submitted within the UNIGIS MSc. programme at the Centre for Geoinformatics (Z_GIS)

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Master Thesis

submitted within the UNIGIS MSc. programme at the Centre for Geoinformatics (Z_GIS)

University of Salzburg, Austria

under the provisions of UNIGIS joint study programme with Goa University, India

GIS Applications for Optimum Routing

A Case Study of Road Transportation in Sri Lanka

By

IDDAGODAGE Sithum Dharani Ferdinandes Student ID: UP40515

Advisor (s):

Dr. Shahnawaz

Centre for Geoinformatics (Z_GIS) University of Salzburg, Austria

Dr. Mahender Kotha Goa University, Goa, India

A thesis submitted in partial fulfilment of the requirements of the degree of

Master of Science in Geographical Information Science & Systems – MSc (GISc) Colombo, Sri Lanka, 01-09-2011

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Science Pledge

By my signature below, I certify that this thesis is entirely the result of my own work. I have cited all sources of information and data I have used in my project report and indicated their origin.

Place and Date: Colombo, Sri Lanka, 01-09-2011 Signature

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Acknowledgements:

With deep gratitude I would like to thank various persons who have contributed to this research which would not have been possible without them.

First of all I would like to thank to UNIGIS, Goa University, India, for giving the opportunity for doing this kind of research during the M.Sc. degree course.

My great thanks go to Goeinformatics International (pvt) Ltd., Colombo, Sri Lanka for help me to get information.

I would like to express my thankfulness to my dear friends for giving me advices, encouragements in many ways.

Finally but very honour, I would like to express my sincere gratitude to my beloved husband: Mr. Lakshman Senanayake for his kind advices, guidance and encouragement throughout my research.

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Abstract:

Distribution route planning are among the major problems because distribution goods to each customer needs to be safe, reliable and efficient. Hence, the research question for this thesis is to answer how to distribute goods in the most economical and convenient manner. The objective of this thesis is to create a GIS based route analysis for distribution network system which helps in daily customer allocation, design fastest and shortest bus routes with GIS facility. The result from this study has helped to develop a Route analysis prototype model for a logistic company in Sri Lanka. This prototype model will help the company transportation management to design shortest and fastest distribution routes, optimal routes and they can also allocate daily customers for each warehouse. The user interface application has been developed by using VBA and ArcGIS 9.3 Network Analyst provided by Environmental Science Research Institute.

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List of Figures

Figure 1.1: Development Model……….…….12

Figure 2.1: Universal Transverse Mercator Zone 44………...24

Figure 2.2: Chart of ArcGIS techniques in data processing………..….30

Figure 3.1: Customer Location allocate for Colombo warehouse using closest facility.... 37

Figure 3.2: Customer Location allocate for Kandy warehouse using closest facility ….….38 Figure 3.3: Customer Location allocate for Galle warehouse using closest facility……….39

Figure 3.4: Customer Location allocate for Anuradhapura warehouse using closest facility ………...39

Figure 3.5: Daily customer locations………...42

Figure 3.6: Colombo daily customer locations out of total daily customers………. ....43

Figure 3.7: Kandy daily customer locations out of total daily customers………. ....43

Figure 3.8: Anuradhapura daily customer locations out of total daily customers………....44

Figure 3.9: Galle daily customer locations out of total daily customers………....45

Figure 3.10: Shortest route for Colombo warehouse distribution and direction window....46

Figure 3.11: Shortest route for Galle warehouse distribution and direction window…… ..46

Figure 3.12: Shortest route for Anuradhapura warehouse distribution and direction window ………...47

Figure 3.13: Shortest route for Kandy warehouse distribution and direction window….. ..48

Figure 3.14: Fastest route for Colombo warehouse distribution and direction window…..49

Figure 3.15: Fastest route for Galle warehouse distribution………...49

Figure 3.16: Fastest route for Anuradhapura warehouse distribution and direction window ………...50

Figure 3.17: Fastest route for Kandy warehouse distribution and direction window…… ..51

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Figure 3.18: Optimal route and direction window for Colombo warehouse using VRP

model………. . 53

Figure 3.19: Optimal route and direction window for Kandy warehouse using VRP model………. . 54

Figure 3.20: Optimal route and direction window for Galle warehouse using VRP model……… .. 55

Figure 3.21: Optimal route and direction window for Anuradhapura warehouse using VRP model……… .. 56

Figure 3.22: Route Analysis model……….59

Figure 3.23: VRP model………..…..61

Figure 3.24: Model for allocate daily customers………....62

Figure 3.25: Route analysis for distribution network System Interface Model……… .65

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List of Tables

Table 2.1: Distribution Network Database………..……….………..17

Table 2.2: Transverse Mercator projection parameters for the Sri Lankan SL_GRID_99 based on the SLD99 and using the Everest 1830 ellipsoid Parameter Value..23

Table 3.1: Customer allocation for each ware house ………..41

Table 3.2: Distance results……….………..………...51

Table 3.3: Total time for each route……….52

Table 3.4: Details of each vehicle for Colombo warehouse………53

Table 3.5: Details of each vehicle for Kandy warehouse……….54

Table 3.6: Details of each vehicle for Galle warehouse………...55

Table 3.7: Details of each vehicle for Anuradhapura warehouse………...57

Table 3.8: Route analysis for distribution network System Toolbars……….64

List of Maps

Map 2.1: Road Map of Sri Lanka………..………..20

Map 2.2: Customer Distribution………...21

Map 3.1: Customer allocation for each warehouse………..40

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Chapter-1: Introduction

1.1 Background

As a developing country, demand in Sri Lankan transportation and logistics industry has increased significantly, due to increases competition in supplying goods. There are many companies supplies same kind of goods. Therefore there is always a high competition for demand. In maintain the demand of a good, the quality of product as well as continues supply of goods is important.

The productive companies follow two methods in supplying their products to the market.

 Supply products with the use of company vehicles

 Supply products with the help of logistics companies

With the growing competition in Sri Lankan transportation and logistics industry, it is most important to have an effective distribution plan.

Route Analysis for Distribution Network is a version of the travelling salesman problem, normally referred to the group of vehicle routing problems (VRP), also with or with no time window constraints. Therefore the plentiful studies that addressed the vehicle routing problems, different software methods (VB tools) have been developed that can be used to reduce the operating cost. Three factors make route analysis for distribution network unique:

1) Efficiency (the whole cost (time) to each distribution) 2) Effectiveness (how well the demand for service is fulfilled) 3) Equity (assign warehouses for each customer).

Route analysis for distribution network has two separate routing issues. Those are assigning warehouses to customers and routing the vehicles to the customers.

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1.2 Objectives

The problem of routing delivery vehicles deals with the significant question of how to deliver goods to customer location from warehouse and come back to warehouse again in efficient, most economical and most convenient manner. The scheduling and routing activities are often controversial because the problem must deal with multiple objectives.

As the number of delivery vehicle operating on the roadways increased by a company without a proper predefined plan, a number of problems occurred. Stealing the goods from vehicles, stealing vehicles properties such as fuel, tires and etc, cheat the company by providing fraud information, do not deliver goods on time, or don’t reach warehouse on time for loading are some of these problems. Even though the vehicle routing literature in common has dealt with several objectives, the most related in the situation of routing and scheduling of delivery vehicles for a company are:

 Order of serving customers

 Best route of the journey

 Total travel time of the trip

 Total distance of the trip

In earlier business world there was not high competition between supplying companies. Therefore the delivery goods using vehicle was not a serious problem as well. Thus delivery goods to customers have changed a lot in these last years.

Transportation is one of the prime applications of GIS. GIS can be very helpful in making predefined delivery plans. Many companies around the world are using GIS and finding it very easy and helpful to operate. “The greatest use of software packages with an element, or component, of GIS technology is at an operational level e.g.

routing, scheduling, tracking, tracing or navigation.”[Forster 2000]

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1.3 Area of Focus

In Sri Lanka as a developing country, the transportation is becoming a serious problem in business world. According to the reports of Road Development Authority (RDA), the whole island mainly covers with trunk roads and main roads those can be used for four weal motor vehicle travelling. There are thirty five roads in class “AA” roads. The total length of all trunk roads is about 4221kilometers. The total length of all main roads ( class

“B”) is about 7799 kilometers. [Road Development Authority Sri Lanka,2010]

The project was mainly focused on developing a network analysis system for a customer who is in logistics industry. The total area was covered by four main warehouses in main cities in Sri Lanka.

1.4 Development Methodology

The Development Model in figure 1.1 demonstrates the flow of work of this project in order to develop the predefined route plan for distribution network system. The research work has been divided into four different steps and each step has their own task to complete.

The four steps involved are explained as follows.

Problem Analysis Definition: Analysis of the existing system and the problems associated with it are explained in the starting part of the research. In this step the problems associated with the distribution network system and the need of predefined route plan for distribution network system are studied. Literature study gave the profound knowledge of the various approaches and solutions to the existing problems in the system. The efficiency is an important part of the whole delivery process and different technologies like GPS and GIS allow obtaining dynamic information about the vehicles' travelling activity.

Different methods and technologies were studied to investigate how a distribution route network system can improve the efficiency of delivery goods by a company. Based upon the above study, the objectives are set for the development work.

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Figure 1.1: Development Model

GIS Database Design: After learning about the problems and setting the objectives, the next task involved creating a GIS database. It is the most important part of the entire

Customizing Interface (Network analyst)

Step 1: Problem Analysis Definition Understanding the needs of route analysis for distribution network in connection with GIS transportation.

Step 2: GIS Database Design

Data

Acquisition for route analysis

Existing road network

Customer location data System Design

Distribution Network Prototype Module Step 3:

Prototype Design

Results

Results analysis Step 4:

Results and analysis

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process. It deals with the compilation and development of the spatial as well as non- spatial data according to the user requirements. It includes primary data collection which is followed by the data processing and the outcomes of that is the final spatial database which can be used to develop the application.

Prototype Design: The next task includes prototype model design on which the application will be developed. The prototype design relies upon the awaited output and on the needs and requirements of the user which were taken from the initial module. The prototype model will describe and explain all the components which will assemble the system. And once the prototype model is designed and GIS data is gathered, the application is ready to develop. ESRI’s ArcGIS software along with the Network Analyst was used for designed prototype model for the application.

Results and analysis: This step involves the results and the analysis the results of the Route analysis for distribution network system.

1.5 Research Overview

This research overview is included to give the reader a brief overview of the thesis and to give an understanding for how the work was structured. This thesis comprises 4 chapters.

Details of each chapter are given below:

Chapter 1: Introduction

The chapter presents an overview of the study. It describes the background, objectives, area of focus and the development methodology of the project.

Chapter 2: Methodology

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The chapter describes the data acquisition methods and importance of GIS in customer distribution network. The historical background of the route analysis projects are described briefly. And the contributions of the softwares used in the project are described briefly. Arc GIS Network analyst was mainly used as the GIS analysis for this project. The contribution of GIS in process of data preparation and the data processing were also discussed in this chapter.

Chapter 3: Processes and Results

The chapter describes the system design and implementation, results and method of interface design of the project. The analysis of the results that obtained from different route analysis models was also discussed. The interface design and the model creation using ArcGIS software were discussed in this chapter.

Chapter 4: Conclusion

Discussion, conclusion and future works will discussed in the chapter 4.

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Chapter-2: Methodology

This chapter contains the description of data collection, database design and data preparation using ArcGIS software, Distribution route network and importance of GIS in route analysis.

2.1 Data acquisition for route analysis

Map Data: Map data contains the location and shape of geographic features. Maps use three basic shapes to present real-world features: points, lines, and areas (called polygons). Points represent anything that can be described as an x, y location on the face of the earth, such as shopping centres, customers, utility poles, hospitals, or cellular towers.

 Lines represent anything having a length, such as streets, highways, and rivers.

 Polygons describe anything having boundaries, whether natural, political or administrative, such as the boundaries of countries, states, cities, census tracts, postal zones, and market areas [Data Types].

Network data: Networks used by ArcGIS 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 [ESRI,2010].

Attribute data: Attribute (tabular) data is the descriptive data that GIS links to map features. Attribute data is collected and compiled for specific areas like states, census tracts, cities, and so on and often comes packaged with map data. When implementing a GIS, the most common sources of attribute data are your own organization's databases combined with data sets you buy or acquire from other sources to fill in gaps [Data Types].

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Spatial data: Also known as geospatial data or geographic information it is the data or information that identifies the geographic location of features and boundaries on Earth, such as natural or constructed features, oceans, and more. Spatial data is usually stored as coordinates and topology, and is data that can be mapped. Spatial data is often accessed, manipulated or analysed through Geographic Information Systems (GIS) [Spatial data].

2.1.1 Data Collection

Data collection and preparation are initial steps of the project process. Data for route analysis was collected from different sources as map data and real-time data.

Map data: Map data collected from GIS Company which at first obtained the base road map from Survey Department of Sri Lanka. Map data consist of trunk roads and major roads. The attribute of roads include the speed limit on roads, name of the roads, and length of road segments. The final road map data is in WGS84 UTM zone 44N coordinate system.

Spatial data: Spatial data are data that have reference to a place. In the project of Route analysis of distribution network, the location of customers can be collected using GPS- GSM (Global Positioning System). The available customer list is to be provided by the company in each day.

Warehouse Data: Four warehouse locations are selected at main cities in Sri Lanka.

Those locations are selected for cover the study area and for the analysis purposes sample data are used as warehouse location.

Tabular Data: These data includes the list of daily customer demand. These are to be provided from Supply division of the company.

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2.1.2 GIS Database design

GIS database design is the most important step in a GIS based project. The updated and precise database provides high accurate results in GIS analysis. GIS database is developed by managing three types of data. Those are map data, spatial data and warehouse data.

Feature Type Attribute Description Roads RoadName

Code Length Speed Time

Road Name Road Type Road Length

Speed Limit of that road sector

Based on speed and the length time is calculated Warehouse Town

Latitude Longitude

Warehouse Name

Latitude value of the location Longitude value of the location CustomerDemands Name

Address City Code Latitude Longitude Demand

Customer Name Customer Address City

Customer Code

Latitude of the location Longitude of the location Customer Demand

Table 2.1: Distribution Network Database

2.2 Data Preparation

Under this heading, the methods that had been used for preparation of collected data are to be discussed. As stated earlier, three types of data were collected from different methods.

2.2.1 Road data preparation

Map data was collected from sources of survey Department. These road data were extracted from 1: 50,000 scaled maps and several survey techniques by the Survey Department of Sri Lanka and updated by the GIS Department. These data was in Sri

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Lanka datum 99 (SLD99) and converted to World geodetic system 84 (WGS 84) coordinate system.

For the purpose of finding the best route from a dataset there should be a well connected continues linear road network. To finding the errors in the connecting points topology tools in data management tools can be used. To achieve single line segments, polygon to line tools, collapse dual lines to centreline tools are also used for update the road network.

The required attributes were added to road feature class attribute table as length of road segments, Drive Speed and Time. The length and the time can be calculated in the ArcGIS database.

The Major task of road data preparation was to convert road segments in to a well built road network. This was done in the Arc Catalogue. In Arc Catalogue window, it helps to create a new network dataset for a linear feature class. All the constraints and requirements are to be selected from this primary stage or can be edited in Arc Catalogue.

For example, one way restrictions, U-turn restrictions, attributes of the dataset, units and direction properties can be added to the network dataset. Though these properties should be added from the primary stage, after creating a network dataset, new road segments can be added or remove unnecessary road segments from the road network dataset. But the road network dataset should be rebuilt using “Build entire network dataset” tool for apply those updates to the dataset.

2.2.2 Customers and warehouse data preparation

The next collected data type was spatial data. These data was collected using GPS-GSM dataset that having a WGS84 geographic coordinate system. There were 645 customer locations were collected irregularly around the country. The location data were put into a ArcGIS database to manipulate easily. The required attributes were added to database as customer code, City and location data for the analysis purposes. The location data were

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represented in decimal units and after convert the database in UTM projected coordinate system, the location data were represented in meters.

The next data type was sample data that are extracted from a dataset of town locations in Sri Lanka. Four major towns in Sri Lanka were selected as warehouse locations. When selecting warehouse locations the point density of the customer locations, the distance between each ware house locations and the road network in Sri Lanka were also considered. The base dataset was in WGS84 geographic coordinate system. These warehouse data was then add in to a ArcGIS database for manipulation. The Name of the town, location data were added to the database for analysis purposes. These dataset also converted into UTM projected coordinate system.

2.2.3 Data Preparation Results

Map 2.1 represents the road map of Sri Lanka. Roads were classified according to the type of roads and displayed in two colours as explain in legend. It contains class “A”, class

“B” roads and few class “C” roads in Sri Lanka. Class “A” roads represents trunk roads and class “B” roads represent major roads. Class “C” roads represent minor roads. These roads only designed at the places where there are on trunk roads or major roads close to customer locations. Each line segment is labelled as its class type and the number of the road that has given by the Road Development Authority of Sri Lanka. The roads are in the WGS84 UTM zone 44N coordinate system. Hence each road segment has calculated its length in meters, drive speed in kilometres per hour for each road segment and time for total length in the database.

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Map 2.1: Road Map of Sri Lanka

Map 2.2 shows all customer locations that had used for the project. There are 645 customers are inserted in to the database. The road layer and the city labels help to understand the customer distribution in the country. Each customer location has its latitude and longitude coordinate value and those locations were collected from a GPS

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dataset. Each customer location includes the Name of the customer, Code, Address, Latitude and longitude and the major city it situated at. The dataset is in the WGS84 UTM44N coordinate system and hence its coordinates values in meters.

Map 2.2: Customer Distribution

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According to the map, it seems that the distribution of the customers is not regular. The highest customer density is located near Colombo area in the western part of the country.

The minimum customer density is displayed in the northern part of the country around the cities like Anuradhapura, Trincomalee and Jaffna. The city in central part of the country like Kandy and the cities like Galle and Matara in the southern part of the country have an average customer distribution.

2.2.4 Coordinate systems

There are several kind of coordinate systems that have been used from the beginning of surveying and mapping. The coordinate systems are used for uniquely determine a position of a point. The coordinate systems are in several types that change according to the calculating pattern they use and the characteristics. Some major coordinate systems can be identified as,

 Cartesian coordinate system

 Polar coordinate system

 Cylindrical and spherical coordinate systems

 Homogeneous coordinate system

 Geographic Coordinate system

All these coordinate systems can be used for represent a location in 3 dimensional space.

Though these coordinate systems give different values for one position, there are relationships between each and every coordinate system, so that one coordinate can be transfer into another coordinate type is called as coordinate transformation.

There are many kind of coordinate systems that used by different countries, regions with the use of different Datums. In Sri Lanka, SLD99 datum was used for surveying and mapping since 2000. The [horizontal] Sri Lanka Datum 1999 (SLD99), has one origin point at Institute of Surveying and Mapping, Diyatalawa (ISMD). The national map-grid

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coordinates, termed SL_GRID_99, are computed using a Transverse Mercator projection on the Everest 1830 ellipsoid [Abeyratne P.G.V., Featherstone W.E., Tantrigoda D.A., 2010]. Therefore Survey department of Sri Lanka uses the SLD99 datum for the surveys and mapping in Sri Lanka.

Longitude of the central meridian 80° 46' 18.16710" E Latitude of origin 07° 00' 01.69750" N

Central scale factor 0.9999238418

False northing 500000.000 m

False easting 500000.000 m

Semi-major axis 6377276.345 m

Reciprocal flattening 300.8017

Table 2.2. Transverse Mercator projection parameters for the Sri Lankan SL_GRID_99 based on the SLD99 and using the Everest 1830 ellipsoid Parameter Value [Source:

Abeyratne P.G.V., Featherstone W.E., Tantrigoda D.A., 2010]

When working with GIS software, it is very important to have a same coordinate system for every data source. The WGS 84 UTM zone 44N coordinate system was used as the coordinate system for all data types. At the beginning, converting all types of data in to same coordinate system ArcGIS 9.3 software was used.

WGS84 is a World Geodetic system that was recognized by the geodetic community in the early 1980s.WGS 84 coordinate system is the reference coordinate system that used by the Global Positioning system. It is geocentric and globally consistent within ±1 m.

Current geodetic realizations of the geocentric reference system family International Terrestrial Reference System (ITRS) maintained by the IERS are geocentric, and internally consistent, at the few-cm level, while still being meter-level consistent with WGS 84 [World Geodetic System].

The definition of this coordinate system follows the criteria outlined in the International Earth Rotation Service (IERS) Technical Note 21. These criteria are repeated below:

 It is geocentric, the centre of mass being defined for the whole Earth including oceans and atmosphere.

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 Its scale is that of the local Earth frame, in the meaning of a relativistic theory of gravitation.

 Its orientation was initially given by the Bureau International de l’Heure (BIH) orientation of 1984.0.

 Its time evolution in orientation will create no residual global rotation with regards to the crust.

The WGS 84 Coordinate System is a right-handed, earth fixed orthogonal coordinate system. The WGS 84 Coordinate System origin also serves as the geometric center of the WGS 84 Ellipsoid and the Z-Axis serves as the rotational axis of this ellipsoid of revolution[International Hydrographic Bureau, 2003].

Universal transverse Mercator (UTM) geographic coordinate system is a grid based method that is used for represent horizontal position in a 2 dimensional Cartesian coordinate system. [Universal Transverse Mercator coordinate system,2011 ].It is a projected coordinate system.

Figure 2.1: Universal Transverse Mercator Zone 44

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UTM 44N refers to the countries that are in world N-hemisphere 78E-84E zone [Spatial reference]. Sri Lanka is located in the UTM zone 44N.

When creating a route analysis system, it is necessity to understand the measuring unit that is going to used in the analysis. The UTM coordinate system provides a constant distance relationship anywhere on the map and coordinates are measured in meters.

[Why use UTM coordinates]

2.2.5 Database Handling

The data is gathered from different sources and the manipulated data into the GIS when developing an effective database for the project. Base station is nothing but the company, where all the data like the road map with names, locations of warehouses and customers, customer’s information and other system constraints are stored. This base station connects with the customers and collects daily customer demand. These data use to allocate daily customers to each warehouse. Then the shortest and fastest routes were calculated for each ware house using Shortest route and Fastest Route models. With the help of VRP model, the optimal routes for each vehicle in the warehouses were also calculated. The information of customer allocations is previously generated and stored in the database. Updating and deleting a data is not a problem for the user. The company can easily update the data if it’s there any new addition or any deletion of customers, warehouses or vehicles.

2.3 Distribution route network

The main goal of any distribution network system is providing safe, efficient and reliable delivery for company goods. Distribution network plays an important role in the business world from warehouses to customers. Every year, numerous companies all over the country must evaluate the efficiency of the supply of their company goods according to the

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customer needs. Many times it happens that, a company cannot achieve their targets when supplying goods to their customers in efficient way. Normally there are many frauds happening in the transportation sector due to miss management of supplying goods. This may cause to benefit losses in many of these companies.

One solution to this problem is to get the support of a distribution network system. There are many routes which can travel between two locations in a route network. Sometimes these routes are not optimal; these types of routes waste time or money for those involved, necessitating an accurate distribution network model to increase efficiency. But solutions like this must also considered some factors like economic concerns, time issues, route efficacy, etc.

While designing the distribution route network, time for whole process, safety of goods, road conditions, economy of operation, user’s convenience etc.., should be considered before designing and planning the vehicle routes.

In distribution route network the two most visible problems are routing and allocating customer locations. In the routing problem every customer is assigned to a warehouse and those particular warehouses are taken as individual starting points to form routes. In the morning vehicles in each warehouse follows these routes, from warehouse to customer location to another, unloading goods to each customer according to their demands.

2.3.1 Historical perspective of route analysis systems

Most of modern route analysis technologies began with the increase of automobiles and planning of new roads. There are many GIS related and other technologies were used for finding solutions for optimal route analysis. Therefore different types of researches were done by several students and GIS communities.

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Best Route Finding Based on Cost in Multimodal Network With Care of Networks Constraints by Azadeh Keshtiarast and Pro. Ali A. Alesheikh, 2006 is a route analysis project that use one of these technologies. In this project, the main goals were consideration of various modes of transportation and their constraints and present an algorithm to find the best route with combination of various transportation modes based on cost and minimum change of mode in travelling. In route finding algorithm between two nodes, the algorithm starts from the origin and add a connect arc to the route. The project is mainly based on programming.

Transportation data model implementation for Iranian roads network by Somayeh Dodge and Pro. Ali A. Alesheikh, 2005 is another GIS based route analysis project. The goal of the project was to present a data model to design an object-relational geodatabase for Iranian road network. ArcGIS transportation data model was used for create geodatabase in this project. Topologically Integrated Geographic Encoding and Referencing (TIGER) files that have published by U.S. Bureau of the Census were used for GIS transportation model. This transportation model is used for create address locator for geodatabases.

GIS is one of the most important technologies that is used for route analysis systems in the modern world. Optimal route analysis using GIS by D. Thirumalaivasan, Prof. V.

Guruswamy, is one of these route analysis projects. The project was done for the ambulance service network of Trauma care Consortium in Chennai city. The origin of the route was the place where the accident occurs and the destination was the nearest hospital. Using the buffer zones the ambulance locations and hospitals located within the buffer zones are selected. The analysis leads to two routes, one from the ambulance location to accident spot and other from accident spot to hospital. The ROUTE module in Arc-Info software was used for finding optimal route.

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2.3.2 Importance of GIS in distribution route network

A geographic information system (GIS) is a technology that widely used in the developing world. Many government and private organizations use GIS in many fields as a decision making tool. GIS helps them to make decisions very quickly and it saves a lot of time and money. As GIS is a widely spreading technology, numerous definitions of GIS now exist.

According to the definition of Lloyd & Queen (1993) “a GIS is a computerized, integrated system used to compile, store, manipulate, and output mapped spatial data.”

GIS is widely used for finding optimal route in many researches. In literature, GIS based route determination for road highway, railway, power line, pipe line have been studied.

According to these studies, GIS is a useful technology and environmentally helpful and cheaper than traditional one[Cheng and Chang, 2001].

GIS is a technology that plays a very important role in tracking, navigating, routing and scheduling vehicles in transportation. GIS route analysis has been a major area for research and development. Selecting the best route is one of the oldest spatial problems.

GIS is an effective solution for this problem. During last few years, there have been many experiments to find the possibility of automated route analysis system. By using GIS software, routes can be planned to give the most economical operation with the help of travel distance and road conditions. Actual street distance between two points, u-turn possibilities also concern in calculate the road network distances.

“Any system of interconnected linear features is a network. Solving problems involving networks is network analysis” [Bundick J., 2003]. Predefined a distribution route network and reduction of the transportation cost can be done by using the network analysis based applications.

2.3.3 Comparison between the Distribution route analysis using GIS and other studies

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There are some route analysis researches and projects can be found in the history as stated under previous sub heading. Route analysis can be done in several methods and using several techniques. GIS is one of the most developed techniques in modern world for route analysis and modelling.

D. Thirumalaivasan and Prof. V. Guruswamy’s project is mostly related to the study of this project. But there are many differences and improvements can be identified throughout the thesis. Basically the above stated project is discussed about an optimal route analysis between two main locations that described earlier as origin and destination. The system had to find the optimal route for these two locations. In Distribution route network project, a number of intermediate locations have to be covered according to the daily customer demands. The order of the customer location list will be defined in the process of shortest or fastest route analysis. And there is a requirement of start from ware house and cover all the customer demands and again come back to the ware house. The optimal route is calculated for full travel. In Optimal route analysis using GIS project, the nearest hospital was selected using buffer technique. But in Distribution route network project, closest facility analyst is to be used for more effective analysis. The buffer techniques can be used for identify the nearest location along direct lines. But using closest facility analysis, most precise results can be obtained.

Implementing models for the use of route analysis is another part of the current project.

Transportation data model implementation for Iranian roads network by Somayeh Dodge and Pro. Ali A. Alesheikh, 2005 had used some model based analysis. In Distribution route network using GIS project, GIS models were created for achieve the user friendliness of the project. There were two main models were created, one for route analysis and other for customer allocation. The main purpose of creating these two models is to compress the total process and convert the route analysis in to simple work.

2.4 Contribution of ArcGIS Software for data processing

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GIS softwares are used for achieve better information for decision making. GIS can be used for represent real world objects on maps. GIS is to use display, manipulate and analysis spatial data. Spatial data are data reference to a place. Spatial objects are represented as point, line or polygon forms. ArcGIS is a GIS software that used by many GIS professionals. It is developed by Environmental Systems Research Institute (ESRI). It can be installed on both the UNIX and Network PC platforms.

Figure 2.2: Chart of ArcGIS techniques in data processing

ArcGIS software techniques were used for several applications in data processing as it was in data preparation. ArcGIS software provides many techniques in attribute data handling that was used in data processing. The techniques and tools that had used for data processing are very useful for data manipulation. Figure 3.2 represents the ArcGIS techniques that had used for data processing.

Data manipulation techniques-

ArcGIS Desktop

Data Manipulation techniques

Network data handling

Select Features Export data

Copy Features Join data

Route Analyst VRP analyst

Closest Facility Analyst

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These techniques were used at the process of allocating customers to each warehouse.

By using attribute data handling techniques, selecting spatial data according to required criteria and export data into separate data layers from the one data layer was done.

Data manipulation techniques are highly used in the process of daily customer allocation process. There are several techniques as copy features, join in data management tools and analyst tools were used for creating the Customer Allocation model.

Network analyst tools-

ArcGIS Network Analyst provides network-based spatial analysis, such as routing, fleet routing, travel directions, closest facility, service area, and location-allocation. Closest facility network analyst tool was used for the process of allocating customers to each warehouse. Closest facility network analyst does not provide a point data output. But in this process we have to obtain a point data result. Therefore several other data processing techniques as exporting, spatial selection had to be used for fulfil the final expectation.

Route network analyst module was used for finding the shortest and the fastest route for each warehouse to daily customer locations and vice versa. Two different network analyst models were created with having different conditions for obtaining shortest route and fastest route results for each warehouse. Shortest route model and fastest route model were created using Network analyst tools. The general conditions that had used for creating these models are as;

 The warehouse should be the origin and the destination of the route

 The distance or the speed as impedance

 The distances should be calculated in kilometres in direction window

 The time will be calculated in hours in direction window

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ArcGIS Vehicle routing problem network analyst module was also used for obtained a more descriptive results with the same set of data. This module helped to obtained optimal routes in each case with more conditions. Several conditions that had used for obtained the final results are as follows.

 The quantity of the demand of each customer

 The capacity, total travel time and travel distance, Maximum number of customers can be served by a vehicle

 The service time for unload the goods, cost per unit time and distance

 The warehouse that supplies demand and the working time period of the warehouse and the customers

Further that the general conditions that have used in finding an optimal route as distance of the road segments, speed of the vehicle, U turn restrictions were used for getting the results. This process helps to obtain several optimal routes with using a set of vehicles for one warehouse to customers and vice versa.

D. Thirumalaivasan and Prof. V. Guruswamy was used ArcGIS network analyst for finding a optimal route for the ambulance service network of Trauma care Consortium in Chennai city. The origin of the route was the place where the accident occurs and the destination was the nearest hospital. Closest facility analysis module, Route module and Vehicle routing problem module were used as the analysis techniques throughout this project.

2.4.1 Importance of Route module in Route analysis for distribution network project Route module is one of the most important analysis techniques in ArcGIS Network analyst. It helps to find the route from origin to destination and same time it facilities to cover intermediate locations between origin and destination, adjust the order of serving and follow the restrictions given by the user. Route module helps to find the shortest or fastest route for a set of data.

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2.4.2 Importance of Closest Facility module in Route analysis for distribution network project

Closest Facility module is also a very effective analysis technique in ArcGIS Network analyst. It helps to find a set of closest routes from one origin to several destinations. This process can be done in two ways. They are finding closest destinations along the routes and using straight lines. By using set of origins and set of irregular locations, a set of routes for nearest locations for each origin can be generated. Buffer techniques cannot use for this type of location sets because it calculates nearest locations using direct distances. Therefore Closest Facility module can be used for allocating customer locations for each warehouse most precise way than using buffer techniques.

2.4.3 Importance of Vehicle routing problem module in Route analysis for distribution network project

Vehicle routing problem module is another most important analysis technique in ArcGIS Network analyst. It helps to find the routes from origin to destination and same time it facilities to cover intermediate locations between origin and destination, adjust the order of serving, schedule the vehicles according to the time window constraints and follow the restrictions given by the user. Vehicle routing problem module helps to find the optima route for one vehicle or a set of vehicles.

2.5 Risk of optimal routing

The analysing optimal routing as the solution for route planning using GIS is becoming a important process in real world. But designing this type of analysis model, there are many conditions to apply. These conditions can be changed according to the user requirements.

The creation of optimal routing can be depend on the reasons as number of vehicles that can be used in the required day, the demand of the customers in each day, the road conditions in a selected day and the facilities of the warehouse.

Some conditions can be applied according to the requirements and facilities of the user.

But sometimes there may some unexpected problems as road traffic jams, road closed or

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no sufficient vehicles availability. Therefore the optimal route planning has some practical problems.

The optimal routing mainly depends on the time window. If one vehicle cannot follow the time window then it cannot complete the route in the pre-planned schedule. Normally, each customer has a service time. If the service time exceeds due to unexpected reason, then again vehicle cannot complete the route according to the plan. These kinds of general and natural problems can be affected to the optimal route planning.

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Chapter-3: Processes and Results

3.1 System Design and implementation

3.1.1 Customer Allocation

Each company has their own customers. Therefore in the first step of the research, a list of customers was collected. This List of customer locations was obtained with the help of GPS technology. Warehouses are the base stations of the study area. These warehouses distribute goods to customers. Normally, one specific warehouse supplies goods for one customer. Therefore, each customer should allocate to one warehouse. In other hand one warehouse can serve many customers.

3.1.2 Closest Facility Network Analysis

Closest facility analysis is a tool that can be used for finding the nearest locations to a base station. The nearest location can be calculated with respect to the length or time.

The length also can be measured along the street lines or direct lines. For the required accuracy of the results these options can be changed.

The customer locations have been spread irregularly all over the study area. The closest facility tool helps to allocate each customer location to a ware house. The closest facility tool provides the linear results. To obtain the customer locations, all customer locations should export with each linear result.

3.1.3 Network Analysis Shortest and Fastest Route

Using GIS Softwares vehicles can be routed to give the best service for the customers and routes can plan to give the most economical operation of vehicles with distance and road conditions being the major criteria for economical routing. To find the best shortest and fastest route for the vehicles and with the daily customers already placed, ArcGIS Network Analyst is used for distribution routes for each warehouse. Two different routes were created 1) Shortest Route 2) Fastest Route which will result in decreasing the fuel

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consumption and will save time. Network Analyst is used to define the better routing for the distribution network. After allocating daily customer locations for each warehouse, Network Analyst will calculate the shortest network location and fastest network location and represent the daily customer locations with the located symbol. The order of daily customer locations can be changed on the Network Analyst Window and it is very easy to update the customer locations and add or edit new customer locations daily in Network Analyst. Another advantage of using network analyst is that you can also add a barrier on the route, to represents a road block and a new alternate route can be designed in order to avoid the road block. The difference between the two routes for the same origin and same destination is huge. Therefore, it is important to remember that the shortest route is not always the fastest route.

3.1.4 Optimal Route and vehicle scheduling

As discussed earlier, ArcGIS supports to supply a best service to customers by planning routes for set of vehicles. To find the optimal routes for the set of vehicles and with the daily customers and warehouses already placed, VRP model that created using Network Analyst vehicle routing problem tool was used. Several routes were created for one set of customers that will result in decreasing the total cost and will save time. After allocating daily customer locations for each warehouse, VRP model will calculate the optimal routes and represent each route and daily customer locations that serve by the vehicle separately. The order of daily customer locations will be changed according to the analysis and it is very easy to update the customer list and add or edit new customer daily in VRP model. The results of optimal routes that created using the VRP model can be changed according to the conditions of vehicles and customer demands.

3.1.5 Add Daily Customer demand

Customer demand can be changed daily according to the customer requirements.

Therefore it is required to know the list of customers who need to supply goods and their

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demands of that day. Normally, these lists can be prepared in excel format. These lists then can be added to Arc GIS map using several methods. For that, tools can be created using Visual Basic. This type of DLL file helps to add the daily customer demand list.

3.2 Results

This section represents the outcomes of route analysis of distribution network system achieved by using ArcGIS 9.3 Network Analysis. These results were obtained from Closets facility analyst, Daily customer allocation model, Fastest and Shortest route models and VRP (Vehicle routing problem) model.

Results from closest facility analysis

The closest facility analysis was used for finding out the closest ware house location for each customer location. There are four major ware house locations were selected at major towns in Sri Lanka for the purpose of closest facility analysis.

Figure 3.1: Customer Location allocate for Colombo warehouse using closest facility

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Figure 3.1 represents the closest facility for Colombo warehouse. The blue colour routes that obtained from closest facility analysis from Colombo warehouse to each customer location. Customers that allocate to Colombo warehouse are represented in red dots. By extracting the customer locations using closest facility results, the customer locations allocate to Colombo warehouse were generated.

Figure 3.2: Customer Location allocate for Kandy warehouse using closest facility

Figure 3.2 represents the closest facility for Kandy warehouse. The red colour routes that obtained from closest facility analysis from Kandy warehouse to each customer location.

Customers that allocate to Kandy warehouse are represented in green dots. By following the same procedure as described above, the customer locations allocate to Kandy warehouse were generated.

Figure 3.3 represents the closest facilities for Galle warehouse. The purple colour routes that obtained from closest facility analysis from Galle warehouse to each customer location. Customers that allocate to Galle warehouse are represented in maroon dots.

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Figure 3.3: Customer Location allocate for Galle warehouse using closest facility

Figure 3.4: Customer Location allocate for Anuradhapura warehouse using closest facility

Figure 3.4 shows the closest facilities for Anuradhapura warehouse. The magenta colour routes that obtained from closest facility analysis from Anuradhapura warehouse to each

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customer location. Customers that allocate to Anuradhapura warehouse are represented in purple dots.

Map 3.1: Customer allocation for each warehouse

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Map 3.1 represents the Map of customer allocation for each warehouse. The legend of the map represents the warehouse and the customers those allocate for each warehouse.

According to the map, four warehouses were selected from major cities of Sri Lanka.

Those are Colombo, Kandy, Anuradhapura and Galle. These warehouses were used as facilities and the customer locations were used as incidents for the closest facility analysis.

Customer locations that allocate for each warehouse are represents using different colours in the map. The warehouses are labelled with their names and represented by a symbol as it has described in legend.

The each customer location is allocated to a one warehouse. According to the results, total number of customers and maximum covering distance are as follows.

Warehouse No. of customer locations Maximum distance from warehouse(meters)

Colombo 373 300

Galle 92 500

Anuradhapura 29 350

Kandy 146 600 Table 3.1: Customer allocation for each ware house

Analysing the values of the table, it shows that the number of customer locations allocate to a ware house is not distance from warehouse. The reason of this result is the irregular customer location of the country. Therefore buffering technique cannot use for this type of location allocation.

Colombo warehouse is used to serve customers in the west part of the country and it covers the maximum customer locations in the country. Colombo is also the main business city in Sri Lanka. Therefore there are many customer locations situated in this region. Galle warehouse covers the southern part of the country and Anuradhapura warehouse is used to serve the northern part of the country. These two warehouses cover large areas of the country but less number of the customers. Kandy warehouse serves the customers who are located in the central part of the country. It covers large area of the country and large number of customer locations as well.

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Results of allocate daily customers for each warehouse

Figure 3.5 represents the total daily customer locations from the list that has given by a day. When adding these locations to ArcGIS the shortcut tool that created using Visual Basic can be used. The 84 customers out of 645 customers were selected as a sample list of daily customers. The customers are selected irregularly and the list covers the customers around the country.

Figure 3.5: Daily customer locations

To find out the daily customers that allocate for each warehouse, previously created customer allocation feature datasets were used. By using Daily Customer allocation model, each daily customer location can be allocate to each warehouse from the total list of daily customer locations quick and simply. All customer locations were assigned to each warehouse separately at the same time.

Figure 3.6 represents a ArcGIS representation of a daily customers in Colombo area from the daily customer list. There are 33 customers out of 84 customers were selected using pre designed daily customer allocation model. The customer locations represent by a blue colour sign and the warehouse represents in black sign and a label.

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Figure 3.6: Colombo daily customer locations out of total daily customers

Figure 3.7 represents a ArcGIS representation of a daily customers in Kandy area from the daily customer list. There are 31 customers out of 84 customers were selected using pre designed daily customer allocation model. The customer locations represent by a pink colour sign and the warehouse represents in black sign and a label.

Figure 3.7: Kandy daily customer locations out of total daily customers

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Figure 3.8: Anuradhapura daily customer locations out of total daily customers

Figure 3.8 represents a ArcGIS representation of a daily customers in Anuradhapura area from the daily customer list. There are 8 customers were selected as customers allocate to Anuradhapura warehouse. The customer locations represent by a purple colour sign and the warehouse represents in black sign and a label.

Figure 3.9 represents a ArcGIS representation of a daily customers in Galle area from the sample daily customer list. There are 12 customers were allocated for Galle warehouse using pre designed daily customer allocation model. The customer locations represent by a maroon colour sign and the warehouse represents in black sign and a label.

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Figure 3.9: Galle daily customer locations out of total daily customers Shortest route results

To analyse the shortest route for each daily customer list, ROUTE module was used. In this process, the distance was selected as the impedance. The direction window describes the details as Name of the route have to be selected to travel, the distance should be travel on that route and direction that have to turn. In each route analysis, the warehouse location was preserved as first and the last destination of the route.

Shortest route from Colombo warehouse to selected 33 customers and back to Colombo warehouse represents in Figure 3.10 using green colour line. The order of the customers is shown in numbers in green circles. According to the figure, route directions with a clear description of road segments and travel distance in kilometres are given by the direction window. Each road map also can be seen by using the direction window. The total shortest distance was calculated as 381 kilometres.

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Figure 3.10: Shortest route for Colombo warehouse distribution and direction window

Figure 3.11: Shortest route for Galle warehouse distribution and direction window Shortest route from Galle warehouse to selected 12 customers and back to Galle warehouse represents in above Figure 3.11 in blue colour line. The order of the customers

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is also given by numbers in cyan colour circles. It also represents the direction window for the created route. It describes route directions with a clear description of road segments and travel distance in kilometres. Each road map also can be seen by using the direction window. The total shortest distance was calculated as 497.2 kilometres.

Shortest route from Anuradhapura warehouse to selected 8 customers and back to Anuradhapura warehouse represents in the following Figure 3.12 in magenta colour line.

The order of the customers is also represented in magenta colour circles. The details of route directions, road names and travel distance in kilometres are given by the direction window that represents in the figure. Each road map also can be seen by using the direction window. The total shortest distance was calculated as 393.9 kilometres.

Figure 3.12: Shortest route for Anuradhapura warehouse distribution and direction window Figure 3.13 represents the shortest route from Kandy warehouse to selected 31 customers and back to Kandy warehouse in magenta colour line. The order of the customers is shown in numbers using magenta colour circles. The route directions with a clear description of road segments and travel distance in kilometres are given by the

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direction window that represents in the figure. Each road map also can be seen by using the direction window. The total shortest distance was calculated as 640.7 kilometres.

Figure 3.13: Shortest route for Kandy warehouse distribution and direction window Fastest route results

To analyse the fastest route for each daily customer list, ROUTE module was used. In this process, the Speed was selected as the impedance. The direction window describes the details as Name of the route have to be selected to travel, the time should be travel on that route and direction that have to turn. The time for travel was calculated by using the distance and the speed of each route segment. The direction window provides a route map for each route segment.

Figure 3.14 represents the fastest route from Colombo warehouse to selected 33 customers and back to Colombo warehouse in green colour line. The order of the customers is shown in numbers using green colour circles. The route directions with a clear description of road segments and travel time are given by the direction window that represents in the figure. The total time was calculated as 9 hours 52 minutes.

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Figure 3.14: Fastest route for Colombo warehouse distribution and direction window Fastest route from Galle warehouse to selected 12 customers and back to Galle warehouse represents in Figure 3.15 using magenta colour line. The order of the customers is shown in numbers in magenta circles.

Figure 3.15: Fastest route for Galle warehouse distribution

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The total time for journey was calculated as 12 hours 25 minutes.

Figure 3.16: Fastest route for Anuradhapura warehouse distribution and direction window Fastest route from Anuradhapura warehouse to selected 8 customers and back to Anuradhapura warehouse represents in the above Figure 3.16 in green colour line. The order of the customers is also represented in chrysophase colour circles. The details of route directions, road names and travel time are given by the direction window that represents in the figure. The total time was calculated as 8 hours and 7 minutes.

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Figure 3.17: Fastest route for Kandy warehouse distribution and direction window Fastest route from Kandy warehouse to selected 31 customers and back to Kandy warehouse represents in above Figure 3.17 in blue colour line. The order of the customers is also given by numbers in blue colour circles. It also represents the direction window for the created route. It describes route directions with a clear description of road segments and travel time. The total shortest distance was calculated as 13 hours 25 minutes.

Warehouse Total Distance in shortest path(kilometres)

Total Distance in fastest path(kilometres)

Colombo 381.03876 401.566286

Galle 497.17829 531.892016

Kandy 640.70479 658.076126

Anuradhapura 393.93935 535.437673

Table 3.2: Distance results

Table 3.2 represents the distances that obtained from shortest route and fastest route analysis for each warehouse to the daily customer demand of it and back to warehouse.

The results are calculated in meters. By analysing the results of each case, it can be identified that the results obtained from two analysis techniques are not same for a same set of data. According to the results, the fastest route analysis always gives more distance

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than the distance in shortest route analysis. It means the fastest route is not the cost effective route in each case.

Warehouse Total time in fastest path

Colombo 9hr 52 min

Galle 12hr 25min

Kandy 13hr 25min

Anuradhapura 8hr 7min

Table 3.3: Total time for each route

But according to the results of Table 3.3, in fastest route analysis, the user can obtain the total time for each travel. The results were calculates to a one minute accuracy. In a fastest route analysis, a user can manage the vehicles according to the time schedule.

Therefore, the fastest route analysis is also important. But if a user needs to reduce the cost, the most relevant analysis that he should use is shortest route analysis.

Optimal route results using Vehicle routing problem

When analysing total times for each travel in above table 3.3, it shows travel time is high.

Sometimes a company may have a total travel time limit for one vehicle. The total travel distance for one vehicle per day can be exceeded when travelling along the selected routes. In this analysis process, a company can use number of vehicles to supply their products to the customers according to their daily customer demand.

To analyse the optimal routes for each daily customer list, vehicle routing problem in Network analyst tool was used. In this process, both time and distance were selected as attributes. The daily customer list adds as the order and warehouses add as depots. The set of vehicles add as routes. According to the given conditions each vehicle visits a depot twice. The routes will not allow violating the time window. The direction window describes the details as Name of the route have to be selected to travel, the time should be travel on that route and direction that have to turn for each vehicle and the set of customers. The time for travel was calculated by using the distance and the speed of each route segment.

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The direction window provides a route map for each route segment. The start time and the end time for a journey are also displayed.

Figure 3.18 represents four optimal routes in four different colours that obtained from VRP model for Colombo warehouse to daily customer list and vice versa. The direction window provides the road descriptions and directions, travel time for each road segment and total travel time.

Figure 3.18: Optimal route and direction window for Colombo warehouse using VRP model

Vehicle Name Total Travel Time Number of serving customers

Cost for driver(in Sri Lankan

Rupees)

V1 6 hr 42 min 8 802.42

V2 8 hr 10 879.98

V3 7 hr 57 min 7 877.09

V4 7 hr 22 min 8 841.58

Table 3.4: Details of each vehicle for Colombo warehouse

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The Table 3.4 represents the total travel time, number of serving customers and total cost for each vehicle for Colombo warehouse. Further that, each route will provide regular time cost, total distance, distance cost and total wait time. The cost distance can be calculated using Total distance and cost for unit distance.

Figure 3.19 shows six optimal routes in different colours that obtained from VRP model for Kandy warehouse to daily customer list and vice versa. The direction window provides the road descriptions and directions, travel time for each road segment and total travel time.

Figure 3.19: Optimal route and direction window for Kandy warehouse using VRP model

Vehicle Name Total Travel Time

Number of serving customers

Cost for driver(in Sri Lankan Rupees)

V1 7 hr 30 min 4 849.95

V2 6 hr 3 min 5 762.92

V3 6 hr 48 min 9 807.70

V4 7 hr 39 min 5 859.49

V5 7 hr 58 min 5 877.64

V6 7 hr 49 min 3 869.48

Table 3.5: Details of each vehicle for Kandy warehouse

submitted within the UNIGIS MSc. programme at the Centre for Geoinformatics (Z_GIS)

Master Thesis