UNIVERSITY OF NAIROBI

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1 UNIVERSITY OF NAIROBI USE OF GIS IN SPATIAL LOCATION OF MILK COLLECTION CENTRES FOR DAIRY COOPERATIVES, GITHUNGURI DIVISION. WANJIRU JOSPHAT NJUGUNA F19/1884/2006 A project report submitted to the Department of Geospatial and Space technology in partial fulfillment of the requirement for the reward of the degree of: Bachelors of Science in Geospatial Engineering May 2011

2 Abstract The Dairy industry is an important part of the food sector and milk collection is a challenging logistics problem that had long been of interest to operational researchers. Advances in information technology (IT) greatly facilitate data collection, manipulation and presentation and these advances facilitate the building of Decision Support Systems (DSS) to support logistics management in the milk collection sector. This study shows how Geographic Information System (GIS) based DSS is used in assessing the level of service, in terms of spatial coverage, Milk Collection Centers (MCC) provide dairy farmers in Githunguri. In addition, the study focuses on planning milk collection routes to minimize travel distance travel time and travel cost in transportation of milk from MCC to the processing plant. A road network assigned assumed travel speed was used to carry out service area analysis and vehicle routing problem analysis (network analysis types). The service areas created buffers around each MCC based on specified time traveled by farmers along existing road network. The numbers of farmers covered by the spatial extent of the service areas were determined and a comparison made against existing number of farmers in each MCC. It is found that as time travelled reduces, service area reduces and the number of farmers covered reduces. Allocation of farmers therefore, to MCC, should result from the consideration of the spatial extent to which MCC can serve in an area so that to minimize time travelled by farmers hence contributing minimum time possible to the overall time necessary in milk collection process. Vehicle Routing Problem (VRP) analysis on the other hand resulted to optimized routes. Various constraints- milk capacity of tankers, time windows and order count, - were considered to generate and map the optimized routes. Four feasible routes were created and their comparison in terms of Cost, Time and Distance were mapped. A pick up density map was made depicting relative round optimization of the four feasible routes. Such an analysis discussed in this project can help a scheduler plan efficient and effective routing of Milk Collection Tankers. VRP analysis in GIS therefore, should be used as an effective tool in logistic management not only in dairy industries but also in applications that require transportation services such as Collection of household waste, goods distribution, mail delivery etcetera. ii

3 Dedication Dedicated to my Mother Loise Wanjiru who has sacrificed much to bring me up to this level. iii

4 Acknowledgements First of all, I would like to thank the almighty God who gave me the opportunity to peruse my studies at The University of Nairobi. My thanks and heartfelt appreciation goes to my supervisor Mrs. T. M. Njoroge for her valuable guidance, suggestions and positive criticism during the execution of this project. I would like also to thank all management and staff members of Githunguri dairy cooperative especially Mr. Kennedy Musakali, The Quality Assurance and Extension Service Manager and Mr. Samuel Mbugua Kihara, Data Controller. My sincere acknowledgment goes to the members of the dairy cooperative and key informants in Githunguri for devoting their valuable time during interviews. I am highly indebted to my parents, grandparents and sister (Lucy wangari) for their consistent encouragement throughout my study. In addition, I wish to thank my friends Edwin Kimwaki, Edwin Muhoro and Dennis Theuri for their support. I am also indebted to all my classmates in the department of Geospatial and space technology at The University of Nairobi for making my stay pleasant and memorable. Last but not least, my special thanks go to Arthur Kariuki and Frederick Mwai for their genuine encouragement and assistance throughout my study. iv

5 Table of Contents Abstract... ii Dedication... iii Acknowledgements... iv Table of Contents... v List of Figures... vii List of Maps... viii List of Tables... ix List of Appendix... x Abbreviations... xi 1 CHAPTER ONE: INTRODUCTION Background of the study Milk Collection Centers and their Location Background to Milk Collection Problem Statement Objectives of the Project Scope and Limitations of the Study Report Organisation CHAPTER TWO: LITERATURE REVIEW Cooperatives Marketing Cooperatives Dairy Cooperatives Githunguri Dairy Cooperative Milk Collection System Door-to-door Milk Collection System System of collection centers Bulk conservation of cooled milk by the producer and transport in tank truck to dairy plant Milk Pick-Up Rounds Milk Collection System in Githunguri Dairy Cooperative The Scheduling and Routing Problem Geographical Information System GIS and Milk Collection System Network Analysis in GIS v

6 2.7 Global Positioning System (GPS) Geo-positioning Basic Concepts GPS Component and Basic Facts GPS in Milk Collection CHAPTER THREE: MATERIALS AND METHODOLOGY Area of Study Data sources and Tools Data sources Tools Overview of the methodology Data Capture Data Preparation Data Extraction Digitization Data Editing Creation of network dataset Service Area Analysis Service area analysis layer Analysis settings Vehicle Routing Analysis Setting Analysis Properties CHAPTER FOUR: RESULTS AND ANALYSIS Githunguri Digital Maps Distribution of Members and MCC for Githunguri Dairy cooperative Database Network Dataset Service Area Coverage of MCC VRP Results and Analysis Vehicle Routing Solution without route Zones Vehicle Routing Solution with route Zones SUMMARY, CONCLUSIONS AND RECOMMENDATIONS Summary and Conclusions Recommendations REFERENCES AND BIBLIOGRAPHY APPENDICES vi

7 List of Figures Figure MCC in Githunguri Figure Data flow in milk manager system Figure A Part of Githunguri Road Network Figure GIS data capture process Figure Georeferencing Procedure Figure Mosaicked Topographic maps Figure Georeferencing Process Figure GPS hand held receiver used for the study Figure Digitizing errors Figure Graphical editing for digitizing error Figure Network Datasets Figure Service area analysis settings Figure Relationships of Network Analysis Classes Figure VRP analysis Layer and Analysis Window Figure Orders (MCC) and properties of Kambaa MCC Figure The properties of Depots Figure Route and its Properties Figure Analysis Settings Figure Part of Githunguri Road Network Figure Layers of Service Area coverage Figure selecting a polygon and the corresponding MCC Figure A Graph of number of members in a 15-minute service area Figure A Graph of number of members in a 30-minute service area Figure Solution of VRP without the route zones Figure Attributes of Route A Figure Route Zones and properties of zone A vii

8 List of Maps Map Area of Study (Githunguri Division) Map Map of Githunguri Division Map Active Member Density Distribution Map Distribution of Milk Collection Centers Map Service Area Coverage Map Map showing 15-minute Service area coverage Map Member Distribution within a 15 minute Service Area Map Route Map Map Route Zone Map Map Optimized Routes Map Route Map Showing Transit Distance Map Route Map Showing Transit Time Map Route Map Showing Milk Capacity per Route Map Route Map Showing Total Cost Per Route Map A Pick Up Density Map viii

9 List of Tables Table Data sources Table Coordinates of Milk Collection Centers Table Attributes of a Dry Weather Roads Table attribute table showing number of members covered per region Table Constraints in the Various Routes Table Attributes of Milk Collection Centers Table Summary of active members in a 15-minute Service Area Table Summary of Active Members in a 30-Minute Service Area Table Milk Groupings and their Sequence in Each Route Table Summary of attributes of the resultant routes Table Zones and their properties Table Attributes of the final optimized routes ix

10 List of Appendix Appendix A: Attributes of Milk Collection Centers for Githunguri Dairy Cooperative 92 Appendix B: Attributes of All Weather Roads Appendix C: Attributes of Dry Weather Roads Appendix D: Attributes of Main Tracks Motorable Appendix E: Attributes of Foot Path Appendix F: Milk Weighing and Recording at Gathaithi Milk Collection Center Appendix G: Milk Collection in Progress at Kambaa MCC in Githunguri x

11 Abbreviations AI Artificial Insemination DGPS Differential Global Positioning System DSS Decision Support System ESRI Environmental Systems Research Institute FURP Fertilizer Use Recommendation Project GCP Ground Control Point GPS Global Positioning System ICA International Cooperative Alliance ILRI International Livestock Research Institute KDB Kenya Dairy Board KNBS Kenya National Beaural of Statistics MCC Milk Collection Center MCT Milk collection Tanker MoLD Ministry of Livestock Development RMS Root Mean Square USAID United States Agency for International Development UTM Universal Transverse Mercator VRP Vehicle Routing Problem. WGS World Geodetic System xi

12 1 CHAPTER ONE: INTRODUCTION Kenya has one of the largest dairy industries in sub-sahara Africa. Developments in the industry span over a period of 90 years up to 2005 and have undergone various evolutionary stages. In the first 60 years, it was dominated by the large-scale farmers, while in the last 30 years smallholder 1 farmers have increasingly dominated the sector (Dairy Industry In Kenya, 2005). Secondly, it has evolved through three market periods: For the period up to 1969 it operated as an open market with various independent dairies being active market players Between 1969 and 1992 and primarily due to the rationalization of the dairy industry by the Government, a monopolistic market situation was created From 1992, the Government liberalized the industry. It is the most well developed of the livestock sub-sector contributing about 6% GDP, and is practiced in the medium and high rainfall areas mainly in the Central highlands, Rift Valley, Eastern and Coastal lowlands (MoLD, 2006). Out of the estimated national dairy herd of 3.5 million, smallholders own 80% and control over 80% production and over 80% of the marketed milk (Muriuki, 2003). Majority of these small scale farmers are concentrated in the Central Highlands and own 1-5 cows(usaid, 2008). These farmers market milk either through the formal or informal market. The informal market comprises direct deliveries to consumers, or through intermediaries such as traders or sometimes through cooperatives. This channel accounts for about 85% of marketed milk. Only 15% of marketed milk flows through the formal market via cooperatives and processors(thorpe, Muriuki, Owango, & Steel, 2000). As milk production occurs in the countryside away from the urban consumption areas, the ability to deliver milk quickly and at minimal cost and spoilage to the urban market or processing plant is of utmost importance to the dairy farmer. The farmers major concern in milk marketing is, therefore, the development of marketing channels that minimize losses and maximize returns. 1 is someone who has 1 to 5 crossbred cows and usually occupies less than 0.5 ha of land and represents the less commercially managed dairy systems in the area. Page 1

13 1.1 Background of the study Milk Collection Centers and their Location Figure MCC in Githunguri. Milk Collection Centers (MCC) are normally placed at shopping centers in the rural areas where farmers transport raw milk after each milking time to be cooled in bulk tank or collected in small tanks (milk churns) which are later fetched by road tankers and transported to the processing plants. Kiosks are also set up away from shopping centers at convenient places to act as MCC Background to Milk Collection In the rural areas farmers resort to a wide range of transport means including hired vehicles, matatus, bicycles, carts and even donkeys. In many cases, they deliver the milk on foot over long distances of up to 10 km or more to a collection point, cooling plant, co-operative society, processing factory or directly to consumers. The constraints imposed by the technical characteristics of milk determine the nature of the entire milk collection and delivery infrastructure, including road quality, length of the milk collection routes, and location of Milk Collection Centers and cooling facilities. It is inevitable that infrastructure plays a critical role in milk collection. The perishable nature of milk imposes the need for adequate and clean water for cleaning equipment such as milk cans, while the long distance (often on rough roads) to the collection centers, cooling plants and processing factories creates the need for sound feeder road network that is also well maintained. Transportation costs are always a significant component of total cost for a company where the movement of raw material or product is required. Major components of this cost include the labor cost of the drivers, the cost of fuel, and the cost of the trucks. These costs are especially important where perishable products are being transported and specialized handing is required. These conditions frequently arise in Page 2

14 the transport of agricultural products, and especially in the handling of dairy products. Significant transportation problems arise in both the collection of milk from farmers and the distribution of finished products to shops. The cost of delivery of these products is a key issue (Adenso-Diaz, González, & García, 1998; Tarantilis & Kiranoudis, 2002) which has something in common with other delivery applications. On the other hand, bulk milk collection is a distinctive logistics application and one where major transport costs are incurred. In most countries, milk is a scarce commodity and within a region, dairy companies attempt to attract as many farms as possible to supply them with milk. The most attractive inducement that a dairy can offer a farmer is a competitive price per litre for the farm's milk output. Rival dairy companies regularly attempt to poach farmers from other dairy companies by increasing prices per litre. The history of the dairy industry contains many examples of so-called Milk Wars. Marketplace competition for dairy products, and similar levels of efficiency in modern milk processing plants, ensure that the dairies cannot profitably operate if the cost of a litre of milk arriving at the processing plant is out of line with others in the industry. The cost of milk has two components: first, the cost of transport, and second, the cost of paying the farmer for the milk (Burtler, Herlihy, & Keenan, 2005). Given that the dairy has little scope for allowing the total cost to increase, then any increase in the price per litre paid to the farmer must be compensated by a reduction in the cost per litre of collecting the milk. An efficient transport operation can allow a higher milk price to farmers, in turn attracting higher volumes that can lead to further economies of scale in milk collection. Because a reduction in transport cost can improve the price per litre that a dairy can offer its farmers, dairy companies have consistently attempted to adopt cost-reducing initiatives. These have included introducing larger capacity collection vehicles and longer working days for the drivers. 1.2 Problem Statement Milk is a perishable commodity and any delay in the delivery from the farmer to the processing plant may cause milk quality to deteriorate. The spatial distribution of Milk Collection Centers (MCC) and the number of dairy farmers subscribed per center result to some of the farmers traversing long distances. Apart from the delay caused by the long distances, farmers suffer from exhaustion. The spatial aspect of allocating new dairy farmers to the existing Milk Collection Centers is not considered Page 3

15 in the appropriate manner due to lack of a system to determine the spatial distribution of Milk Collection Centers and the spatial extent to which it (MCC) can optimally serve the dairy farmers. As a result, farmers end up being allocated to Milk Collection Centers that are far from their homes and therefore the time and distance taken to walk to a MCC increases. Dairies on the other hand, have always been interested in the ability of schedulers to design a cost efficient set of routes, which allocate MCC to trucks in a way that minimizes the total cost of milk collection and the time of milk delivery to the processing plant. The demand from dairy management therefore is for a milk collection system that they believe to be the most efficient and cheapest possible. This study therefore, focuses on using Geographic Information System (GIS) in determining and analyzing spatial distribution of MCC first to determining, spatially, service provision to the subscribed members at each MCC and secondly routing of Milk Collection Tankers to enhance effectiveness and efficiency of the milk collection process for Dairy Cooperative. 1.3 Objectives of the Project The overall objective of this study is to demonstrate the application of GIS in analyzing the spatial distribution of Milk Collection Centers with aim of routing Milk Collection Tankers to enhance efficiency and effectiveness in the milk collection process. Specific Objectives To depict the spatial distribution of Milk Collection Centers. To determine the number of members 2 that can be served, spatially, by each center s service area coverage. To determine optimum route(s) for Milk Collection Tanker(s). 2 Dairy farmers who are members to Githunguri Dairy Cooperative Society. Page 4

16 The results should aim at achieving the following. Generate digital maps showing the spatial distribution and service area coverage of Githunguri s Milk Collection Centers. Provide a system to generate optimum routes during a milk collection process. 1.4 Scope and Limitations of the Study The study is limited to the use of GIS functionalities to generate results and to address the objectives mentioned, which is to investigate the level of service, in the spatial sense, MCC provide dairy farmers in Githunguri division and in addition determining optimum routes for routing Milk Collection Tankers. The study focuses on one dairy cooperative, Githunguri dairy cooperative society, located in Githunguri division of Kiambu district, Central province of Kenya. 1.5 Report Organisation This project consists of five chapters. Chapter one deals with introduction to dairy industry, background to the concept of milk collection, problem statement, objectives, scope and limitations of the study. Chapter two reviews literature related to dairy cooperatives, milk collection systems and routing. Methodological issues including the study area description are presented in chapter three. The fourth chapter presents the results of the study and their interpretation. The final chapter summarizes the project, concludes and presents the recommendations. Page 5

17 2 CHAPTER TWO: LITERATURE REVIEW 2.1 Cooperatives A cooperative can be defined as an autonomous association of persons united voluntarily to meet their common economic, social and cultural needs and aspirations through a jointly-owned and democratically controlled enterprise (International Corporative Alliance (ICA), 1995, p. 15). As per ICA (1995), Cooperatives are based on the values of self-help, selfresponsibility, democracy, equality, equity and solidarity. In the tradition of their founders, cooperative members believe in the ethical values of honesty, openness, social responsibility and caring for others. The cooperative principles are guidelines by which cooperatives put their values into practice. These are: Voluntary and Open Membership Democratic Member Control Member Economic Participation Autonomy and Independence Education, Training and Information Cooperation among Cooperatives Concern for Community According to Stirling (2006), the United Nations estimated (in 1994) that the livelihood of three billion people was made more secure by cooperatives. At least 800 million are members of cooperatives and 100 million are employed by them (ibid). In his early writing, Robert Owen has undoubtedly mentioned the soul of cooperatives as follows, There is but one mode by which man can possess in perpetuity all the happiness which his nature is capable of enjoying that is by cooperation of all for the benefit of each. Page 6

18 2.1.1 Marketing Cooperatives A marketing cooperative is an organization owned and operated by a group of farmers who produce similar products. Farmers join a marketing cooperative to gain more control in marketing their products so they can increase the price they receive for their products, reduce the costs of marketing for their produce and for obtaining agricultural inputs such as seed and fertilizer; and make the market for their goods more secure(tsehay, 1998). The marketing cooperative accomplishes these objectives by performing certain functions such as processing, packing, storing, cooling, shipping, promoting, and selling; negotiating for better market terms because of volume and variety offered by their members; and buying production supplies (seeds, fertilizer, animal feed, containers, etc.) in large volumes at lower prices Dairy Cooperatives Dairy co-operative societies are registered under Section 11 of the Co-operative Societies Act Cap (490), Laws of Kenya. In addition, the Kenya Dairy Board (KDB) issues various categories of license to dairy co-operative societies depending on the predominant activity and products sold. Some are licensed as milk bars while others are licensed as producers or mini-dairies. Over the years, the co-operative movement has played an important role in agricultural production and marketing. They have been particularly instrumental in the main milk surplus areas of Central Kenya in collection, bulking and sale of farmers milk to either processors or local consumers. Through bulking, the cooperatives have been able to reduce the cost of milk marketing and have thus realized higher returns for farmers, but perhaps more importantly, provide a stable and reliable outlet for milk. Currently, it is estimated that over 200 dairy co-operatives and self-help groups are actively engaged in milk marketing. Page 7

19 2.1.3 Githunguri Dairy Cooperative Githunguri Dairy Farmers Co-operative Society was started in 1961 with a membership of 31 smallholder dairy farmers. It is located in Kiambu, Central Province, and 50 km from Nairobi City. The Society was formed as an initiative to help the smallholder dairy farmers of Githunguri Division, to market their milk (Muriuki, 2011). From an intake of 24,000 kg of milk a day and an annual turnover of Kenya shillings (Kshs.) 211 million in 2004, it today collects about 105,000 kg of milk a day from a membership of 12,000 small farmers, which represents 12,000 households assuming each farmer comes from a single household. According to Kenya National Bureau of Statistics (KNBS), Githunguri Division has 39,350 households therefore it means that the cooperative support 30.5% of the population. The cooperative has an annual turnover of over one billion Commercial activities and benefits to members Milk collection, processing and marketing is the core activity for Githunguri Society but it also provides other services such as input supply stores (mainly feed) and Artificial Insemination (A.I) services for members. The stores also provide food for the member s family, which brought gender empowerment as men no longer squander scarce family resources as they used to as families access them first through the stores hence improved motivation on the family to produce more milk. Services to members are not only reasonably priced but are also offered on a credit basis. Being able to source for goods and services from their own cooperative society also contributes to the consolidation of the farmer member s wealth. According to the Society Manager(Muriuki, 2011), advantages of dairy enterprises as compared to other agricultural enterprises such as coffee, tea, banana, are dairy provides higher income per farm unit; the income inflows are continuous and consistent; the society guarantees prompt payment; and the members are guaranteed steady income. The society payout to farmers depends on the milk and milk product (yoghurt, butter, ghee and cream price income) sold for the period. The payout has been on average Ksh per kg of milk for 2011(Muriuki, 2011). Page 8

20 2.2 Milk Collection System In most of the dairy producing areas, milk collection is organized along collection routes. Individual farmers deliver the milk to the pick-up point or marketing agents collect the milk directly from the farms. At the milk collection stage, both aluminium and plastic containers are used. Smallholder farmers prefer to use plastic containers citing their low cost and convenience. However, in large-scale areas, where large quantities of milk are handled, most farmers use the aluminium cans. At the pick-up point or collection center, tankers collect milk and transport it to the processing plant from which other products of milk are processed Door-to-door Milk Collection System In the simplest, door-to-door milk collection system, the milk truck stops at each farm, loads the milk cans and leaves an equal number of cleaned, sterilized empties. This is a low-yield pick-up method, slow and interrupted by numerous stops. The transport vehicles wear out fairly quickly; the route may include bad roads. Also, the full carrying capacity of the trucks is not used because the milk cans, which come from different farms, are not all filled up to the same level. Milk collection under such conditions is inevitably costly. Though it may be irrational, it is the only feasible method in many small dairy farms located in remote areas throughout the countryside. Pick-up of uncooled or barely cooled milk in cans requires strict adherence to rules governing the duration of the collection period and the distance covered. Failure to observe these rules can easily alter the quality of the milk delivered to the dairy System of collection centers The method described is no longer valid in areas where people live close together or where small farms are grouped around villages. The best system in such cases is a collection centre to which farmers deliver milk every day, or preferably morning and evening, using their own means of transport. This system facilitates pick-up enormously as the milk truck loads the entire output of a village or milk producing area at a single stop. The time saved means that greater distances can be covered. Page 9

21 The structure of the collection centre may vary greatly depending on whether the milk is shipped from the centre to the dairy in bulk or in cans. a. The milk may remain in the cans of individual farmers and the centre limit its role to providing a central pick-up point and cooling facilities. The milk delivered by individual producers will continue to be accounted for at the receiving platform of the dairy plant and the cans will be washed and sterilized in the plant. The disadvantage is that the milk truck loads a larger number of cans, some of which will not be filled to the brim. b. The milk may be mixed at the collection centre. In this case, the centre is responsible for keeping the accounts with individual milk producers. The centre may also wash and sterilize cans not to be returned to the plant. The centre may sometimes transfer the milk from the initial cans into larger-capacity pick-up cans. This type of centre is not advisable for handling amounts exceeding 1000 litres/day. c. For centers handling larger quantities of milk, the slow and fairly laborious practice of keeping milk in cans gives way to bulk holding. In this case the centre's equipment will consist of a milk reception and weighing station; a milk cooler to lower milk temperature to some +4 C; insulated or refrigerated storage tanks for cooled milk; pumps and piping to distribute and transfer milk and a station with hot water, detergents and disinfectants for cleaning apparatus and milk producers' milk cans Bulk conservation of cooled milk by the producer and transport in tank truck to dairy plant. This system of collecting milk is where dairy farmers, mostly farmers that produce milk in large quantities, store milk in cooling tanks which is later fetched by milk tankers and transported to the processing plant. This system has the following advantages: Milking can be organized separately from milk collection, thus freeing the producer from delivery timetable constraints. Page 10

22 The milk is cooled rapidly after milking which enhances its bacteriological quality. The longer-keeping stored milk can withstand a longer collection period which means the pick-up and delivery round can also be lengthened. Pick-up can be made every two days where milk quality permits (though a three-day pick-up is definitely to be discouraged as the risk of psychrotrophic flora is too high). In technical terms, however, bulk collection is conceivable only under the following circumstances: The milk producers must have enough business to justify purchasing or otherwise making available a cooling tank. This also presumes a rural power network sufficiently developed and reliable to accommodate the power demand from cooling tanks without excessive risk of blackouts. The milk in each tank must be of good quality to avoid any risk of contaminating the milk in the tank truck. The tank truck must be able to back right up to the cooling tank (maximum distance 8 15 m). In areas accessible only to small trucks working with milk cans, bulk pick-up with large-capacity, heavyweight tank trucks is simply not feasible. It should be remembered that all-zone bulk pick-up would rarely be possible. The first consideration in choosing a milk collection system should be to maximize both technical and economic advantages. Where possible, the collection area selected should lend itself to gradual expansion. In a uniform collection zone, the dairy plant can set up a single collection system. Unfortunately, in the Third World, the dairy industry is frequently part of a development process, which includes milk producers of all sizes, and so the plant will have to adopt a mix of approaches: In a specific collection zone where large milk producers are in the majority, the producers are equipped with cooling tanks, and pick-up is by tank trucks. Tank trucks may have an additional rack for milk cans from small producers. Barring Page 11

23 that, the milk produced by several farmers will have to be picked up at a central collection point. A combination milk can/refrigerated tank truck pick-up is risky because of possible contamination and overheating. Where small producers are in the majority, the plant may have tank trucks to collect milk from several collection centers as well as from one or more large producers equipped with cooling tanks. As they work out their refrigeration and collection costs, some dairies may even have to contemplate the following option for areas where access is difficult: a. Setting up a collection centre b. Small-scale or cottage industry, for cheese manufacturing, located at milk production sites as a means of reducing transport costs. 2.3 Milk Pick-Up Rounds Whichever pick-up system is chosen, the cost of milk collection is clearly almost entirely dependent on: The truck; Labour. The goal of sound management should therefore be to minimize these two major cost items. The first thing to do is to optimize milk pick-up rounds and then: identify the number and size of trucks needed; Minimize the working hours of the milk collector whilst assuring the delivery of high-quality milk. To optimize milk pick-up rounds, the milk collection area must be mapped out. This involves: Classifying collection sites by village, hamlet, isolated farm or collection centre. Classifying the number of milk producers at each site in accordance with maximum milk output per producer. Where necessary, milk quality may also be indicated. Page 12

24 Totaling peak and slack period milk totals for each collection point. Listing the peak load of the various types of trucks or tank trucks available on the market. With these elements in hand, several milk round models can be tested. At this point, there is a temptation to reduce rounds to the minimum number by lengthening their duration, using bigger trucks and having longer intervals between pick-ups. At this stage, economic factors cease to be the sole consideration. Depending on milk quality and the collection system selected, the maximum interval between two pickups (daily or every two days) is the next thing to be worked out, remembering that pick-up frequency has a major bearing on costs. In fact, in two-day pick-up service, more milk is collected, necessitating bigger trucks and closer pick-up points. Any attempt to maximize the length of milk rounds is limited to how long the milk can be kept without cooling it: this is particularly true of milk cans. This interval depends on how big and fast the truck is and on milk production density within the pick-up area. Maximum round length may also be limited by the following factors: No round can exceed the maximum number of hours the driver is legally allowed to work each day. The plant may be organized in such a way that milk rounds need to end at specific times during the day. Timetables of access to farms. Highways or roads that can only be used during the day, or at certain times of the day. The above limiting factors on the length of milk rounds need also be considered in determining truck size. One practical way of deciding which of the different round patterns tested is more attractive, not forgetting the above imperatives, is to compare them as follows: The total mileage for each round is divided into the active sector, i.e. the mileage between the first and last milk producer (kr) and the downtime zone, meaning the Page 13

25 distance between the dairy and the first producer and between the last producer and the dairy (kt). The number of litres of milk picked up daily per kilometre of active distance corresponds to what is often designated as production density or technical density. The number of litres of milk picked up daily per kilometre of active distance plus downtime zone: which constitutes the pick-up density. Round optimization makes it possible to increase pick-up density without increasing vehicle size. The direction of the pick-up circuit should permit the maximum average truck speed and the shortest standing time for milk. Two plants who wish to optimize their milk rounds should avoid overlapping and superimposed pick-up zones. Rounds have been worked out manually, and often very cleverly by professionals since the beginning, but today the use of information technologies and scientific estimates can do a quick, flexible, highly effective job. 2.4 Milk Collection System in Githunguri Dairy Cooperative Githunguri Dairy Cooperative Society uses System of Milk Collection Center which has a mixture of two methods based on type of Collection Centers. They include: Permanent Milk Collection Centers Mobile Milk Collection Centers Milk is collected twice a day in the morning at 5:00 am to 7:00am and 2:00pm to 4:00pm in the evening. Permanent Milk Collection Centers Under this type of milk collection system, Milk Collection Centers are set up in permanent structures. This particular system is employed mostly on the eastern part of Githunguri Division. Mobile Milk Collection Centers In Mobile Collection Centers, temporary centers are set up at the time of collecting milk. There are no permanent structures erected to serve as centers and therefore Page 14

26 after every collection of milk the instruments used are taken back to the Dairy Cooperative. This system is largely employed on the western part of Githunguri Division. The centers utilizing this type of system are six in number. They include Mathanja, Kaminditi, Munandaini, Gitombo, Lioki and Raiyani. The other fifty-eight MCC use the former type of Collection Centers. 2.5 The Scheduling and Routing Problem Bodin and Golden (1981) describe the difference between vehicle routing and vehicle scheduling. They define vehicle routing as a sequence of pickup and/or delivery points through which the vehicle must travel, beginning and ending at the depot point. The vehicle schedule is defined as a sequence of pickup and/or delivery points related to departure and arrival times. When the times are not specified in the problem, the problem is a routing problem. When times are fixed in advance, the problem is a scheduling problem. The purpose of all scheduling and routing is the same: to find the optimal assignment of the vehicle fleet to serve the demands of the pickup and/or delivery points. The difference between the scheduling and routing problems depends on a problem s characteristics and assumptions(kilmer & Prasertsi, 2004). For instance, consider the problem of picking up goods from farms by a truck fleet. If it is assumed all farm products can be picked up any time, then time constraints may be ignored. This is an example of a pure routing problem. However, if the arrival or departure times are important, the inclusion of time window constraints will be necessary. Page 15

27 Figure Data flow in milk manager system Source: (Burtler, Herlihy, & Keenan, 2005) The state of the art in tour routing and planning has evolved surprisingly. Once, these tasks were done by logistics departments. Existing automated tour planning systems were very difficult to generalize and apply to other situations, even being very similar to that for which they had been developed. Afterwards, as computer calculus and memory storage capacity increased, better solutions to routing problems appeared (Gonzalez & Machin). Characteristics of a Routing Software The quality of the software for designing and optimizing routes evolved significantly in scope and functionalities, given the need of the majority of transport companies to reduce costs, both for delivery and collection. The experience in various companies using automated tools reduced freight costs by 10 to 15%(Gonzalez & Machin). High freight costs have centered the attention of managers and planners to build effective controls and continuous process improvements. Routing and planning systems are of significant help when developing these tasks. The key concept in a routing software is a tool that works satisfactory in real situations and in simulation, enabling to work with fictitious scenarios where some Page 16

28 parameters could be changed in the system and observe possible influences in costs, collecting times, etc. Routing software such as ArcGIS integrates friendly window interfaces and controls where users can: Modify geographic routes and transport costs per kilometer. Simulate open and close of processing plants. Add new transports companies or join existing transport companies Make milk collection planning for a specific day. Compare calculated results with real results Redesign routes assigned to each tank, Create tours passing by processing plants, dairy farms or desired points. Make new planning for 24 or 48 hours. These tasks aid the planning user to improve evolutionarily the costs involved in the collection, based in his know how. 2.6 Geographical Information System Elements of a GIS include data and information technology (i.e., computers, software, and networks) to support it. Spatial data include any data that have a geographic location. In its totality, a GIS can be viewed as a data-management system that permits access to and manipulation of spatial data and visual portrayal of data and analysis results GIS and Milk Collection System An efficient and effective milk collection system requires an understanding of the spatial distribution of Milk Collection Centers in relation to both the farmer and processing plant. This will utilize Geographical Information system- a computer based information system that provides the following sets of capabilities in the handling of the spatially referenced land related data and information. Data input Data management, which involves storage updating and retrieval Data manipulation and modeling Page 17

29 Data information and output Data contained in GIS for milk collection system include digital maps, a record of MCC (showing milk intake and active members), record of geographic coordinates of MCC and processing plant. GIS in Milk Collection System for this particular study will involve the use of network analysis. This will utilize networks created within ArcGIS called network datasets to solve the problem of efficient routing of Milk Collection Tankers across the spatially distributed Milk Collection Centers Network Analysis in GIS Network analysis in GIS is often related to finding solutions to transportation problems. In GIS, the real world is represented by either one of two spatial models, vector-based, or raster-based. Real world networks, such as a road system, must be modeled appropriately to fit into the different spatial models. Even though the models differ, the solution to different transportation problems in either raster or vector GIS uses the same path finding algorithms. A considerable number of network analyses can be performed using the network analysis tool in ArcGIS. They include finding the best route, the closest facility, creating an OD cost matrix, solving a vehicle routing problem and service areas analysis. This study will utilize two types of network analysis. Service Area analysis: This determines spatial coverage of facilities. Vehicle Routing Problem: This determines optimum routes. Network A network is a system of interconnected linear features through which resources are transported or communication is achieved. The network data model is an abstract representation of the components and characteristics of real-world network systems. In ArcGis, the geodatabase has two core network models. The network dataset, optimized for undirected flow (especially written for transportation), and the Page 18

30 geometric network model, which implements directed flow systems for activities (such as river networks and utility lines). Connectivity is inherently important in order to travel over the network. Network elements, such as edges (lines) and junctions (points), must be interconnected to allow navigation over the network. Additionally, these elements have properties that control navigation on the network. The following is an example of a part of the transportation network in Githunguri displaying roads of different types; all weather roads, dry weather roads, main track (motorable), footpath and junctions at all the intersection nodes. Figure A Part of Githunguri Road Network 2.7 Global Positioning System (GPS) The Global Positioning System (GPS) is a burgeoning technology, which provides un-equalled accuracy and flexibility of positioning for navigation, surveying and GIS data capture. The GPS NAVSTAR (Navigation Satellite timing and Ranging Global Positioning System) is a satellite-based navigation, timing and positioning system. Page 19

31 The GPS provides continuous three-dimensional positioning 24 hrs a day throughout the world. The technology seems to be beneficiary to the GPS user community in terms of obtaining accurate data up to about100 meters for navigation, meter-level for mapping, and down to millimeter level for geodetic positioning. The GPS technology has tremendous amount of applications in GIS data collection, surveying, and mapping Geo-positioning Basic Concepts By positioning, we understand the determination of stationary or moving objects. These can be determined as follows: 1. In relation to a well-defined coordinate system, usually by three coordinate values and 2. In relation to other point, taking one point as the origin of a local coordinate system. The first mode of positioning is known as point positioning, the second as relative positioning. If the object to be positioned is stationary, we term it as static positioning while if the object is moving we call it kinematic positioning. Usually, the static positioning is used in surveying and the kinematic position in navigation GPS Component and Basic Facts The Geographic Positioning System (GPS) uses satellites and computers to compute positions anywhere on earth. The GPS is based on satellite ranging. That means the position on the earth is determined by measuring the distance from a group of satellites in space. The basic principle behind GPS is simple, even though the system employs some of the most high-tech equipment ever developed. In order to understand GPS basics, the system can be categorized into Page 20

32 FIVE logical Steps 1. Triangulation from the satellite is the basis of the system. 2. To triangulate, the GPS measures the distance using the travel time of the radio message. 3. To measure travel time, the GPS need a very accurate clock. 4. Once the distance to a satellite is known, then we need to know where the satellite is in space. 5. As the GPS signal travels through the ionosphere and the earth's atmosphere, the signal is delayed. To compute a position in three dimensions, we need to have four satellite measurements. The GPS uses a trigonometric approach to calculate the position. The GPS satellites are so high up that their orbits are very predictable and each of the satellites is equipped with a very accurate atomic clock GPS in Milk Collection To understand and map the geographic distribution of Milk Collection Centers, the knowledge of their spatial location is of essence. Satellite imagery can be used to determine the position of the centers but not entirely all the centers. Therefore a GPS, preferably a hand held GPS, can be used to obtain the spatial location within an acceptable accuracy of 3m for most if not all of the MCC. The handheld GPS will provide the x, y and z coordinates of each Milk Collection Centers. Page 21

33 3 CHAPTER THREE: MATERIALS AND METHODOLOGY 3.1 Area of Study The area of study is Githunguri Division of Kiambu district in central Kenya. It is approximately square kilometers with a current population estimated to be and a population density of 852 persons per square kilometers(kenya National Beaural of Statistics (KNBS), 2009). It borders Ruiru Division to the east, Gatundu Division to the north-east Lari Division to the north, Limuru Division to the west and south-west and Kambaa Division to the south. The study area is in UTM zone 37M and approximately between longitudes to and latitude to. The selected study area, Githunguri Division, has an altitude range of meters above sea level. Rainfall is bimodal and the average rainfall received in the district varies from 600 to 2000 mm per year depending on location and altitude. The rainfall is reliable and favorable for agricultural activities. The average annual rainfall range for Githunguri is mm (Fertilizer Use Recommendation Project (FURP), 1987). The district has two growing periods per year with a total length of days (Kassam et al., 1991). The annual mean temperature ranges between 18 0 C C for Githunguri study area. Livestock kept in the study area include dairy animals and varying breeds of goats, and poultry. Beekeeping is also practiced in some households. The dairy animals are mainly kept under zero-grazing system. Milk production in the district is favored by flourishing milk processing and packaging industries in the neighboring Nairobi and within the district. The main milk processing and packaging industry in the study area is Githunguri Dairy Cooperative Society. It collects and processes milk within Githunguri Division. Page 22

34 23 Map Area of Study (Githunguri Division)

35 3.2 Data sources and Tools Data sources Table Data sources a) Digital Map of Kiambu A pair of digital maps at a scale of 1:50,000 each covering Kiambu District was obtained from Survey of Kenya. The study area (Githunguri division) was covered by a part of the two sheets. The map clearly showed roads, settlements, and plantations. The map also had grid superimposed on the contents and coordinates shown as well as marginal information. Names of places were also clear. b) GPS coordinates Using a handheld GPS receiver, coordinates of all Milk Collection Centers within the study area were taken. This involved visiting the area and collecting a convenient point Page 24

36 where the sky was clearly visible without obstruction. In places where it was not possible to have clear sky within the Milk Collection Center, a nearby location was selected to acquire coordinates that were later edited to give coordinates of the Milk Collection Center. A list of names of all Milk Collection Centers acquired from the dairy cooperative 3 was used for easy navigation from one collection center to another given that the names of Collection Centers take after the names of villages. The process was also made successful by the help of the local people. It was necessary to collect the coordinates because few of the collection centers could be located from Google earth. c) Tabular data Records of the milk production per day, names of the collection centers, and the active members in each milk center were obtained from Githunguri dairy cooperative in digital format (excel format). Apart from creating a database, the records of names of the milk centers were used to identify the collection centers on the ground during fieldwork as well from Google earth. The number of Milk Collection Tankers and their carrying capacity was obtained from the dairy cooperative. The speed of the Milk Collection Tankers on the different types of roads was assumed. The speed was necessary to assist in computation of travel time from processing plant to the milk centers. 3 Githunguri Dairy Cooperative Society Page 25

37 3.2.2 Tools Hard ware Handheld GPS Receiver Computer with specification of 250GB Memory, 2GB RAM and 3.2 GHz speed Flash disk of space 1GB Printer Compact disk 700MB Software Arc GIS 9.3 Global Mapper 12.0 Adobe Reader v.9.0 Microsoft office suite (2003 and 2007) Paint Map Source Page 26

38 3.3 Overview of the methodology Figure3.3.1 Flow chart showing methodology Figure represents a summary of the methodology used in carrying out the project. Page 27

39 Following a thorough decision on the data required for the project, both spatial and nonspatial data as described in table were collected and a sequence of data preparation and editing was carried out which included:- GPS data manipulation in preparation for importation into ArcGIS environment, georeferencing of topographical maps, manipulation of records (attribute data) in excel worksheet and importing the same into ArcGIS, which were then joined to attribute data of GPS coordinates to create a geo-database, the Dairy Geodatabase. Shape files creation via digitization of the geo-referenced data was performed. Further, the spatial distribution of the Milk Collection Centers was assessed by carrying out a service area analysis, which gave a better representation compared to buffering. These processes clearly identify the level of service Milk Collection Centers offer dairy farmers in terms of area coverage of each center 4. Finally, route optimization using vehicle routing problem was carried out and displayed. Various constraints were taken into consideration such as time window, vehicle capacity etc so that to come up with optimal route(s). 3.4 Data Capture GIS data capture is a technique in which the information on various map attributes, facilities, assets, and organizational data are digitized and organized on a target GIS system on appropriate layers. The basic processes include identification, collection, digitization and correction of errors necessary to build the database. 4 Milk Collection Center Page 28

40 GIS Data capture process Data identification Figure GIS data capture process (Mulaku, 2011) An understanding of the project requirements based on its objective was the first step towards identifying the necessary data. The sources of this data were identified as well which included Githunguri Dairy Cooperative Society, Survey of Kenya (SoK) and International Livestock Research Institute (ILRI). Data collection This is the stage where necessary data was collected from relevant sources and fieldwork. All data collected were in digital format. All dataset collected were evaluated for their quality and completeness before any use. Portable memory was used for collection. The data collected were digital topographic maps, shape files for administrative units, as well as attribute data for all Milk Collection Centers. Githunguri Dairy cooperative has sixty-four Milk Collection Centers. Page 29

41 3.5 Data Preparation a) Conversion and Transformation Digital data obtained from survey of Kenya were in JPEG format. JPEG format is compressed and hard to maneuver using GIS software. Global mapper was used to export the images in Tagged Image File Format. GPS coordinates in the GPS receiver were downloaded using Mapsource software. The downloaded data in Mapsource were sorted and the necessary data copied to excel file. This data was further sorted out and saved as text file that could be imported and opened in ArcGIS. Shape file data on administrative boundary obtained from ILRI were in WGS 84 coordinate system and therefore they were transformed to UTM projection, Arc 1960 datum using ArcGis so that they can overlay with other sets of data in the later coordinate system. b) Georeferencing This is linking an image to a map projection using a map geometric transformation. A transformation is a function that relates the coordinates of two coordinate systems. A coordinate system consists of a set of points, lines, and/or surfaces, and a set of rules, used to define the position of points in space in either two or three dimensions. A map coordinate system is defined using a map projection. The ability to describe accurately the geographic locations is critical in both mapping and GIS. Georeferencing enables elements in a map layer to be located on or near the earth surface and viewed, queried and analyzed with other geographic data. This process requires spatial reference information, which are geographic coordinates of control points within area covered by the map image. On Maps, locations are given using grids, graticules, and tic marks labeled with various ground locations (both in measures of latitude, longitude and in projected coordinates system such as UTM meters). Page 30

42 Root mean square (RMS) is the error in the distance between the inputs location ground control points (GCP) and their transformed location for the same GCP. RMS error is calculated with the following equation. Where are transformed coordinates are input source coordinates Georeferencing Topographical Maps Two topographical maps at a scale of 1:50000 were geo-referenced given that the area of study was covered by two sheets. Limuru topographical map was loaded in ArcGis 9.3 and using the georeferencing tools as shown in figure 3.5.1, a set of four corner point well distributed on the map to avoid collinearity were added. The geographic coordinates of the four corner points were read from the grid values of the map. Image pixilation was done to locate accurately each corner point. The projection and datum used was Universal Transverse Mercator (UTM) and Arc 1960 respectively. The root mean square resulting from the georeferencing was which translate to on the ground. The same procedure was carried out on Kiambu topographical map and a similar RMS value of was achieved. The two topographical maps were mosaicked forming a well-matched and seamless display as shown on figure Figure shows a summary of the Georeferencing process Page 31

43 Figure Georeferencing Procedure Figure Mosaicked Topographic maps Page 32

44 Figure Georeferencing Process c) Defining Map Projection Map projection for shape files created in readiness for digitization was defined in ArcCatalog. All projections for the whole study were set to be Universal Transverse Mercator (UTM), Arc 1960 datum, spheroid: Clerk_1880_GRS(Geodetic reference system) d) GPS coordinates of Milk Collection Center Figure GPS hand held receiver used for the study (Source: photograph taken by Josphat) For this study, a Garmin receiver was used to determine and record coordinates of MCC. Twenty-three MCC out of sixty-four were collected. All major collection centers were collected and samples of satellite centers at each MCC. The receiver recorded the coordinates at an accuracy of. The coordinates stored in the memory of the GPS receiver were downloaded using Mapsource software The downloaded data was copied to excel file for further organisation and eliminating unnecessary data then saved as text file. Page 33

45 Table Coordinates of Milk Collection Centers The coordinates were then loaded as x, y coordinates in ArcGis, there coordinate system defined and then exported as shape files and included in the Dairy Geodatabase. The resulting feature class file could be edited and queried. e) Creation of Geodatabase The geodatabase is a collection of geographic datasets of various types used in GIS and managed in either a file folder or a relational database. It is the native data source for GIS and is used for editing and data automation. Some of the common datasets included in workspaces and geodatabase are feature class, tables and raster datasets. A number of rich geographic information types are used to add GIS behavior and integrity to the features include network datasets (Environmental Systems Research Institute (ESRI), ). Page 34

46 In this study, a Dairy Geodatabase was created and all shape files and tables imported. The geodatabase contained the following data: roads, coordinates of the twenty-three Milk Collection Centers, attribute information of all sixty-four MCC, georeferenced topographical maps, villages, towns and administrative boundaries. a) Network dataset Networks used by Arc-GIS Network Analyst are stored as network datasets. A network dataset is created from the feature source or sources that participate in the network. It incorporates an advanced connectivity model that can represent complex scenarios, such as multimodal transportation networks. It also possesses a rich network attribute model that helps model impedances, restrictions, and hierarchy for the network. The network dataset is built from simple features (lines and points) and turns. 3.6 Data Extraction Digitization Digitizing is the process of converting features into a digital format, digitizing creates new data. Several ways to digitize new features include digitizing on screen or head up over an image, digitizing a hard copy of a map on digitizing board, or using automated digitization. For this study on screen digitization was used. The main features digitized in this study are four classes of roads: 1) All weather roads 2) Dry weather roads 3) Main track (motorable) 4) Footpaths The road sections in the resulting network were then assigned assumed mean travel speeds, with values 60 km/hr in the case of all weather roads, 40 km/hr for dry weather roads and 20km/hr for main tracks (motorable). The speeds were used to compute the drive time on the road section. Walking speed of m/s, walking speed of an Page 35

47 average adult (Brennan), was assigned to all sections of the road network. The walking speed was used to compute the walk time along all the sections of the network. Table below shows attributes of a section of a road network. Table Attributes of a Dry Weather Roads After digitization, other data that were obtained as shape files were plotted on the digitized map Data Editing The main task in editing digitized graphic data include a) Error correction: errors such as gaps in linear features, error in edited polygons and lines joining at wrong places were corrected. b) Entering missing data: details left out accidentally during digitization were verified visually onscreen and entered in the digitizing environment. Figure shows errors during a digitization process while figure shows graphical editing for digitizing errors. Page 36

48 Figure Digitizing errors (source: Lo and Yeung, 2002) Figure Graphical editing for digitizing error (Source: Lo and Yeung, 2002) Page 37

49 3.6.3 Creation of network dataset In this study, two sets of network datasets were created. One was used to facilitate finding service area coverage of each Milk Collection Center and the other in finding optimum routes in the solution of vehicle routing problem. The network datasets were created in ArcCatalog under the Dairy Geodatabase. The main step in the creation of the network dataset is setting the connectivity of the various classes of roads as well as specifying the attributes of network data set to be used as impedance 5 in the network analysis process. Network dataset for service area analysis was built from all the four classes of roads that is, all weather roads, dry weather roads, main tracks and foot paths. The cost attribute 6 set for this network dataset were Length and walk time. Three classes of roads were used in creating network dataset for solving vehicle routing problem; they included all weather roads, dry weather roads, main tracks. The cost attribute set for this network dataset include length and drive time. Figure shows the two networks, Final_roads_Routing_ND and Final_roads_Service_Area_ND for vehicle routing problem and service area analysis respectively. Figure Network Datasets. 5 Attribute which is minimized while determining the route. 6 The attribute used as impedance in network analysis. Page 38

50 3.7 Service Area Analysis With Network Analyst, one can find service areas around any location on a network. A network service area is a region that encompasses all accessible road or/and footpaths that lie within a specified impedance. For instance, a 10-minute service area for a facility includes the entire road or/and footpaths that can be reached within ten minutes from that facility. Accessibility Accessibility refers to how easy it is to go to a site. In ArcGIS Network Analyst, accessibility can be measured in terms of travel time, distance, or any other impedance on the network. Evaluating accessibility helps answer basic questions such as, "what percentage of area is not covered by a 15-minute walk to a Milk Collection Centers or how many dairy farmers are covered by the 15-minute walk service area and therefore how many are not?" Accessibility therefore, can help one determine how well a center(s) serves a particular region. Evaluating accessibility One simple way to evaluate accessibility is by a buffer distance around a point or site. For example, finding out how many members of the dairy cooperative live within a onekilometer radius of a MCC using a simple circle. However, considering people travel by road, this method will not reflect the actual accessibility to the MCC. Service networks computed by ArcGIS Network Analyst was therefore used to overcome this limitation by identifying the accessible road or/and footpaths within a specified distance or walk time of a MCC via the road network. Once created, the service area was used to determine the number of members that can be served within the specified impedance. Multiple concentric service areas show how accessibility changes with an increase in impedance. Page 39

51 3.7.1 Service area analysis layer The service area analysis layer stores all the inputs, parameters, and results of service area analysis. Network dataset created under section was used to create the service area analysis layer. The sublayers of a service area analysis layer include: Facility feature Barriers feature Polygon feature Line feature The sublayers of the service area analysis layer used in this project were facilities and polygons. a) Facilities feature layer All the twenty-three Milk Collection Centers were loaded and stored under the facilities sublayer. b) Polygons feature layer The results of the service area analysis were stored under this sublayer Analysis settings Before any analysis was performed, parameters were set under the properties of the service area analysis layer as shown in Figure The parameters set under the properties dialogue box include. a) Impedance This is the cost attribute minimized while determining the route. The cost attribute set for this particular study is Walk-Time, which represents the time taken to traverse a unit distance along the road. b) Default polygon breaks: This is the extent of the service area to be calculated and was set at different polygon break of, 15, 20, 25 and 30 minute. This represents, theoretically, the time farmers spend to walk from their home to the Milk Collection Center. The optimum time is a 15-minute walk to a Milk collection Page 40

52 center that is roughly 1.2 km walking distance. At 15-minute time span or better, it would improve the entire time taken in collecting milk from farmers as well as reduce the exhaustion of farmers as a result of walking long distances. c) Direction: directions were set towards the facilities, the Milk Collection Center. d) Allow U-turns: U-turns were not allowed assuming that farmers moved in a single known direction to the Milk Collection Center. Three sets of results were generated: Figure Service area analysis settings A Multiple Concentric Service area at 15, 20, 25 and 30 minutes time impedance. Two service areas at 15 and 30-minute time impedance. The 15-minute service area coverage was further used to determine the number of dairy farmers that can access the Milk Collection Center(s). The procedure used is as follows: The numbers of active members stored in the Dairy Geodatabase for all the sixtyfour MCC were aggregated per sub-location. The density of active members per square kilometer was derived for every sub-location and added as an attribute. Page 41

53 A raster dataset was created from the resulting feature dataset with density of active members as the field value. Classification of the raster dataset was done using nine classes. The classified raster dataset was then clipped using the fifteen-minute service area polygons. The clipped raster dataset was converted to feature dataset. The attribute of the feature dataset included density of active members per square kilometer and area covered by a particular density as shown on table From these two attributes the number of dairy farmers (members of Githunguri dairy cooperative) covered by service area was derived from the equation below. A comparison was made between the existing number of members and the number of members that can be served within a fifteen-minute service area was made. Page 42

54 Table attribute table showing number of members covered per region Page 43

55 3.8 Vehicle Routing Analysis. The Vehicle Routing Problem (VRP) can be defined as a problem of finding the optimal routes of delivery or collection from one or several depots to a number of cities, customer or stations while satisfying some constraints and giving minimal total cost. Collection of household waste, gasoline delivery trucks, goods distribution, snowplough and mail delivery are the most used applications of the VRP. VRP solver in ArcGIS finds the best routes for a fleet of vehicles servicing many orders and can answer problems that are more specific because numerous options are available, such as matching vehicle capacities with order quantities, giving breaks to drivers, and pairing orders into the same route. The basic steps involved in performing a vehicle routing problem analysis include the following: 1. Creation of a vehicle routing problem analysis layer, a type of network analysis layer. The vehicle routing problem analysis layer stores the inputs, parameters, and results for a given vehicle routing problem. 2. Adding network analysis objects to the vehicle routing problem analysis layer 3. Setting up the vehicle routing problem analysis layer properties 4. Solving the vehicle routing problem analysis layer. Network analysis objects in ArcGIS Network Analyst, is a feature or row in a network analysis class. Network analysis objects are used as input and written as output during network analysis. A network location is a specific type of network analysis object that has a defined position on a network dataset e.g. locations of Milk Collection Centers (Environmental Systems Research Institute (ESRI), ). Network analysis layer is a composite layer that contains the properties and network analysis classes used in the analysis of a network problem, and the results of the analysis. Figure below shows classes in a VRP and their relationships. Page 44

56 Figure Relationships of Network Analysis Classes Source (ESRI, ) In solving a VRP, a vehicle routing problem analysis layer was created from the network analyst tool bar. Under the network dataset combo box, the appropriate network dataset was chosen ie FINAL_roads_Routing_ND. Page 45

57 Network Analyst Window Vehicle Route Analysis Layer Map Window Figure VRP analysis Layer and Analysis Window The vehicle routing problem analysis layer stores the inputs, parameters, and results for a given vehicle routing problem. The network analysis classes considered in this project were: Orders, Depots, Routes, Route Zones. The properties of each network analysis class were set. Page 46

58 Properties of the vehicle routing problem a) Orders An order can be a delivery to a customer (for example, furniture delivery), a pickup from a customer (for example, grease pickup from restaurants) or some other type of work (for example, inspection visit). Each order has a size or quantity, such as total weight of goods to be picked, or volume of milk to be picked up. An order can have a service time associated with it, which is the time needed to complete the order. For example, a milk tanker may require a 5 to 10 minute service time to load milk. The service time can be the same for all orders, or it can be different for each order. An order can have one or two time windows associated with it to indicate when a vehicle is allowed to visit the order. For example, a milk tanker is only permitted to arrive at a Milk Collection Center between 8:00 and 10:00 a.m. or between 2:00 and 4:00 p.m. because arriving at any other time would disrupt the milk collection process. The orders are point features, so they are created for a given vehicle routing problem analysis layer by adding network locations. The orders added to this particular milk collection problem were locations for each MCC. The properties set for each order was as per the status and requirement of each MCC. The figure overleaf, figures 3.8.3, displays the properties set for each Milk Collection Center. Some of the properties considered and set were: Name of Milk Collection Center Description of a Milk Collection Center Pick up quantities at each Milk Collection Center Time windows, the start and end of a collection period specified by Githunguri Dairy cooperative Service time. This was computed as per guidelines of pumping speeds 7 provided by Plant Milk Receiving Guidelines (2008). 7 rate at which milk can be loaded into a milk collection tanker, litres/hour. Page 47

59 . Figure Orders (MCC) and properties of Kambaa MCC b) Depots This feature layer stores depots that are part of a given vehicle routing problem analysis layer. A depot is a location that a vehicle departs from at the beginning of its workday and returns to at the end of the workday. The depots are locations where the vehicle is loaded in case of deliveries or unloaded in case of pickups. In some cases, a depot can also act as a renewal location whereby the vehicle can unload or load the quantities and continue performing further deliveries or pickups. A depot has open and close times, as specified by a hard time window. Vehicles cannot arrive at a depot outside this time window. The depots are point features, so they are created for a given vehicle routing problem analysis layer by adding network locations. Page 48

60 The location of Githunguri processing plant was added as a depot. The properties set for the depot, Githunguri processing plant, were: Name, Githunguri Processing Plant Description Time Windows. This represents the beginning and end of each collection period at the depot. The start time was set at 6:00 am and the end time was set at 7:00pm. The figure below shows the properties set for the Depot Figure The properties of Depots c) Routes feature layer This feature layer stores routes that are part of a given vehicle routing problem analysis layer. A route specifies the vehicle and driver characteristics as well as represents the traversal between depots, orders, and breaks. In ArcGIS Network Analyst, vehicles, routes, and drivers are synonymous, and the term "route" is used to encompass all three. Page 49

61 A route starts and ends at a depot location. It can start and end at different depots. It may spend time at the depots unloading the vehicle. The amount of time spent at the start and end depots are fixed for the route and are specified as service times. A route may start at a fixed time or it may have a flexible start time; that is, it may have an earliest-to-latest start time range. The start time range and the time window of the starting depot are taken into account when determining the actual start time of the route. The operating cost for an individual route can be made up of time-based costs, distance-based, costs and/or fixed costs that are independent of the amount of time worked or the distance driven. For example, there may be a fixed cost associated with using a vehicle if additional vehicles are to be rented to handle high-workload days. Similarly, the driver may be paid for the number of hours worked, inclusive or exclusive of overtime and lunch breaks. Such costs may be used to specify time-based costs. The fuel costs may be used to specify distance costs. The vehicle operating on a given route may also have a capacity, which limits how much it can carry. This specifies the capacity for a given route. There may be constraints on a driver's workday such as the total distance driven or the number of hours a driver can work or drive due to state regulations or labor union agreements. The route may include a work break. The driver may or may not be paid for this break. A vehicle may have certain capabilities; for example, a power lift or special shielding, or technicians may have different skill sets. The orders that have these specialties defined on them must be assigned to the appropriate routes. A route may be associated with a zone if the route is restricted to work in a predefined geographic region. Routes are linear features. They can be imported from existing routes in other vehicle routing problem analysis layers, from other linear features, or from tables. For any of these three input types, routes can be added with the Load Locations dialog box. Alternatively, routes can be specified by the add item menu command. In this project, the later procedure was used to create and specify parameters for each route. Page 50

62 Figure shows created routes and specification of the parameters used for solving this specific milk collection problem. The decision on the number of routes to be created was based on the total quantity of milk to be collected from all the twenty-three Milk Collection Centers and the constraint imposed by the carrying capacities of all the Milk Collection Tankers to be used. The entire milk collected is litres per shift and 81, liters per day. The carrying capacities of Milk Collection Tankers can range from 5000 litres to litres. A set of different combination of existing Milk Collection Tankers within the dairy cooperative can be used to collect the entire milk in each shift. Milk collection Tankers, one of capacity litres and three tankers of capacity litres were used as the number of tankers to collect the entire milk. Each tanker collects milk from a single route hence four routes will be generated. Figure Route and its Properties. Page 51

63 The table below shows the constraints and parameters considered in each route for purposes of solving the milk collection problem. Table Constraints in the Various Routes Name Tanker capacities Maximum order count Cost(Kshs) per Km Maximum total time(min) Start of routing period (liters) Route A :00 am Route B :00 am Route C :00 am Route D :00 am The cost per unit km was calculated based on the fuel economy for road tankers and the cost of fuel per litre. The fuel economy for road tankers is approximately 7.5 mpg (Greenaway, 2009) which translates to 3.3 km per litre. The cost of diesel fuel according to the Kenya fuel price fixtures as of 14 march, 2011 was Kshs Maximum total time was based on the threshold at which fresh milk start fermentation. According to Mr. Mwenza, a Dairy Chemistry lecturer at Dairy Science Institute, milk starts fermenting 4 hours after milking therefore, the routing time constraint was set at 4 hours (240 minutes). d) Route Zones feature layer This feature layer stores route zones that are part of a given vehicle routing problem analysis layer. Route zones specify a work territory for a given route. A route zone is a polygon feature and is used to constrain routes to servicing only those orders that fall within or near a certain area. Route zones were created within Githunguri region to preassign Milk Collection Tankers to particular areas. The zones were created to ensure all the MCC are assigned to a route. Initial solution without zones resulted to one of the MCC not being assigned to a route as will be discussed under section 4.4 (a). Page 52

64 3.8.1 Setting Analysis Properties. Before any solution was made, the following properties were set for the vehicle routing problem analysis layer on the Analysis Settings tab of the Layer Properties dialog box: Figure Analysis Settings Time Attribute: The time cost attribute was set to define the traversal time along the elements of the network. The time cost attribute is required since the vehicle routing problem solver minimizes time. Distance Attribute: The distance cost attribute used to define the length along the elements of the network. Default Date: The implied date for time field values with an unspecified date. If a time field such as TimeWindowStart1 has a time-only value, the date is the Default Date. For example, if an order has a start time window of 9:00 A.M. and the Default Date is March 6, 2008, then the start time window for the order is 9:00 A.M. on March 6, If the Default Date setting is changed, then the implied date for all time field values with an Page 53

65 unspecified date is the new default date. The default date has no effect on time field values that already have a time along with a particular date. Capacity Count: The number of capacity constraint dimensions required to describe the relevant limits of the vehicles. In an order delivery case, each vehicle may have a limited amount of weight and volume it can carry at one time based on physical and legal limitations. In this case, if you track the weight and volume on the orders, you can use these two capacities to prevent the vehicles from being overloaded. The capacity count for this study is one (volume of milk). Time Field Units: The time units used by the temporal fields of the analysis layer's sublayers and tables (network analysis classes). Distance Field Units: The distance units used by distance fields of the analysis layer's sublayers and tables (network analysis classes). This does not have to be the same as the units of the optional distance cost attribute. Allow U-turns: ArcGIS Network Analyst can be set to allow U-turns everywhere, nowhere, or only at dead-ends (cul-de-sacs). In this study, U-turns were allowed everywhere implying the route can double back on the same road in the opposite direction. Page 54

66 4 CHAPTER FOUR: RESULTS AND ANALYSIS 4.1 Githunguri Digital Maps A digital map covering the study area was drawn. The map contained the different types of roads: all weather roads dry weather roads main motorable tracks foot paths In addition, tea plantation, administrative boundary at subdivision level, villages and towns were drawn to help identify milk centers whose names normally take after the names of villages. Map shows the selected physical and natural features within Githunguri Subdivision Distribution of Members and MCC for Githunguri Dairy cooperative A map showing a density distribution of dairy farmers was drawn. The map was drawn from aggregated number of members per sub-location divided by the area of the sublocation. Page 55

67 56 Map Map of Githunguri Division

68 57 Map Active Member Density Distribution

69 From map 4.1.2, higher concentrations of members are at the central and eastern part of Githunguri division. The eastern part of Githunguri division has fewer numbers of dairy farmers-members to the cooperative. According to the map, most of the centers collected are concentrated and distributed on the Eastern part of Githunguri Subdivision. The Eastern part of Githunguri Subdivision was delineated to bring to focus the distribution of Milk Collection Centers in the Eastern part of Githunguri. The boundary of the Eastern part of Githunguri was bounded by the boundary of a 3km buffer or service area to the West and Githunguri boundary to the North, East and Southern part. Map shows the delineated Eastern part of Githunguri Subdivision and the distribution of MCC within its boundary. Page 58

70 59 Map Distribution of Milk Collection Centers

71 4.1.2 Database Edited feature dataset, tables and network were stored in the dairy Geo-database to assist in generating results and performing analysis. Attributes of feature class were edited by adding fields and more attributes. Table shows the attributes of Milk Collection Centers specifying the number of members, Milk intake per month, per day and per shift. Table Attributes of Milk Collection Centers Page 60

72 4.1.3 Network Dataset Figure Part of Githunguri Road Network Figure shows a section of Githunguri road network. Page 61

73 4.2 Service Area Coverage of MCC Service area coverage in this project resulted to a network service area covering regions that encompasses all accessible roads or/and footpaths that lie within the specified impedance to Milk Collection Centers as shown on maps 4.3.1, and Figure shows the layers created under the service area analysis data frame in ArcMap. Figure Layers of Service Area coverage Maps showing service area coverage were generated from the layers shown in figure above. Page 62

74 Map Service Area Coverage The map shown above represents regions that are covered at 15, 20, 25 and 30-minute walk to a Milk Collection Center. The corresponding walking distance ranges from 1 km to 2.5 km. A large portion of the western part of Githunguri division was not covered. Page 63

75 64 Map Map showing 15-minute Service area coverage.

76 65 Map Member Distribution within a 15 minute Service Area

77 From the density map shown, Map 4.2.3, the number of members that are covered per polygon surrounding a set of centers or a single center were computed and a comparison was made against the actual number of members served by the corresponding center(s). The polygons were selected interactively and the corresponding Milk Collection Centers in the polygon were intersected as shown in Figure The comparison was made in a table format as shown below in table Figure selecting a polygon and the corresponding MCC Page 66

78 Table Summary of active members in a 15-minute Service Area Polygon Number Milk Collection Centers Gatina Ha Kariuki 1 Ha Nyakaro Gitiha Gitwe 2 Kambaa Kahunira Ngochi 3 Thuthuriki Githunguri Gitwe 4 Matuguta Mung u Current Number of Members Spatially Determined number of members Githiga Kamondo Gathaithi Mutuya Ikinu Gathanji Gakoe Kanjai Magomano Karia Lioki A graph showing the comparison between current number of members served by Milk Collection Centers against the number of members as computed spatially within a 15- minute service area was drawn as shown on figure Page 67

79 Member Count Member Distribution Current Number of Members Spatially Determined number of Members Polygon Number Figure A Graph of number of members in a 15-minute service area. From the graph shown above, the current number of subscribed members exceeds the number of members lying within a 15-minute service area except for polygon 8 constituting Mutuya and Ikinu MCC. This shows that even though there are higher member densities around Mutuya and Ikinu according to Map 4.2.3, there are equally enough Milk Collection Centers and a good road network to serve the dairy farmers around that region. Page 68

80 Similarly, a 30-minute Service area resulted to results as shown on table and corresponding graph as shown on Figure Table Summary of Active Members in a 30-Minute Service Area Polygon Number Milk Collection Centers Gatina Ha Kariuki Ha Nyakaro Gitiha Gitwe Kambaa Kahunira Ngochi Thuthuriki Githunguri Gitwe Matuguta Mung u Current Number of Members Spatially Determined number of members Githiga Gathaithi Mutuya Ikinu Kamondo Gathanji Karia Kanjai Gakoe Magomano Lioki Page 69

81 Member Count Member Distribution Current Number of Members 400 Spatially Determined number of Members Polygon Number Figure A Graph of number of members in a 30-minute service area From the graph above it is observed that more people can be served in a 30- minute service area except for polygon 3 whose service area covers five-milk Collection Centers that is Kahunira, Ngochi, Thuthuriki, Gitwe and Githunguri. The current number of members served by the five centers is 978 yet within the service area, 854 members can be served. 124 members are not within the 30-minute service area, which is a 2.4 km walk to a collection center. Kamondo Milk collection center has more members than it can serve with a current subscription of 234 dairy farmers yet within the service area, 174 members can be served. Page 70

82 4.3 VRP Results and Analysis Vehicle Routing Problem (VRP) in this study and as defined under section 3.8 will be seen as the problem of finding optimal routes of collecting/pick-up of milk from one or several collection centers to the depot (milk processing plant) while satisfying some constraints and giving minimal total cost, time and travel distance. After creating a vehicle routing problem analysis layer, populating the required network analysis objects, and setting appropriate analysis properties, the solution for the vehicle routing problem analysis layer was obtained by using the solution button on the analysis tool bar. The solution is largely based on simulation and scheduler s judgment taking into consideration the various constraints stipulated as the case in table In this study, the solution of the VRP was executed iteratively undergoing two stages to obtain optimal routes to all the MCC. The two stages include: Vehicle Routing Solution without route Zones- this provides the intermediate results on the possible routes. Vehicle Routing Solution with route Zones- this provides the final improved results on the optimal routes Vehicle Routing Solution without route Zones The solution to the VRP without route zones is as shown in figure where the resulting network analysis window was updated to group each order (MCC) by the routes to which they were assigned and the sequence by which each MCC was visited in each route by the corresponding MCT. Map displays the routes created without route zones. Table shows the resulting groupings per route and the sequence of each MCC visitation in each route. The Depot Visits class was also updated to show the starting and ending depot for each route, which is Githunguri Dairy Processing Plant. Page 71

83 Figure Solution of VRP without the route zones Page 72

84 73 Map Route Map

85 MILK COLLECTION CENTERS (MCC) Table Milk Groupings and their Sequence in Each Route. ROUTES Route A Route B Route C Route D Unassigned MCC sequence Gatina Ngochi Ikinu Gathaithi Githiga 2 Ha Kariuki Kahunira Karia Kamondo 3 Gitiha Matuguta Lioki Mutuya 4 Ha Nyakaro Mung u Gakoe Gathanji 5 Kambaa Thuthuriki Kanjai Magomano 6 Kambaa Githunguri 7 8 Under the network analysis window, further results, were extracted to aid in the interpretation of the resulting routes. Some of the results include. The total milk collected per route. The total time taken per route. The total distance traversed per route. Total cost incurred per route. Violated constraints. The results were displayed, in ArcMap, as tables as shown in Figure which constituted the properties of each route providing their attributes. Page 74

86 Figure Attributes of Route A Figure shows the results, as displayed in ArcMap, of the attributes of Route A. similar attribute tables were generated to extract the attributes of each route. Table was created to show a summary of the results on attributes of each route. Table Summary of attributes of the resultant routes ROUTES Attributes Route A Route B Route C Route D Total Distance (Km) Total Time (min) Total Travel Time (min) Milk Capacity Collected (litres) Total Cost (Kshs) Violated Constraints Null Null Null Null Page 75

87 From the results as shown in Figure and Table 4.3.1, Githiga MCC was not assigned to any group or route because it violated the capacity constraint in route B being the most likely route it would have been assigned. Route B is the most likely route because the spatial location of Githiga MCC relative to the spatial distribution of other MCC in the route is close thus minimizing the transit time and transit distance. The Milk carrying capacity of route B is 1500 litres, which is the route with the highest carrying capacity. The milk collected along that route excluding Githiga MCC is litres. Milk intake at Githiga MCC is litres therefore if Githiga MCC was added to the route; the resultant milk capacity in the route would be litres, which exceeds the carrying capacity of that route-route B Vehicle Routing Solution with route Zones Solving Vehicle Routing Problem with zones is mainly known as clustering (Vehicle Routing and Scheduling). When developing the clusters, Capacity restrictions, time constraints, constraint on travel distance et-cetera are taken into account in resolving route conflict (Environmental Systems Research Institute (ESRI), 1998). Switching of MCC from one tour to another can be done during clustering such that the capacity of vehicle and other constraints are not violated. This is called tour improvement (Vehicle Routing and Scheduling) and it ensures all MCC are routed at minimal cost and travel distance. In this study, clustering and tour improvement aims at ensuring that Githiga MCC was assigned to a route and preferably route B, without violating the set constraints, which would result to a shorter transit time and transit distance. This was achieved by first reassigning Githunguri MCC to route A thus creating enough space in route B. Secondly, assigning Githiga MCC to route B. The switching of MCC from one route to another was achieved by creating route zones. A route zone is a polygon that is created around a set or cluster of MCC that obey constraints stipulated by a scheduler in this case as shown in table Figure shows the created route zones and its properties while the corresponding map is as shown on Map Page 76

88 Figure Route Zones and properties of zone A Page 77

89 78 Map Route Zone Map

90 Table Zones and their properties Route Zone A B C D Is Hard zone True True True True Table Attributes of the final optimized routes ROUTES Attributes Route A Route B Route C Route D Total Distance (Km) Total Time (min) Total Travel Time (min) Milk Capacity Collected (litres) Total Cost (Kshs) Violated Constraints Null Null Null Null Map shows the final routes Map showing optimal routes to all the MCC. Thematic maps were also drawn to display comparison in rank for distance, time and cost within routes. A line map showing the comparison in distances is as shown in Map Maps showing comparison in transit time, Milk capacity and total cost are as shown on Map 4.3.5, Map and Map respectively. A pick up density map was generated. Round optimization increases pick-up densities. Pick up density was computed from the following formula Where: Page 79

91 80 Map Optimized Routes

92 81 Map Route Map Showing Transit Distance

93 82 Map Route Map Showing Transit Time

94 83 Map Route Map Showing Milk Capacity per Route

95 84 Map Route Map Showing Total Cost Per Route

96 85 Map A Pick Up Density Map

97 From Map 4.3.4, Map 4.3.5, and Map 4.3.7, route A has the longest transit distance, longest transit time and the most expensive route while route C has the shortest transit distance and transit time and is the least expensive route. Route B and route D rank second and third respectively. From map 4.3.6, the route that collects the most milk is route B with a capacity of litres while the route that collects the least milk is route A with a capacity of 9362 litres. Route D and C rank second and third with a capacity of litres and litres of milk collected respectively. From Map 4.3.8, route B has the highest pick up density while route A has the lowest pick-up density. This shows that the round optimization of route B is much better compared to route A. Page 86

98 5 SUMMARY, CONCLUSIONS AND RECOMMENDATIONS 5.1 Summary and Conclusions This study was undertaken to explore the level of service, in terms of spatial coverage, Milk Collection Centers provide dairy farmers and to develop a system for routing Milk Collection Tankers by taking Githunguri Dairy Cooperative in Githunguri Division as the case study. It entails the specific objectives of mapping the spatial distribution of Milk Collection Centers in Githunguri, determining their service area coverage and the corresponding number of farmers covered by each service area as well as determining optimum routes from processing plant to each MCC. Primary data on GPS locations for Milk Collection Centers was collected for twenty-three out of sixty-four MCC and the location of Githunguri processing plant. Mainly all the major Milk Collection Centers, nine in number, were collected and samples of satellite centers surrounding each major collection center. Additionally, the remaining forty-one centers and their attributes were included in the Dairy Geodatabase. The Dairy Geodatabase was created to store, organize, and facilitate efficient retrieval of all feature datasets, attribute tables and derived datasets created in this study. The Milk Collection Centers and other features within Githunguri Division were used to create a map that showed the spatial distribution of MCC in relation to the processing plant and more importantly within the division. Two network datasets were successfully created. One of the network dataset was created from three classes of roads i.e., all weather roads, dry weather roads and main track (motorable) while the other network dataset was created from four classes of roads that included footpaths. The later network dataset was used in determining service area coverage of each Milk Collection Center. Service area coverage utilized the road network dataset to buffer a region that is within reach at 15, 20, 25, 30-minute travel distance along the existing road network. Further, the 15 and 30-minute service area were used to determine the number of dairy farmers that lie within the regions. The following results were clear from the analysis. At 15 minute service area, for most of the Milk Collection Centers except Mutuya and Ikinu, the number of dairy farmers determined spatially are fewer than the actual numbers subscribed to these centers. This shows that some dairy farmers Page 87

99 travel longer distances than 1.2 km, which is the equivalent of a 15 minute travel distance given that an average adult walks at m/s(Brennan). At 30-minute service area, most MCC except five, Kahunira, Ngochi, Thuthuriki, Gitwe and Githunguri covers a region that includes most dairy farmers subscribed to the various centers. These results revealed that as the service area increases the number of dairy farmers covered spatially increases. This is at the expense of increasing distances and time travelled to the Milk Collection Center. At one kilometer 8 service area, which is the approximate target distance a farmer is supposed to walk according to Kennedy Musakali, The Quality Assurance and Extension Service Manager, many farmers would be left out. With regards to routing analysis, the former network dataset was used in determining optimum routes from the depot to all the collected Milk Collection Centers. The study results on Vehicle Routing Problem for the twenty-three MCC showed that four optimum routes were feasible each having a series of MCC ordered in the manner in which they will be visited by Milk Collection Tankers. In the optimization of the four routes, three factors were minimized. They include Distance Time Cost Three major constraints were considered in the optimization process. i. Capacity of Milk Collection Tankers ii. Maximum time required in the whole process of collecting milk. iii. Time-window. The period required to visit each MCC The optimized routes were mapped and the results revealed that: Route A has the longest transit distance, longest transit time and the most expensive route. Route B collects most milk. 8 Correspond to a travel time of less than 15 minutes. Page 88

100 Round optimization of route B is the best compared to other three, Route A, Route C and Route D. From such result therefore, a scheduler can make decision and deploy Milk collection Tankers with confidence of minimizing transportation cost using Geographical information system. 5.2 Recommendations 1. Determination of the spatial extent to which Milk Collection Centers can serve dairy farmers need to be considered by the dairy cooperatives during the allocation of dairy farmers to the various MCC in order to: a. Minimize the distance dairy farmers travel to a Milk Collection Center. b. Minimize the time of travel that contribute to the overall time of milk collection 2. Improvement and creation of new feeder roads is necessary to increase the service area coverage of Milk Collection Centers thus improving their level of service to the dairy cooperatives. 3. Cooperatives should provide competitive price for milk driven by the prevailing market price through minimization of transportation cost by utilizing routeplanning software-gis based DSS such as ArcGis- for an effective logistic management. 4. Vehicle Routing Analysis in GIS should also be utilized by dairy cooperatives to plan an efficient and effective delivery of milk products such as yoghurt, fresh milk et-cetera from the processing plant to shops and supermarkets. 5. Vehicle routing Analysis in GIS should further be used in complex milk collection scenarios that require collection of different types of milk e.g. goat milk and cow milk within the same Milk Collection Tanker by utilization of specialties in GIS. 6. Other applications involving transportation services of perishable and non perishable goods such as Collection of household waste, gasoline delivery trucks, goods distribution, mail delivery etc should employ Vehicle Routing Analysis in GIS to improve their logistics. Page 89

101 REFERENCES AND BIBLIOGRAPHY Adenso-Diaz, B., González, M., & García, E. (1998). A hierarchical approach to managing dairy routing. Interfaces, 28(2), Boldin, L. D., & Golden, B. L. (1981). Classification in vehicle routing and scheduling. Brennan, Elizabeth, ed. BellaOnline. (accessed April 12, 2011). Burtler, M., Herlihy, P., & Keenan, P. B. (2005). Integrating Information Technology and Operational Research in the Management of Milk Collection. Food Engineering, 70 (3). Co-operative Societies Act Cap (490), Laws of Kenya. Environmental Systems Research Institute (ESRI). ( ). ArcGIS 9.3 Desktop Help. Environmental Systems Research Institute (ESRI). (1998). ArcLogistics Route. Eshetu, T. (2008). The role of dairy cooperatives in stimulating innovation and market oriented smallholders development: the case of Ada a dairy cooperative. Fertilizer Use Recommendation Project (FURP). (1987). Description of the first priority Sites in the various districts, Kiambu district. Final Report, Annex III, Vol. 20. Ministry of Agriculture, National Agricultural Research Laboratories, Government of Kenya, Nairobi. Gonzalez, M., & Machin, M. GIS and logistics tool for milk transportation in dairy industries. Greenaway, Mark, ed. Suffolk (accessed April 9, 2011). International Corporative Alliance (ICA). (1995). Cooperative Identity Statement. Geneva. Kassam, A. H., Velthuizen, H. T., Fisher, G. W., & Shah, M. M. (1991). Agroecological land resources assessment for agricultural development planning. A case study of Kenya. Resources database and land productivity, Technical Annex 1, land resources, Land and Water development Division, FAO and IIASA. Page 90

102 Kenya National Beaural of Statistics (KNBS). (2009). POPULATION & HOUSING CENSUS RESULTS. Kilmer, R. K., & Prasertsi, P. (2004). Scheduling and Routing Milk from Farm to Processors by a Cooperative. Journal of Agribusiness, Macmillan Publishers Limited. (2007). MACMILLAN English Dictionary (2 ed.). (R. a. Plc., Ed.) Macmillan Publishers Limited. Mulaku, G. C. (2011). Land Information System Lecture Notes. Unpublished. Muriuki, H. G. (2011). Lessons In Dairy Development Case Studies: Githunguri Dairy Cooperative, A Dairy Co-operative Society. Muriuki, H. G. (2003). Milk and Dairy Products, Post-harvest Losses and Food Safety in Sub-Saharan Africa and the Near East, a Review of the Small Scale Dairy Sector Kenya. Rome, Italy: Food and Agricultural Organization. PKF Consulting Ltd, International Research Network. (2005). Dairy Industry In Kenya. Export Processing Zones Authority. Plant Milk Receiving Guidelines. (2008). Stirling, S. (2006). Let s organize, A SYNDICOOP handbook for trade unions and cooperatives about organizing workers in the informal economy: A joint publication of the International Labour Organization (ILO), the International Cooperative Alliance and Geneva. Tarantilis, C. D., & Kiranoudis, C. T. (2002). Using a spatial decision support system for solving the vehicle. Information & Management, 39(5), Thorpe, W., Muriuki, H. G., Owango, M. O., & Steel, S. (2000). Dairy development in Kenya: The past,the present and the future. Annual symposium of the Animal Production Society of Kenya, March2000. Nairobi. Tsehay, R. (1998). Prospects of Ethiopian dairy development. Proceeding of the Role of Village Dairy Cooperatives in Dairy Development: Prospects for Improving Dairy in Ethiopia, Addis Ababa. USAID. (2008). Kenya Dairy Sector competitiveness program: Milk shed Assessment and small business. Land O Lakes/ Fibec limited Report. Vehicle Routing and Scheduling. (n.d.). Online Tutorial 5. Page 91

103 APPENDICES Appendix A: Attributes of Milk Collection Centers for Githunguri Dairy Cooperative Page 92

104 Appendix A Continued Page 93

105 Appendix B: Attributes of All Weather Roads Appendix C: Attributes of Dry Weather Roads Page 94

106 Appendix C Continued Appendix D: Attributes of Main Tracks Motorable Page 95

107 Appenix D Continued Appendix E: Attributes of Foot Path Page 96

108 Appendix E Continued Page 97

109 Appendix F: Milk Weighing and Recording at Gathaithi Milk Collection Center Appendix G: Milk Collection in Progress at Kambaa MCC in Githunguri Page 98

Longitudinal studies refer to those that gather information from the same set of respondents through repeated visits over a defined period of time.

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