Degree Project Level: Master’S

Degree Project Level: Master’s

CO2-efficient retail locations: Building a web-based DSS by the Waterfall Methodology

Author: Julateh K. Mulbah & Tilahun Gebreslassie Kahsay Supervisor: Kenneth Carling, Xiaoyun Zhao Examiner: Moudud Alam Subject/main field of study: Microdata Analysis Course code: MI4001 Credits: 30 ECTS Date of examination: June 09,2021

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Abstract:

Several studies have been carryout on finding optimal locations to minimize CO2 emissions from the last mile distribution perspective. In conjunction with that, there has been no study conducted in Sweden that provides a decision support system to compute the transport consequences of the modifications in the retailer’s store network. This thesis did used the following steps: requirement analysis, system design, implementation and testing to build a prototype decision support system that is to help retailers find optimal locations for a new retail store. This thesis provided a subsequent answer as to which data are needed along with the rightful user interface for said decision support system. Subsequently, this thesis does present a decision support system prototype from which some recommendations were provided as to what skills set and tools are needed for the management and maintenance of said decision support system. The primary data used during this thesis is the Dalarna municipalities, six selected retailer’s stores networks and the Dalarna Road network geo-data (Longitude and latitude). This thesis does conclude that it is possible to integrate an optimization model within the Django framework using a geo data to build a decision support system.

Keywords: Optimal location, Statistical modeling, Web development and P- median model

Acknowledgements:

We like to express appreciation to all those who supported us in this thesis development from the inception up to the final presentation. We humbly thank our supervisors: Kenneth Carling and Xiaoyun Zhao for their undeniable support and guidance.

Table of Contents: Abstract: ...... i Acknowledgements: ...... ii List of Figures: ...... iv List of Tables: ...... iv List of Equations: ...... iv 1: Introduction ...... 1 1.1 Research Background ...... 1 1.2 General Research Problem and Research Goal ...... 2 1.3 Research Objective and Research Questions...... 3 1.4 The Scope of the thesis ...... 4 1.5 Research Disposition ...... 4 2: Related work and derived insights ...... 6 3: Methodology, input data, methods, and software tools ...... 9 3.1 Description of Empirical Data and Study Population...... 9 3.1.0 Data Source Description ...... 9 3.1.1 Study Population and Data ...... 9 3.1.2 The Borlange Residence Volunteer Data ...... 12 3.2 Methodology and Methods ...... 13 3.2.0 System Design and Model Formulations ...... 15 3.2.1 Model Formulation ...... 15 3.2.2 Model Interpretation ...... 17 3.2.3 Emission Based P-median Method ...... 17 3.3 DSS Implementation ...... 19 4: Data quality and processing ...... 23 4.1 Data Pre-processing ...... 23 5: An illustration of the prototype of the DSS ...... 26 6: Thesis discussion and future work ...... 31 7: Limitations ...... 33 8: Conclusions ...... 34 References ...... 35 Appendix ...... 38 Appendix A: The Registration Form for Retailers ...... 38 Appendix C: Finding optimal location...... 38 Appendix D Result from finding optimal location using the DSS...... 39 Appendix E System Architecture ...... 40 Appendix F A link to a short presentation of the prototype DSS ...... 40

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List of Figures:

Figure 1 ------11 Figure 2 ------12 Figure 3------19 Figure 4 ------24 Figure 5 ------25 Figure 6 ------26 Figure 7 ------28 Figure 8 ------29 Figure 9 ------30

List of Tables:

Table 1------9 Table 2------14 Table 3------16 Table 4------18 Table 5------20

Table 6------27-28

List of Equations:

Equation 1------16 Equation 2------16 Equation 3------16 Equation 4------16 Equation 5------17 Equation 6------17 Equation 7------17

1: Introduction

1.1 Research Background

The International Energy Agency (IEA), in 2009 stated that the growing global urbanization is expected to have a ratio of 66% by 2050. This ratio will increase transport energy use and carbon dioxide (hereafter CO2) emissions by nearly 50% in the year 2030 and more than 80% by 2050 (Zhao et al., 2016). CO2 is a key greenhouse gas that is contributing to climate change and global warming. In recent years, a lot of attention has been drawn to the impact of CO2-emitted by the transport specifically for retail (Archer et al., 2018). Since CO2 emissions are not environmentally friendly, there have been series of measures taken to help reduce the amount of CO2-emitted like the introduction of the Carbon tax in 199l in Sweden (Daunfeldt et al., 2009). This carbon tax started at a rate equivalent to today’s

Swedish krona 250kr, per ton of fossil CO2 emitted but now in 2020, the tax rate is now SEK 1190 (Daunfeldt et al., 2009). Another strategy introduced by authorities to reduce transport-related CO2 emissions is the congestion charges implemented in Stockholm and Gothenburg in 2007 and 2013 (Daunfeldt et al., 2013). However, business owners feared and expressed concerns that the introduction of the charges might negatively affect their sales. On the contrary a research conducted found out that the congestion charges instead had a positive environmental effect, while there was no substantial evidence to back up these claims that these congestion charges did reduce their sales (Daunfeldt et al., 2013).

The need for new technologies, fuels, and smart urban development are two main area of focus to intervene in the reduction of global energy-related CO2 by 50% from the predicted level in 2050 (Zhao et al., 2016). The problems with strategies like the direct regulations, promotion of green fuels, and modification of travel behaviour require a long time to be adhered to. In return, it become ineffective for the reduction in retailing CO2-emission on a larger scale (Carling et al., 2013).

This thesis does fit into the said strategy of using new technologies to intervene in the reduction of CO2 emitted from customer travel to and from a retail store. According to research, using the optimal locations for retail chain stores and e-

tailing outlets and good urban planning could lead to a more and fast reduction in

CO2 emission related to retailing (Carling et al., 2013, Zhao et al., 2019).

1.2 General Research Problem and Research Goal

Regardless of all the efforts to reduce CO2 emissions from goods reaching the customer (last-mile distribution, it has still been influenced by consumer own, environmentally not so efficient private car transportation. This causes the last-mile delivery to be the most expensive and inefficient when it comes to the entire delivery process (Vanelslander et al., 2013). A case study conducted in the Dalarna region confirmed a 22% reduction in CO2 emission can be achieved if stores had been located at their optimal location from a CO2 emission standpoint (Carling et al., 2013).

From all the current research conducted within the field of location science specifically in Sweden, none of these studies have researched on how a web-based decision support system (hereafter, DSS) can be developed using a sequential methodology like the waterfall methodology. A DSS that does provide retailers the means to automatically find optimal locations for their stores within their store network. As well as considering the average distance from the customers to a retail store. Specifically, when a retailer considers relocating, add a store to their network.

Presently, whenever a retailer is about to modify its store network, (s)he must hire a consultant to do the location optimization. Most times the optimization models used by the consultants are only useful for that time. Furthermore, these processes are not automated making the consultant to redo the entire process all over.

The goal of this thesis is to develop a prototype Web-based DSS for retailers, where the core components will be a user interface and toolkits running in the background to execute the computing of the transport consequences of a modification in the retailer’s network of stores. This prototype is to provide an answer to our research goal as to how can a waterfall methodology can be used to develop the said DSS for finding optimal location with regards to distance. The next section will provide the necessary objectives needed to achieve the stated goal and the necessary questions to be asked to provide the right solution to this thesis goal and the overall research question.

1.3 Research Objective and Research Questions

This thesis’s contribution to knowledge is to provide findings from the investigation of using a sequential methodology to build a prototyped DSS. To provide an empirical answer to the said investigation of this thesis, a thorough review of related work was done to derived insights. These insights were used to provide us answers to the above-mentioned investigation. Furthermore, a solution to the said investigation the below-listed objectives were derived:

a) To identify the data needed, its availability compatibility, and combination.

b) To identify the user interface needed this means the type of information, access, and visualization.

c) To identify the methodologies and models needed in the background toolkit of the above-mentioned DSS.

d) To identify the needed tools and skills set for the management and maintenance of the above-mentioned system.

In this thesis we did use a sequential methodology similar to the waterfall methodology that did included the requirement analysis step to provide an answer to objective a) and objective b). Furthermore, the system design step does allow for the provision of a solution to objective c) and finally the implementation step does provide solution to objective c) and d). from these stated objectives this thesis does provide a solution to our thesis objectives and the thesis goal. The only difference between this thesis sequential methodology and the waterfall methodology is the Deployment phase where this thesis was unable to cover because of time constraint.

Once there are solutions to these objectives a prototype was developed and tested. The lessons learned along with insights from this thesis work will then be used as guidance and foundation for further developments of the said Web-based DSS. Stakeholders to benefit from this thesis are Retailers interested in opening or relocating their stores for better accessibility. Also, authorities interested in decreasing CO2-emissions induced in last-mile delivery, and lastly for consumers who want to minimize the need for last-mile transport.

1.4 The Scope of the thesis

Holistically defining optimal location will not be considered; instead, the optimal location referred to locations that minimize CO2 emission from consumer travel (Carling et al., 2013). A location could be considered optimal for a couple of reasons from both the retailers and consumers perspectives and even the urban planners that would not necessarily reduce CO2 emissions. This thesis focuses on knowing how the Waterfall Methodology can be used to develop a DSS meant for finding optimal locations can help in the reduction of transport consequences with regards to CO2 emissions. This thesis is going to use the case study design to provide an answer to the main research question and its subsequent questions drawn from the research objectives.

The prototype is limited to the Dalarna Region instead of the entire Sweden. The case study research design does allow for a researcher to only focus on an object of phenomena (case) that which can be an individual or an organization (Baimyrzaeva, 2018). All the data used in this thesis came from a secondary source like the Google Maps and Open Street Maps Services.

Due to time limitations for data integration and analysis, this thesis did focus specifically on geocoded data of six selected retail stores network within the Dalarna Region. Moreover, the focus of this thesis is on the transportations by private cars whereby other possible means such as trains, bicycles, and airplane are neglected. However, (Carling et al., 2015) noted that the average Swedish consumer travels 30 km for shopping semi durable and durables which suggested that private cars is the predominant mode of transportation. This is because of time limitation and this thesis did considered the Dalarna Region in Sweden to comprehensively provide the most efficient solution using the available timeline.

1.5 Research Disposition

This thesis is divided into eight sections. Section 1 introduce the main research problem, purpose, objectives, scope, and disposition of this thesis. Section 2 provides related works and derived insights that gives the reader a detailed theoretical base for this thesis research question and the research problem. Section 3 discusses the methodology, input data, methods and software tools employed

during this thesis. Section 4 does discuss data quality and processing. Section 5 comprises of the illustrations of the prototype of the DSS and comprises results from the optimization model employed. Section 6 presents the thesis discussions and outlines of potential directions for future work. Section 7 provides all the limitations of this thesis along with most of the assumptions made, and it also discuss the feasibility of relaxing these assumptions in future works. Section 8 does present the conclusions of this study and its follow by the references and appendix.

2: Related work and derived insights

A study conducted in 2016, proposed that an ex-ante evaluation method can be used to quantify the impacts of CO2 emission induced by intra-urban car mobility based on the different residential plans (Zhao et al., 2016). They found out the further away residence live from a facility and need to drive to access it, the more CO2 (s)he will emit. The study confirms that for intra-urban car mobility distance to directly affect the amount of CO2 that is emitted from a customer travel to and from a facility (Zhao et al., 2016). The study supports the conjecture that finding an optimal location with the help of a DSS should help in the reduction of transport consequences with regards to CO2 emitted from that customer travel to and from that facility. The difference between that study and our thesis is that the former was focusing on any facility that means it could be school, working places or a shopping mall whereas our focus is on a store within a store network.

The study is also similar to our thesis because the researcher uses a case study research design and an empirical method for data analysis. The study also differs from our study in its findings because the researcher provided an ex-ant evaluation method for quantifying the impact of CO2 emissions induced by intra-urban car mobility. On the contrary our thesis is focus on investigating as to how a Waterfall model can be used to build a prototyped DSS that can help retailers find optimal locations for a store they might want to add to their existing stores network within the Dalarna region. The study was selected for review because it does serve as a basis for our CO2 model estimation because it shows that the further away a customer lives from a retail store the more CO2 they will emit when travelling to and from that store. From the study the researcher did conclude that apartment buildings are more effective in meeting residential need and helping in the reduction of CO2 emission than a dispersed single-family home (Zhao et al., 2016).

Another study conducted in 2015 the research did implement a method to empirically measure the difference in carbon footprint between traditional and online retailing that requires data from the entry point to the customer residence (Carling et al., 2015). The method implemented during this study uses the location data on the brick-mortar stores, online delivery points and residences of the region population along with the goods transportation network within the region. The

choice of data during this study does provides an insight and an answer to our thesis objective (a) that is which type of data are needed to build the above-mentioned DSS. From this study the results indicated that the average distance travelled from a consumer home to a brick-mortar store is 48.54 km whereas for distance to an online delivery point is 6.7 km (Carling et al., 2015). This study does inform our thesis by providing the current distance measure between a customer and a brick- and- mortar store this provide the information on as how long it takes a customer to travel to a retail store within the Dalarna region. This information also informs our thesis as to how to calculate for a CO2 emission from a customer travel within the Dalarna region. They concluded their study by stating that e-tailing on average reduces its CO2 footprints by 84% when purchasing a standard electronics product (Carling et al., 2015). The difference between this study and our thesis is that the researcher was measuring and comparing the CO2 emission from a traditional brick- and-mortar stores and e-tailing outlets, and they were not concern with optimization of the locations.

A road network complexity does influence finding optimal locations. A study conducted was able to find out that the an increase in complexity, up to a certain level do improve the solution but once the complexity is beyond certain level it does not improve the solution (Zhao et al., 2019). They also concluded this study with a sensitivity analysis of the algorithms used and the number of facilities to further get insight of the computation complexity and location problems from an intra-urban to inter-urban (Zhao et al., 2019). This is one method that is explored in this thesis because the analysis area of focus is the Dalarna region making it possible to work with intra-urban and inter-urban mobility data for analysis.

According to a study conducted in 2012 shows that the Euclidean distance gives a solution that is 2-7 percent worse than the network distances but due to time this study does consider the Euclidean distance measure to compute origin destination(hereafter OD) distance matrix (Carling et al., 2012). During this research, they also validated that the solutions deteriorate with increasing (P) or the number facilities when locating an optimal solution. From this analysis this thesis is mostly considering the location of at most 2 stores, that which reduces possibility of reducing the solutions of the model.

It is very important to understand mobility pattern when solving location optimization problems. A study conducted on mobility established that human mobility pattern is the same as levy flight pattern this means humans moments are random. The researcher also provided another factor to the agent-based model that already had two factors making it three factors and then simulated results to confirm their observations. The researcher did used GPS traces data of 258 volunteers so that they can get a better understanding of both human mobility patterns and the mechanism (Zhao et al., 2014). The analysis gathered during that research was considered for analysis in this thesis just for an inference purpose.

One major difference from all the above-mentioned studies or research is the lack of a research conducted on how can the Waterfall Methodology be used to build a DSS meant for the above-mentioned task? Providing an empirical answer to the above-mentioned questions makes this research different from all the research conducted within the location science field of studies.

3: Methodology, input data, methods, and software tools

3.1 Description of Empirical Data and Study Population 3.1.0 Data Source Description

This thesis used actual dataset from Statistics Sweden, Open Street Maps (hereafter OSM), Open Street map network (hereafter OSMNX) and Google open maps. Statistics Sweden is mainly responsible to supply users and customers with statistical data for decision making, debate and research. Open Street Maps as its name suggests it is an open-source project that allow for the creation of free editable map of the world. The OSMNX is a python package to retrieve, model, analyse and visualize street network from Open Street Map. This package allows for users to download and model walkable, drivable, or bikeable urban network with just a line of python code and these derived networks are easy to analyse and visualize. Google map is also an open-source map that provides points data for locations and this service is own and operated by Google.

3.1.1 Study Population and Data

The Dalarna region got a total population of 287,966 persons as of 2021 according to statistics Sweden and this region occupy an area of 28,029km2., population density of 10.26/km2 and its annual population change from 2010 to 2020 is 0.38%. The region has 15 major municipalities and majority of the region population does live around the capital Falun that now has a total of 59406 and the next most populated municipality is Borlange that host the total of 52590 persons. The five most populated municipalities along with their populations number are summarized below in a Table 1.

Table 1 Population Summary of the Five Most populated Municipalities in Dalarna.

Name of the Municipality Population in Thousands

Falun 59406 Borlange 52590 Ludvika 26992 Avesta 23323 Mora 20470

The Dalarna region was chosen for this study because of the many transport studies that have been conducted within the region. Specifically the study conducted on the measurement of CO2 emissions induced by online and brick-and-mortar retailing along with many other studies (Carling et al., 2015). This study provided insight as to the road network of the Region and the distribution routes use along with the amount of CO2 that is been emitted by these customers travel to the bricks-and- mortar store or the online purchase.

The Dalarna Road network was generated by the OSMX library before data cleaning the Dalarna Street network which comprises of only drivable roads had a total node of 32515 and of 7857 edges. A node can be referred to as geo points on a map whereas the edges to refer to the links between these nodes. The projection use for this road network data is the World Geodetic System 1984 (GS84) 34N for Sweden. To make sure that this data only consists of drivable roads we converted the graph (nodes and edges) into a Geo Data frames this data does comprises of 6544 observations and seven variables. This road data also consists of 761 roundabouts ,24 jug handle and 10 teardrops from the visualization of this road network in Figure 1 most of the roundabout are in the southern part of the region. The total road length of all the edges in the data is 40,422.300 Kilometres. The highway column had the various types of roads considered in this network that ranges from residential roads up to unclassified roads. Figure 1 is a visual representation of the highway tags column of the road network data.

Figure_ 1 The Dalarna Roads network based on the highway category.

From the above visualization of the Dalarna Road highway column category, most of the road are roads within the residential areas. This does not mean that these residential roads are the most important part of the road network because these roads allow for customers to leave their neighbourhood and get onto the main roads to travel to shopping areas or their various point of interest. The grey segment of this road network depicts unclassified road, and these include both primary links and some residential roads. The light-green segment of this road network represents the primary links that connects the municipalities within the Region.

The retailer’s network data comprises of six different retailers stores locations within the Dalarna region. These six different retailers do operate in the following industries: food retail, clothing retail, general consumer goods and the wooden furniture. These retailers are: Willys, Lidl, H&M, Biltema, RUSTA and IKEA. The retailer location dataset consists of twenty-eight (28) individual chain of stores owned and operated by the above mentioned six retailers. The Willy network in the Dalarna region comprises of eight (7) stores. The next is the Rusta network within the Dalarna region also has six (6) stores follow by Biltema network comprising of three (3) automotive stores. Furthermore, Lidl also have seven (7) groceries store within the Dalarna region while the H&M has four (4) clothing stores within the region. Lastly IKEA has only a store in the region.

Figure 2 provides a graphical representation of these 28 stores dispersion within the Dalarna region using a map generated by Folium a geo python library. The red info- sign icon represents Willys, the blue info-sign icons for Rusta, the green info-sign icons for Baltima, the red cloud icons for Lidl, the blue cloud icons for H&M and the green info-sign icon for Ikea. This data was extracted from google maps by using the name of these retail stores and searching within the Dalarna region. This data was generated to give us an insight as to where most of the store’s network are currently located within the Region and to understand as to if distance was considered during the onset of building these stores.

Longitude

Latitude Figure 2 Pilot retail stores dispersion in Dalarna Region

3.1.2 The Borlange Residence Volunteer Data

The Borlange Residence Volunteer Dataset (Zhao et al., 2017), was used to provide inference on the general mobility behaviour of residence within the region since the Borlange municipality does host the second highest number of inhabitants within the region. Another reason for using the dataset is to help us make sensible assumptions of peoples travel behaviour in terms of their modes of transports and locations of stores within their localities. The data was gathered from a total of 316 volunteers using a Bluetooth GPS device (Zhao et al., 2017). The dataset comprises of the 262,021 records of movements stored in 258 GPS logger files. The dataset had a total of 5402 invalid records that were removed during the data cleaning process, and these invalid data was due to the signal losses. The GPS signal

comprises of each volunteer information on his or her whereabouts every 5 or 30 seconds specifically when a GPS signal was received. The data generated from the GPS had the longitude(x) latitude (y) time(t) and velocity (v). The world Geodetic system 84(WGS84) was used to reference longitude and latitude. The type of GPS device used during this study measurement preciseness was 5m as claimed by the user manual. Furthermore, the velocity unit of measurement is m/s. Analysis derived from this dataset does confirms that people does travel randomly confirming the levy flight research. It was from this study we made some of our assumptions and limitations that will be explained at the latter part of this thesis.

3.2 Methodology and Methods

To provide an empirical investigation in this thesis , so the waterfall methodology was employed to provide an answer to the research question providing answer as to how this methodology can be employed to develop a web-based DSS for finding optimal retail location. (Bassil, 2015). The waterfall methodology was chosen as the appropriate methodology for this thesis because of the web development component of this thesis and the sequential nature of this methodology. We would have used another web development methodology like the Agile web/software development methodology, but the Agile methodology could not fit our team size and the structure of thesis (Murray, 2016). The Agile requires that the solution development goes on regardless of unfinished task which is not appropriate for this thesis because every step needs to be completed before moving to the next step. Since this is an academic work, it is more appropriate to use the waterfall methodology because of its sequential nature. Furthermore, the waterfall methodology does allow for the software requirements, design, code or implementation, test and debug the software, presentation of the finished product and lastly follow by the maintenance mode (Murray, 2016). To practically demonstrates how the waterfall Methodology can be used to develop a web-based DSS for finding optimal retail location we follow all the sequential steps listed to develop this prototype DSS. The next parts of this section are divided in the following parts depicting the sequential way of developing the DSS and these ways are Requirement Analysis, System Design, Implementation, Testing, Deployment and Maintenance.

The Requirement Analysis step provides a detailed explanation on the system analysis and design. A requirement analysis was administered at the beginning of this thesis to ensure that this project meets all the above listed objectives. Specifically in the development of a DSS that reduces distance travel by customer from their homes to a retail store. As indicated earlier, the optimization of store locations does reduce the amount of CO2 that is emitted from a customer travel to and from a brick-and-mortar store(Carling et al., 2013). Our solution will provide an answer to our thesis goal as to how a Waterfall methodology can be used to develop a web-based DSS for finding optimal retail location..

After a numerous derivation of a functional and non-functional requirements the selected ones were summarized, as displayed in Table 2. Since the non-functional requirement is mostly used to help make the system simple, replicable, maintainable and less time consuming for the system designer or basically more useable user interface (Giri, 2010). The functional requirements and non-functional analysis were summarized and placed in the Table 2. This table provides a summary of how the data collection was carryout and identifying important attributes as indicators necessary for the modelling. A geodatabase to host the available data provided from different sources like retailers that will be using the DSS.

Table 2. System Requirement Design Nonfunctional Functional Simple, user-friendly, replicable, automated Model design and development Central storage unit (Data integration center) Geodatabase file design (Population distribution, stores locations, road networks and municipalities positions) Indicator’s identification Preprocessing Data

To provide an answer for objective (a) of our research at the system requirement step we were able to identify the necessary data needed and its availability along with its possible integration. From research the needed data are a geocoded data of where people live in the region, the number of times they travel to the store and along what routes they used, store locations and the road network. The available data from the needed data were the geocoded data of the stores, road network and a not so detailed data of where the Dalarna population live. This insight was then transferred to the next step where we design the system and formulated the model.

3.2.0 System Design and Model Formulations

The system design does handle the requirement specification that is from the first phase to the last phase of the system design. The system design also helps with specifying hardware and system requirement and helps with the definition of the overall system architecture (Bassil, 2015). This system design also comprises of the model’s formulations or the backend toolkit for this decision support system and it user interface. We also design the system architecture that provide an overall insight of the prototype DSS which is visually presented as Appendix E in the appendix section of this thesis. We did consider the Euclidean distance measures to formulate a P-median model that aims at minimizing the sum of the distances between each node (Hakimi, 1964). This choice was based on the fact we could not use the non- Euclidean approach to optimize the facility location selection because we did not have accurate information on customers population distribution within the region. 3.2.1 Model Formulation

P-median model is a location model that can help with finding optimal location of facilities based on an objective function(Han, 2013).From the problem definition since objective (c) of our thesis is to provide an answer as to what models are needed to develop the above-mentioned DSS. We use the P-median model because this model allows for the optimization distances between nodes and edges. Our thesis problem is that retailers are faced with the problem of having to always remodel their store’s locations selection process, whenever they are about to build a new store and now because of the global environmental issues the world is facing.

In this segment of this report we explains the model formulation of a variant P- median model known as the distance-varying P-median model that was inspired by (Kiris, 2014). The reason of choosing this variant of the p-median model is because of it consideration of distance (Zhao et al., 2016). Another reason for choosing this variant of the P-median model because it does use the discrete location model and the facilities and demands are in discrete positions. In this model the goal is to place p facilities to minimize the demand-weighted average distance between a demand node i and the location j, in which the facility is placed. In this model there is no capacity constraints at the facility. In Table 3 the demand nodes are represented by

a set of vertices 푖 휖 퐼, and the possible locations of the facilites are given by another set of vertices j휖퐽. The model parameters are as follow in the table below.

Table 3 Decision Variables and Interpretations Decision Variables

Variables Variable interpretation

i Demand areas (1,2……..n) j Facilities locations (1,2…m) p Number of facilities to be located (1-because a retailer is locating a single store at a time) 푑푖푗 Shortest distance from node i to node j 푥푖푗 This represents the locations allocated to store j yj Facility at site d is located: 1, otherwise: 0 ci The customers demand

The formula below is the objective function that was defined for our model. What this model does is it adds the product between weighted distance of the population and the median allocation to find the 푥푖푗 variables that minimizes this sum of this objective function (Júnior, 2021). 푚 푛

Minimize 푧 ∑ ∑ 퐶푖 푑푖푗푥푖푗−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−(1) 푖=1 푗=1 The next step was the model restrictions regards to optimizing the distance between the demand and the store now exactly p stores are chosen to be built within the chosen locations. 푛

∑ 푦푗 = 푝 − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − −(2) 푗=1

The next restriction is that only one store within a network can be placed into a municipality. This is because all the pilot stores consider for this research only have a single store within a municipality excluding Willys that got two outlets in Falun. Lastly the demand points used in this research is the mid-point for all persons living in each municipality within the Dalarna Region. 푛

∑ 푥푖푗 = 1, ∀ 푖 − − − − − − − − − − − − − − − − − − − − − − − − − − − − − −(3) 푗=1

The third restrictions on the other hand of the equation are to make sure that the sum of the weighted distance (considering the population) is less than or equal to the median distance of the allocated store j. 푚

∑ 퐶푖 푥푖푗 ≤ 푦푗, ∀푗 − − − − − − − − − − − − − − − − − − − − − − − − − − − − − −(4) 푖=1

Finally, these restrictions below are to make sure that the decision variables are binary:

푦푗 ∈{0,1, }∀ 푗 − − − − − − − − − − − − − − − − − − − − − − − − − − − − − −(5)

푥푖푗 ∈ {0,1}, ∀ 푖, 푗 − − − − − − − − − − − − − − − − − − − − − − − − − − − − − (6)

3.2.2 Model Interpretation

The objective function (1) minimizes the total weighted travel cost between a store and demand sites in a municipality. The distance between stores and demand sites is calculated using the Euclidean distance measure based on the Pyomo model used. The Scikit learn package was used to calculate the origin destination matrix (hereafter OD-matrix), i.e., the distance between any pairs in the network, that was used within the model. Constraints (2) and the objective function (1) interpret as each demand d is to be allocated to their closest supply store within the network. Constraint (2) indicates that p stores to be allocated at a given location. Constraints (5-6) states that the location allocation variables should be integer and making it binary (0,1). The model is linear programming optimization model, and these location values are integer values either 1 or 0. The above model was implemented in python using the Pyomo library, an optimization library for solving linear programming problems. Detail’s explanation will be provided in the table summary in the next section of this report. 3.2.3 Emission Based P-median Method

In this study, a new Emission-varying p-median model was derived at by using the p-median model that minimizes transportation cost and in return will minimize the amount of CO2 from transportation activities between stores and demand points(Giri, 2010). Parameters and the objective function of this new model are shown in the equation below: The objective function of the estimated emissions model is to calculate how much

CO2 to be emitted from customer travel when the location of the new store has been optimized.

CO2 estimation model below is the objective function of this optimization model: Minimize E ∑∀푖⊂푆 푑푖 × 푆푖 × − − − − − − − − − − − − − − − − − − − − − (7)

Table 4 Decision variable for the Emission reduction function E: Amount of emission (Kg CO2)

i: Customer’s demand

Si: Number of travelled from demand point i Distance from store to demand point i (km) di: efi Emission factor (Kg CO2/Km)

As per this equation for the emission calculation using a fixed emission-factor the amount of CO2 emission is proportional to travelled distance and the number times a person travelled(Kiris, 2014). The emission factors used in the equation is also determined according to the type of vehicle. As per research it was assumed that people in the Dalarna region mostly drive a gasoline-powered Toyota Avensis 1.8 with a CO2 emissions of 0.15 kg per km, so this means if a customer is driving search vehicle and had to drive 30km to get to a retail so than an estimated CO2 is (0.15*30kg/km)(Carling et al., 2015). It is possible for the emission factor to change or vary based on the type of car a customer is using to go the retail store at the said distance.

To provide an answer to objective b) of this thesis we did design a user interface using JustMind an online platform that help with user-interface design from conception level as its suggested in its name. In continuation to providing an answer to objective b) of our thesis we consider what information is the user going to access and how this information is displayed to the user meaning what kind of visualization to consider. Since from the research we know that most of our retailers does not have the technical know-how understanding how a mathematical model works. We firstly derived a use case using the research problem which a retailer would like to have a web-based DSS that help him or her to remodel or add a new store to their retail stores network within in the Dalarna Region. The user will firstly need to login to a system that they can perform these said task. This means the retailer becomes the administrative user of this system. Once the user login to the system there is an app that allows them to register their network within the system. There is another app that help them to remodel their network or add one more store within the Dalarna Region considering the average distance measure from their store to their customers. Lastly a system does have a site visitor views these user of the system does not interact with the system. They can only view information that are

share by the administrator on this application, but this site also allows for a retailer to create an account on this DSS.

Figure 3 provides a graphical representation of the user interface overview for our prototype web-based DSS for retailers. Figure 3 does provide an idea as to how the entire system is organized ranging from the user interaction with the system up to their exit of the system. The prototype DSS allow for the user to register their network, view their network, remodel their network, and optimize a new store location within the Dalarna Region.

Figure 3 The prototyped DSS overview

3.3 DSS Implementation

According to research the implementation phase of the Waterfall model that which is referred to as the realization of the business requirement and design specifications to get a concrete executable program (Bassil, 2015). This phase of the waterfall methodology also encompasses all the tools used for the solution along with the executable program, databases, website, and the software component. This implementation phase also comprises the actual code where the requirement and blueprints are converted to a production environment. For this project, the following tools and framework were considered for development of the decision support

system and the model implementation. This segment of the report does provide an answer to objective c as to what toolkits are needed for the building this DSS that can help retailers find optimal location for a new store considering distance.

Python- is an interpreted, object-oriented, high-level language with dynamical semantics. It does have a high-level built-in data structures and with a dynamic typing binding that makes it one of the most used for rapid application development. The python syntax emphasizes readability and therefore reduces the cost of program maintenance. During the implementation phase of this thesis the following Libraries in python was implemented:

Table 5 Python Libraries and summarized descriptions Name of python Libraries Summarized Description Fiolum A python library used for visualizing geospatial data Shapely A Python package for manipulations and analysis of planar geometric objects Geopy A python client for several popular geocoding web services Pyomo A python-based open-source optimization modeling language with a vast set of optimization capabilities OSMNX A python package to retrieve, model, analyze and visualize street networks from Open Street map Networkx A python package for the creation manipulation and study of the structure, dynamics, and functions of complex networks.

Django- is a high-level python web framework that does rapid development and clean, pragmatic design (Django Documentation | Django Documentation | Django,

2021). This framework was built by experienced developers, and it relieves a developer the stress of reinventing the wheels and economically friendly because it is an opensource framework. This framework is also secure, fast, and scalable. The Django framework was used to design and implement the user interface for this thesis. This framework uses the Model, view, and controller format. The models do refer to the data, the view is the same as the logic and the template or the controller can also be referred to as presentation. Using this framework, the user authentication was built, and this allow for the addition of a user to the system. Once a retailer is not register to the system it is impossible to use this system. Since this a web-based DSS it is important that a site page is added to the system that has a view for outsiders and as well the site viewers cannot make changes to the information within the system. All of these are done by the robustness of the Django framework.

GeoDjango- is an included contrib module of Django that turns it into a world-class geographic web framework. The GeoDjango can be refer to as an extension or add- on to Django that spatial enabled the Django framework. GeoDjango provides a simpler possible way to create geographic web application, like location-based services and its features are: a. Django model fields for OGC geometries and raster data b. Extensions to Django’s ORM for querying and manipulating spatial data. c. Loosely coupled, high-level python interfaces for GIS geometry and raster operations and data manipulation in different formats. d. Editing geometry fields from the admin

The geodjango which is an extension of the Django framework does provide a robust libraries and plugins on spatial data. This also provide ways for spatial data visualization and analysis. This geodjango also help with the making of geo location application where the analysis is visualized. The geodjango uses the GDAL(Geospatial Data Abstraction library),Geos(Geospatial),Proj.4(a Library for coordinate reference systems, ESR and postGis(open source software that help postgreSql).

QGIS-3_ is a user-friendly Open-Source Geographic Information system (GIS) licensed und the GNU general public License. QGIS is an official project of the open-source Geospatial foundation (OSGeo). This software work on all the most popular operating systems globally. The rationale for using this software was to help with visualization of the location data of the people of Dalarna, brick-and-mortar stores locations and the road network data as well during the development of the above-mentioned system. The qgis tool was used to also convert the spatial data generated for the population grids, locations of the brick-and-mortar stores along with the municipalities data to a comma separated value file for analysis in python.

PostgreSql- is a very effective open-source object-relational database system that has been in existence for the past thirty (30) years and has been actively used. It has over the years gained a very great reputation because of its reliability, feature robustness and performance. In this thesis the PostgreSQL was used to store all the

spatial data that were used for analysis, visualization and for the implementation of the optimization model on the given data. During the implementation segment of this thesis all the above listed tools were employed for the building of this prototype of the DSS. This section of this thesis does provide an answer to objective (b) and objective (c) of this thesis. The testing process has also been implemented by Django because it does automatically generate a testing file that does test the DSS along the development stages. As for the deployment and maintenance it will be discuss in the conclusion section of this thesis.

4: Data quality and processing

This section of the Report provides a description as to what data quality techniques were used for the data consider in this thesis along with how the data were processed for the model implementation. 4.1 Data Pre-processing

Most of the data used in this thesis was secondary data like the municipalities’ geo locations latitude and longitude data. For data quality check the most we could do with it was check the coordinates on the map using the open street or google map and it all fitted. So, any standard error that is accepted for map measurement from these platforms does apply to our data.

The next data that was used in this study was the road network data and we did check for missing data and fortunately there was none because all the nodes and edges in this road network was connected. So, we use this data as it was to project the geo coordinates of the Region municipalities for a better projection. This helps to keep the projection of the municipalities standardized for visualization on the Dalarna region map or the road network map.

This data was used to develop the OD-matrix. As for the six retailers’ data gathered at the beginning of the study, we also cross checked the latitude and longitude points on the open street maps, and it does correspond with the actual areas on the map of these store before considering this data for analysis.

From the visualization of this data as demonstrated by Figure 2, it can be noticed that most of these stores within the region are mostly situated within populated areas of the region. This means most of the people traveling long distances to shop are people living in sparsely populated communities. Another observation of the data is most of the business reasons for stores locations was highly influenced by population. From this analysis certain municipalities within the region does have a store from all the six selected retailers. On contrary some municipalities do not have any of these retail stores. For example, from the six-retail network considered for this thesis they all do have a store in Borlange, Falun, Mora, Ludvika and Malung- Salen and none was found in Leksand and other less populated municipalities. These four municipalities based on data from Statistics Sweden these are the most

populated municipalities within the region and this information is presented in Table 1.

In this thesis, the data use for the P-median model implementation consisted of data generated from the Dalarna region 15 municipalities as the demand points why the municipalities population data collected from statistics Sweden was used as the number of customers at a demand point. Figure 4 is a graphical position of these 15 municipalities within the Dalarna region using these areas latitude and longitude data and projected to the Dalarna Road network. This graph was generated using the centroid points for the municipalities.

Figure 4 Dalarna County Municipalities latitude and longitude dispersion

Figure 1 does provide the summary of the road network data and what type of road composition is the network. The red stars symbols does represents the various municipalities within the Dalarna region. During the analysis of the road network, we noticed that maximum speed limit of all the roads is 100km/hr, and the minimum speed limit is 3km/hr. Figure 5 is a summary data of the speed limit of the roads within the Dalarna Road network data extracted from OSMNX. From Figure 5 the speed limits for each road ranges from 3km – 100km/hr and the international roads does have speed limits from 100km-110km/hr and some had up to four different combinations all ranging from 30km-110km/hr.

Figure 5 Speed limits of the Dalarna Road Network

All the above-mentioned data were considered because these data were used to provide an answer to our research objective a) and objective c) as to what data are needed to develop the prototype DSS and the necessary models and methodologies.

5: An illustration of the prototype of the DSS This section does provide a comprehensive report as to what results were achieved during this thesis and based on the available data gathered along with the models use to provide an answer to our primary research question along with the thesis objectives.

As per the results from the distance-p-median model used during this research we firstly derived a graphical representation of the 15 observations of the centroids of the Dalarna Region municipalities longitude and latitude coordinates data. The centroid can be termed average distance between the points of a location (Deakin et al. 2002). Figure 6 shows the dispersion of the centroids of the 15 municipalities within the Dalarna region.

Figure 6 The Dalarna 15 Municipalities centroid dispersion

Figure 6 is a visual representation of the municipality’s dispersion within the Dalarna Region. With Pyomo library we were able to define the mathematical model presented by Table 3 using the indices of i and j. The next step for our model implementation in Pyomo was we provided the model parameters by using the 15 municipalities centroid points from projecting the municipalities on the road network and then derived the distance matrix and the number of stores that we will want to locate. We also did consider the population factor by creating a weighted distance that does consider the number of customers at a location and also considering the median distance from amongst all of the distances between the points. In this case we firstly consider a retailer that will like to add three stores in the Dalarna region considering all of it municipalities and their various population.

To implement this case, we did equate P to 3 and pass it to the model for implementation. After that we included the decision variables that were described in the Table 3.

Table 6 provides a summary of the model output after taking in the parameters and the data. From this model output it is clear as to whether the model reached a solution along with time it took the model to compile. The total distance between the municipalities and the new store along with the number of restrictions consider by this model. The algorithm use to implement this model is the branch and bound algorithm because the Pyomo library in python does used the GLPK(GNU Linear programming kit) package along. This solver does use the branch and bound algorithm to solve an integer program as our problem is also an integer programming problem. The branch and bound algorithm is a general purpose approach to implicitly enumerate the feasible region (Kianfar, 2019). This algorithm was firstly introduced by Land and Doig for solving integer programming problems (Kianfar, 2019). This algorithm was chosen because it does find optimal solution faster for limited size and the enumeration is done within a reasonable timing. We also notice that as the variable increases the algorithm does takes a lot of time because of the branching tree can become too large.

Table 6 Summary of the Model result

Problem: - Name: unknown Lower bound: 7166547385.721684 Upper bound: 7166547385.721684 Number of objectives: 1 Number of constraints: 241 Number of variables: 240 Number of binary variables: 240 Number of integer variables: 240 Number of nonzeros: 210 Sense: minimize Solver: - Status: ok User time: -1.0 System time: 0.02 Wall clock time: 0.02 Termination condition: optimal Termination message: Model was solved to optimality (subject to tolerances), and an optimal solution is available. Statistics: Branch and bound:

Number of bounded subproblems: 0 Number of created subproblems: 0 Black box: Number of iterations: 0 Error rc: 0 Time: 0.03636932373046875 Solution: - number of solutions: 0 number of solutions displayed: 0

Our next step was to visualize the municipalities that were chosen as median along with allocations. We then generated a graph figure_7 that shows the dispersion of the selected locations for the stores that were chosen and its neighbouring locations that can use the store with regards to distance.

Figure 7 Dispersion graph of the allocated Municipalities

From this analysis of allocating three (3) stores the final tabular result was generated as to providing the allocated locations and the total distance of these new stores. Below is summary of this result in Figure 8 that has the distance (in meters) from these allocated municipalities and to the chosen municipalities. During this thesis, this model does consider how populated these areas are but not their purchasing power.

Figure 8: Allocation result from the model implementation

Figure 8 presents the model allocation result from our optimization model, and it does consider these three (3) municipalities as optimal locations to build the stores considering distance and the population factor. From Figure 8 the Borlange municipality is serving as the median point for eight other municipalities and their average distance travelled are stated in Figure 8. The Falun municipality is serving one(1) municipality and Mora is serving three(3) municipalities. This allocation was done considering the average distance and selecting the median distance point with regards to the population of these municipalities. From Figure 8 Mora was selected even although it was not the third most municipality but because it had a lower average distance as compared to Ludvika or Avesta.

During this thesis we were also able to build an administrative protocol which does allow for an admin to permit addition of retailer to the prototype DSS. This administrative view was built with the help of Django that enable a retailer to able to add a user, network, and visualize the points of their stores on a map generated by leaflet. From this prototyped DSS we were also able to create a system that does has a view site page that serves the public. We also created the optimization application in Django where a retailer is to provide his or her information as to the region of choice and the name of the retailer and their store name and then click the optimization button. This button will than provide the name of the municipality and its longitude and latitude coordinates. In the appendix there is a graphical summary of this decision support system that were developed during the period of this thesis. Figure 9 is the administrative view of our decision support system that does host all the applications within our system.

Figure 9 Administrative Login View

6: Thesis discussion and future work

This thesis does provide an empirical answer to the main research question by providing a prototype DSS that does optimize the location selection of retail stores. As the thesis proceeded we were unable to get all the input data and this we had to scale down our sample size to the region level and specifically we chose the Dalarna region. We than use the 15 municipalities centroid as the demand points for the entire region to fit it into our model. Another crucial aspect of this thesis is the combination of optimization model and web development. We were able to find frameworks that does assist with data integration and data visualization for the implementation of this prototype. This thesis provides a solution to a retailer that likes to optimally locate their new stores within their network considering the average distance between their customers and their store. This thesis also provided a solution that does help with emission reduction from customer travel to and from retail store by selecting locations that reduces the average distance between the demand points.

While found by Carling et al. (2012) and Carling et al. (2015) to be overstated, Hillsman and Rhoda (1978) introduced the errors in localization that may stem from spatial aggregation and crude network distance measures. They term the practice of representing the population of a spatial unit by a single point as a not good representation of the population because the population are located at a disbursed location. This practice normally lead to an eight percent or even more errors(Hillsman & Rhoda, 1978). Unfortunately for this thesis we had to use this technique for the prototype DSS and based of the constraint of time.

As for the insights gathered during thesis we learnt how to formulate an optimization model using the p-median model and then applying it in python using the Pyomo library. The next insight is how to formulate the location data of these stores and generating a distance matrix for analysis. We learnt how to formulate and integrate an optimization model in Django for the web-based component of the DSS implementation. So, from our thesis we learnt that it is possible to integrate an optimization model and web-based application framework to create a web-based DSS for finding optimal locations for retail stores. For the deployment of this system, it can be done using Heroku which is a cloud-based platform that lets

companies build, deliver, monitor and scale-up their apps. It is very compatible with Django and most development done with Django can be deployed on this cloud- based platform. From our thesis we learnt that Django is one of the most secured web application frameworks to use. Finally, we learnt that the more realistic factors are consider as constraints for the optimization model used in this thesis the more robust the model will become, and the more realistic solution will be provided.

Future work is to incorporate the actual location point of the Dalarna region customers to provide more realistic distance medians for the retailers. Another area of improvement will be scaling this prototype DSS to a national level where retailers every part of Sweden can precisely update their stores networks or add another store their network considering their current network. Lastly for data retrieval from the system a future work can be done to allow the retailers download these allocations into an excel file where they can share with their supervisors.

7: Limitations

The first major assumption for our solution in this thesis is that consumer always optimize his/her travel time by using the most optimal routes to visit a retail store, which has been found to be approximately true (Jia et al., 2013). Another assumption made during the development of the prototype about the retailers is that a retailer does consider having at least a store within a municipality. Since we are considering the Dalarna region we did divided the region into it 15 municipalities just for the prototype of the DSS to help retailers within the region find optimal location for a new store they would like to add to their network. The next assumption is that according to laws of location science sites of an optimal multisite pattern must be selected simultaneously rather than independently, one at a time (Church & Murray, 2008). This means its more appropriate for retailers to create their network at once rather than doing it one at a time. From this rationale this version of our prototype does allow a retailer to exclusively consider creating their entire model at once or just a adding a single store to their network.

This model allows for a retailer to add one more store in the Dalarna region or remodel their network considering distance and population. The primary goal of this optimization model is to reduce distance with consideration of the population. This is an integer decision optimization model because we are locating optimal locations and considering 0 or 1 restrictions for a store and considering the median distances.

The limitation of this thesis is that we never consider the levy flight aspect of human mobility. This is to say that people do not leave their homes to only shop because there are several reasons why people travel and sometimes people do combine their travel missions (Solmaz, 2019). This study also did not use the network distance measures which is the more appropriate when optimizing people travel time on roads because most time the roads are not straight line there are curves as well. Another possible limitation of this study is the measurement tools used by the primary data collectors that is the opensource platform that were used during this thesis. It difficult to account for the possible errors that these instruments could have encountered.

8: Conclusions

In this thesis we firstly provided a problem definition and research questions follow by setting up objectives that could lead to providing the necessary answers to our research question and the research problem. To provide an accurate answer to our thesis research investigation we did practically use most steps of the waterfall methodology by structuring our research along the sequential way of the waterfall methodology. We than went to providing answers to our thesis objectives that is from objective a) by listing out the needed data, its availability, type, compatibility, and the ability to integrate the generated data. We did provide an answer to objective a) during the system analysis when we gathered information as to which data were needed for this DSS prototype. At the system design step, we were able to design a user interface providing an answer to objective b) and providing answer to the question which type of information access and visualization this interface should provide. We also provided answer to objective c) by using a sequential methodology like the waterfall methodology and the p-median model. Lastly, we also provided answer to objective d) by providing recommendation as to what platform can be used to deploy the DSS and what needed maintenance this DSS will need for scalability. We conclude that it is possible to build a DSS using the waterfall methodology, that can help retailers find optimal location when they want to add a store to their existing network.

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Appendix

Appendix A: The Registration Form for Retailers

Appendix B: Login view of the DSS

Appendix C: Finding optimal location.

Appendix D Result from finding optimal location using the DSS.

Appendix E System Architecture

Appendix F A link to a short presentation of the prototype DSS https://youtu.be/R4AoUUbO83I