planners set about to construct an air-traffic schedule is by most often using previous seasons schedule and rearranging it to fit demands for the coming season. The problems begin when trying to incorporate larger changes into an old season (i.e. new aircraft, new destinations, increase in rotations etc.) and relying on experience when doing so. It is hard to incorporate all parameters that make up for a feasible flight schedule in a mathematical model, it is even harder when doing so manually. The Planners have to limit themselves in order to begin implementing new changes. At TUIfly Nordic, these limitations can be categorized into three main groups, see figure 5.3. These three categories cover three major areas concerning crew (FTL), maintenance

Figure 5.3 Risk parameters. Illustration: Mercedes Inal. and aircraft rotations. Seen in the illustrations are sub-parameters to each of the three major ones, these sub-parameters are all affected, to some extent, by any changes to the three major parameters. The planning department wants to determine the risk caused by a change in such parameter, e.g. cost implications, stability and efficiency. This problem resonates in risk management. The risk assessment equation (or simply the risk equation) is a function: Risk = f (Threat, Vulnerability, Asset). (5.1) This is a probability function stating that risk is the probability that a threat will exploit a vulnerability to cause harm to an asset (JISC infoNet [22]). Figure 5.4 (JISC infoNet [22]), illustrates a risk assessment model. This is a common model, frequently used to describe risk management. This model is adapted to the purpose of this project, stating that after risk parameters are identified (i.e. figure 5.3), they are to be analyzed qualitatively and quantitatively in order to plan an appropriate response followed by monitoring and controlling the effects of the response. This means to 5.3. RISK MANAGEMENT 62

Figure 5.4 Risk assessment model [22]. clarify what steps are to be taken, to identify the magnitude and severity of the risks involved in air-trafﬁc planning, and act according to certain risk management actions. When assessing risks, there are often statistics to go by in terms of how to deﬁne the severity of a risk. A simple model, one that is commonly associated with equation (5.1) is the following equation (5.2), although the terminology can vary depending on the writers preference.

Risk = Severity ∗ Probability. (5.2)

This equation will be the definition of the typical risk management action model illustrated in figure 5.5. Assessing the risk concerning changes in key performance indicators is a large undertaking. Assessment of risks involved are coloured greatly by the Planners views and opinions and some parameters are connected to others in such way that it is hard to assess the severity and probability of them occurring (e.g. how does one put a value on union agreements?). Since there hasn’t been any incorporated report generating tool in the IDPS system for simple planning parameters, at TUIfly Nordic, statistics are hard to come by. Measuring the stability and optimality of an air-traffic schedule has not been a possibility so far. Example: The following example will illustrate how the risk management actions seen in figure 5.5 can be applied. There are several flights that are planned with a zero margin between maximum FDP and planned FDP, these flights are FDP sensitive for long haul flights for instance, but they are still regarded as an acceptable risk, putting them in the lower left box (1x1) of figure 5.5, however it also fits into the upper right box (3x3), representing a high risk situation since even small disturbances (e.g. delays) might break the FDP limitations and drive a need to implement re-planning measures. So for instance the simple method of ranking can be taken a step further (for FDP only in this case): 5.4. MULTIOBJECTIVE OPTIMIZATION 63

Figure 5.5 Risk management actions. Illustration courtesy of Google, reworked by Mercedes Inal to ﬁt TUIﬂy Nordic.

FDP Risk value: Risk management action (according to ﬁgure 5.5) 1 if0-20% (1x1) 2 if20-40% (1x2) & (2x1) 3 if40-60% (1x3) & (2x2) & (3x1) 4 if60-80% (2x3) & (3x2) 5 if80-100% (3x3)

This is only a presentation of how it is possible to go about the given problem, it will be a case for future studies.

5.4 Multiobjective optimization

This section will provide an discussion surrounding the complexities of creating a mathematical model suitable for the risk parameters mentioned above. It was mentioned earlier that the Planners could group parameters that they need to keep track on into three main categories. These three categories contain several parameters, and these three categories will sometimes cross effect. For simplicity these three categories can be classed as functions. 5.4. MULTIOBJECTIVE OPTIMIZATION 64

Optimizing a function with certain decision variables is generally very simple, an example is given in equations (5.3), by using the simplex method this will yield one optimal solution.

n minimize f (x) = c ∑ x j j=1 n subject to ∑ a jx j ≤ c j (5.3) j=1

x j ≥ 0 for all x ∈ X

However when you have several functions that are interdependent of each other, like the risk parameters in ﬁgure 5.3, then optimizing becomes difﬁcult. A simple multiobjective function is given in equation (5.4).

minimize F(x)=( f1(x),.... fm(x)) subject to x ∈ X (5.4)

The notation used is as follows, X is the decision space, Rm is the objective space, and F : X → Rm consists of m real-valued objective functions. The difficulty of optimizing such a function resides partly in that it is made up of smaller functions, meaning each variable is in fact a function which depends on its own respective variables. The problem described in the previous sections of this chapter is such that if among a set of choices, where there is a way of valuing each choice, then these can be graphed and the optimal choice can be picked out. This problem will be defined as in equation (5.5). The objective is to minimize each function, since it is for the time being assumed that small values are favorable, but it is also assumed that these functions have a contradicting nature, say for instance if f1(x) should be strictly decreasing and f3(x) should be strictly increasing, which inevitably causes a conflict.

minimize f1(x) + f2(x)+,...,+ fm(x) subject to g(x) ≥ 0 (5.5) x ∈ X

Here the constraints are represented by a function g(x). A geometric interpretation is provided, as this chapter will not go further into proofs or how to solve such an optimization. The data set M, equation (5.6), represents the countless points that could be produced by equation (5.5).

m M = {( f1(x), f2(x),.., fm(x)) | x ∈ X} ⊂ R (5.6)

Figure 5.6 is a representation of a smaller time space, where equation (5.6) illustrates a set M which 2 contains a very large number (ﬁnite) number of points in R . The horizontal axis displays f2(x) 5.4. MULTIOBJECTIVE OPTIMIZATION 65

and the vertical axis f1(x).

2 M = {( f1(x), f2(x)) | x ∈ X} ⊂ R (5.7)

Figure 5.6 Marginal allocation example. Illustration: K. Svanberg [23].

The convex line created, in the figure represented by the blue line encircling the set M, is the efficient curve and the points that lie on this curve are efficient points. These points will be the efficient solutions of a minimization of f1(x) + f2(x). The image shows that given a specific situation a solution will be decided with whichever point fits the circumstances best, this is illustrated by the isocost lines (dashed lines) in the graph. A few methods were considered for explaining this problem mathematically, the two chosen are a marginal allocation approach K. Svanberg [23] and pareto curves., both of which achieve the same results. Vilfredo Pareto was an Italian economist that is behind the term Pareto optimality, or efficiency. This is a common concept in economics, it is an allocation method of choices or goods among any set of which these concern. 6

Discussion

6.1 Checklist for minimum software performance

In this section a list has been produced that covers the minimum requirements that the Planning department at TUIﬂy Nordic has put forth. New software must hold up to certain demands in order to be worth investing in. The list addresses some key software performance demands that are not currently available through Arsis. These demands are not listed in any order of importance, if new software should be worth evaluating then the listed demands can be used as a checklist for further research. 1. Interconnectivity with current systems • IDPS connectivity, Arsis compatibility • RM5 compatibility 2. Possibility to change parameters • Adding parameters and values in-house, without the need to call support 3. Report generating options • Large variety of report styles • Diagrams/Charts • Comparability • Excel compatibility 4. Optimization system • Different optimization solutions for multiple scenarios

66 6.2. CONCLUSIONS FOR THE NEEDS OF TUIFLY NORDIC 67

• Ability to separately optimize partial stages in the process i.e. maintenance, crew, price/rotation 5. Running times • Analysis time (XX min) • Produce reports (XX min) 6. Internal processes • Importing information to the program that analysis’s depend on i.e. fuel costs, contracts, leases, cleaning. • Implementation time, how long will it take to incorporate the system into the existing work process • Training for usage 7. Price • Per license • User fees • First time cost i.e. start up cost, training, installation & support 8. User friendliness 9. Support

6.2 Conclusions for the needs of TUIﬂy Nordic

In this paper, the air-traffic planning process for TUIfly Nordic has been studied and documented. The study is based mostly on observations and these observations have been supplemented by literature research in order to create a model that fits the scheduling processes at TUIfly Nordic, incorporating some of their current planning processes. This model tries to explain the difficulties in solving a large scale optimization problem that describe several sub problems that are sometimes easy to solve when studied separately but combined creating a complex problem. This paper also provides a background into airline optimization and explaining key variables that make up a flyable flight schedule. It has been mentioned that charter airlines are rarely covered in literature, research often covers regular flights as it is simpler to model flights when there is a daily routine with a set frequency or a repeating schedule yearly, making the steps described in Chapter 5 possible to follow in terms of order. Literature available tend to use American airlines more often than the European ones, as these have different rules and regulations not easily modeled. 6.3. FUTURE RESEARCH 68

The needs of TUIfly Nordic have been extensively described through out this paper and it is not entirely simple to sum up the needs of the Planning department. It was of great importance to determine the key performance indicators, parameters to evaluate and compare different scenarios of air-traffic schedules. These KPI had not been decided previously and was thus regarded as progress and an opportunity for further research in the future. Establishing KPI also opened up new discussions, as there has only been limited possibilities available to compare the air-traffic schedules for different seasons there was therefore also a need to determine how to set up ways to measure each of these KPI. The conclusion of this analysis has been that the basic need is one that stems in risk management. How to rank key performance indicators and how to optimize according to these parameters while being able to generate active reports for evaluation and scenario comparison. There was a question as whether to implement new software, where the need was defined as a system that simplifies the building of traffic scenarios and generates reports while establishing a link that works between the systems currently in operation. While the system need might seem basic, it is however not a simple need to meet, there is currently no such program available. Some systems available today are Sabre®Rocade from Sabre Airline Solutions (worldwide large-scale system), Jeppesen and Navitaire all of which offer a wide range of solutions for airline operations and recovery. For small and even medium sized airlines these systems are cost prohibitive leaving room for may in-house developed solutions for airline operations and optimization. Introducing a new system for TUIfly Nordic was found to be largely unnecessary opting for further research and development of the systems currently in operation. This way continuity is established throughout the planning process while also simplifying the necessary support provisions and personal training, since there is already a support system established for the current programs. Improvements to the airline operation systems are constantly made, and renegotiations about re- visions to the license agreements in order to include support for new developments can be looked into.

6.3 Future research

This project only scratches the surface of the needs for TUIfly Nordic. There are a few areas where efforts can be put into for future research that can provide an improvement to the processes today. These areas are the RM5 system and the module ARP, an in program module or an application that optimizes the air-traffic schedule is in development. Furthermore, and a much larger project, is to look into the IDPS systems and Arsis, there is currently no option to import files into Arsis. An issue that is the main reason for why schedule building is made in Arsis today and not in ARP. Had it been possible to import files into Arsis, a continuous planning process chain would be accomplished through the RM5 system, requiring only a schedule input into Arsis to establish contact with Opscon and Airpas. 6.3. FUTURE RESEARCH 69

There is a need to research how to rank the KPI according to the level of their relative importance and the risk they impose onto a ﬂight schedule. This can be done partially by establishing how they are interconnected and how they vary when penalties are set and partially by their own individual importance. Again this is something that has to be reviewed and where only extensive data gathering could yield a possible solution. Bibliography

[1] S. Lavoie, M. Minoux, and E. Odier, “A new approach for crew pairing problems by coloumn generation with an application to air transport,” European Journal of Operational Research 35, 45–58 (1988). [2] G. Desaulniers, J. Desrosiers, Y. Dumas, S. Marc, B. Rioux, M. M. Solomon, and F. Soumis, “Crew pairing at Air France,” European Journal of Operational Research 97, 245–259 (1997). [3] G. Desaulniers, J. Desrosiers, Y. Dumas, M. M. Solomon, and F. Soumis, “Daily aircraft routing and scheduling,” Management Science 43, 841–855 (1997). [4] N. Kohl and S. E. Karisch, “Airline crew rostering: problem types, modeling and optimization,”, 2002. [5] C. Barnhart, A. M. Cohn, E. L. Johnson, D. Klabjan, G. L. Nemhauser, and P. H. Vance, in Handbook of Transportation Science, second edition ed., R. W. Hall, ed., (Kluwer Academic Publishers, 2003), Chap. 14 Airline Crew Scheduling, pp. 517–560. [6] N. Kohl, A. Larsen, J. Larsen, A. Ross, and S. Tiourine, “Airline disruption management- Perspectives, experience and outlook,” Journal of Air Transport Management 13, 149–162 (2007). [7] L. W. Clarke, C. A. Hane, E. L. Johnson, and G. L. Nemhauser, “Maintenance and crew considerations in fleet assignment,” Transportation Science 30, 249–260 (1996). [8] 737-600/700/800/900 Maintenance Planning Document, Boeing, 2010. [9] Airplane maintenace program B767-300 TUIfly Nordic AB, TUIfly Nordic AB, 2009. [10] . Sabre Airline Solutions, “Sabre Rocade Airline Operations Suite,”, Rocade.pdf (© 2005, 2006 Sabre Inc. All rights reserved.). [11] J. L. Snowdon and G. Paleologo, in Operations Research and Management Science Hand- book, A. R. Ravindran, ed., (CRC Press, 2007). [12] M. Clarke and B. Smith, “Impact of Operations Research on the Evolution of the Airline Industry,” Journal of Aircraft 41, 63–72 (2004).

70 BIBLIOGRAPHY 71

[13] C. A. Hane, C. Barnhart, E. L. Johnson, R. E. Marsten, G. L. Nemhauser, and G. Sigis- mondi, “The fleet assignment problem: solving a large scale integer program,” Mathematical Programming 70, 211–232 (1995). [14] B. Rexing, “Airline fleet assignment with time windows”, Massachusetts Institute of Tech- nology, Department of Civil and Environmental Engineering, 1997. [15] B. Rexing, C. Barnhart, T. Kniker, A. Jarrah, and N. Krishnamurthy, “Airline fleet assignment with time windows,” Transportation Science 34, 1–20 (2000). [16] C. Barnhart, N. L. Boland, L. W. Clarke, E. L. Johnson, G. L. Nemhauser, and R. G. Shenoi, “Flight string models for aircraft fleeting and routing,” Transportation Science 32, 208–220 (1998). [17] P. Vance, C. Barnhart, E. Johnson, and G. Nemhauser, “Airline crew scheduling: a new formulation and decomposition algorithm,” Operations Research 45, 188–200 (1997). [18] W. El Moudani, C. A. N. Cosenza, M. de Coligny, and F. Mora-Camino, “A bi-criterion approach for the airlines crew rostering problems,” Evolutionary Multi-Criterion Optimization 1993/2001, 486–500 (2001). [19] J.-F. Cordeau, G. Stojkovic,´ F. Soumis, and J. Desrosiers, “Benders decomposition for simultaneous aircraft routing and crew scheduling,” Transportation Science 35, 375–388 (2001). [20] D. C. Flórez, J. L. Walteros, M. A. Vargas, A. L. Medaglia, and N. Velasco, “A mathematical programming approach to airline crew pairing optimization,”. [21] E. Kalvelagen, “Dantzig-Wolfe decomposition with GAMS,”, Amsterdam Optimization Modeling Group LLC, Washington D.C./The Hague. [22] “JISC infoNet,”, Webpage, © 2009 Northumbria University. [23] K. Svanberg, “On marginal allocation,”, KTH, Stockholm, Sweden. Appendix A

Table 1 represents the data used to produce the diagram in figure 2.3. The table contains figures that represents future production and are thus sensitive material belonging to TUIfly Nordic (©TUIfly Nordic).

72 BIBLIOGRAPHY 73

Legs/Aircraft Year Month Boeing-737 Boeing-747 Boeing-757 Boeing-767 January 196 221 203 February 164 199 191 March 199 223 179 April 177 155 85 May 210 129 98 June 342 229 95 2010 July 358 243 101 August 352 243 102 September 333 218 97 October 293 235 91 November 251 230 96 December 246 26 237 171 January 258 46 222 195 February 240 44 209 179 March 277 38 230 169 April 260 132 79 May 386 92 92 June 557 90 93 2011 July 661 100 98 August 647 94 97 September 589 86 90 October 516 61 87 November 411 109 December 406 154 January 415 190 February 399 191 2012 March 424 207 April 287 115

Table 1 Legs ﬂown by aircraft type per month.