Using GIS Network Analyst to Solve a Distribution Center Location Problem in Texas

Texas A&M University, Zachry Department of Civil Engineering

Instructor: Dr. Francisco Olivera, CVEN658 Civil Engineering Applications of GIS

Number of Words: 4039 Number of Tables and Figures: 12

Author: Chunyu Tian

Submitted Date: 12-06-2010

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CONTENTS ABSTRACT ................................................................................................................................ - 2 -

1. INTRODUCTION ............................................................................................................................. - 3 -

1.1 Background ....................................................................................................................... - 3 -

1.2 Problem Description .......................................................................................................... - 4 -

2. LITERATURE REVIEW ................................................................................................................. - 6 -

3. METHODOLOGY ............................................................................................................................ - 7 -

4. APPLICATION AND RESULT DISCUSSION ........................................................................ - 13 -

4.1 Result Discussion ............................................................................................................ - 13 -

4.1.1 Sensitivity Analysis .................................................................................................. - 13 -

4.1.2 Multimodal Transport ............................................................................................... - 13 -

4.1.3 Service Area Analysis .............................................................................................. - 14 -

4.1.4 Closest Facility Analysis .......................................................................................... - 15 -

4.2 Application ...................................................................................................................... - 15 -

5 CONCLUSIONS ............................................................................................................................... - 15 -

6. REFERRENCES .............................................................................................................................. - 17 -

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ABSTRACT

In this paper, a distribution center location problem is studied using the network analyst

extension in ArcGIS. This distribution center is responsible for purchasing raw materials from

five suppliers located in five different cities, producing products and sending them to four stores

in four big cities, which are Houston, Austin, San Antonio, and Dallas respectively. The amount

of raw materials purchased from suppliers and demand of each store are given. Transportation

cost is assumed to be the main factor in choosing the location of this distribution center. The

freight transportation is outsourced to third-party logistics companies, whose charge rate is time

based. The transportation mode is chosen as truck. College Station, Waco and Conroe are the

three distribution center locations to choose from. For each of them, network analyst is used to

find the minimum cost route between the distribution center and those cities. After that, the

amount information is added to calculate the total cost. The result shows that College Station is

the best location given those demand and supply amount. A sensitivity analysis is done to see the

influence of amount change on the result. The service area of College Station is obtained to help

make decisions in new store locations.

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1. INTRODUCTION

1.1 Background

Geographical Information System (GIS) has been widely used in logistics during the past few

years. GIS is a set of tools that obtain, store and analyze data related to locations. Network

analyst is a very important extension in GIS software. Network analyst can dynamically model

realistic network conditions [1]. Given the data of roadways and cost attributes, the network

analyst can be used to analyze problems such as vehicle routing, closest facility and service area.

The purpose of this project is to make use of network analyst to find out the best location of a

distribution center from three cities in Texas including Waco, College Station and Conroe. The

functions used in this project include optimal routing, service area and closest facility.

Distribution center is developed from the concept of warehouse. The function of

distribution center can be divided into mainly four kinds. The first function is to purchase raw

materials from suppliers. In this project, there are five supply cities. As a result, five routes

connecting the supply city and distribution center are created. The second function is

manufacturing. After receiving the raw materials, the distribution center is responsible for

making products. The third function is material and product storage, which is the same with

warehouse. The fourth function is to send the products to the stores located in the demand cities.

Therefore four routes connecting the distribution center and demand cities are created. In this

project, the first and the fourth function are considered in the calculation process. In both the first

and the fourth function, transportation is included. Form figure 1, we can see the structure of the

problem studied in this project. In most logistics activities, transportation cost takes more than

60% of the total cost.

As a result, when choosing the location of a distribution center, the transportation cost is

the main factor that needs to be considered. The other factors such as the land acquisition, staff

salaries and technologies are very close for the three option cities within the study area of Texas.

Based on the above assumptions, the author choose transportation cost as the decision cost to

find the best location of this new distribution center.

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Figure 1: The transportation process between distribution center, supply and demand cities.

In this project, a company named LCG is used as the study object. It is an imagined

company by the author to make use of GIS. There are two reasons that the author uses an

artificial company to study the problem. First, most of the data such as the amount of demand

and supply and also the locations are confidential. Second, such problems are faced by many

companies and as long as the data are given, this problem can be used with the method in this

project. As a result, this project is more like an academic project instead of solving an existing

problem. In real situations, the facility location problem is very complicated the cost structure is

too hard to be accurately estimated. Several assumptions are made in order to simplify the

problem and make use of GIS network analyst extension.

1.2 Problem Description

LCG is a big company located in California selling chairs and sofas. In recent years, the

demand in Texas for products from LCG has increased tremendously. Originally, those products

are transported from the warehouse in Arizona. It is no longer economical to do it the same way.

This company decides to build a distribution center to purchase raw materials and distribute

products to four stores located in Houston, Austin, Dallas and San Antonio. In this project, only

the truck transportation is considered to transport the freight. The charge rate is based on time

and amount. There are three locations for choose. In this paper, for each location, the best route

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and mode combination are decided and the minimum cost is obtained. The comparison of

minimum costs for three locations provide decision making basis for the manager of the

company.

(1) In this project, only the transportation cost is considered.

(2) A third party logistics company is assumed to be used to transport freight.

(3) The transportation cost is a time based cost.

(4) There are five supply cities and four demand cities. The amount of supply and demand is

fixed or change with the same rate.

(5) The best location of distribution center is the location with minimum transportation cost.

A third party logistics company provides transportation service based on the amount and

time. The method of using a logistics company simplifies the problems because if we use our

own trucks, there cost would be very complicated. It will include fixed cost for trucks, the salary

for drivers and also the fuel cost, maintenance cost. As the amount of freight is very large, it is

assumed that this third party logistics company will arrange some trucks that specially serve

LCG between the distribution center and those supply and demand cities. As a result, this third

party logistics company just needs to find the shortest travel time route.

In this project, the most important assumption is that the amount of supply and demand in

each city will remain stable or have the same trend of increasing or decreasing. Another

assumption is that the third party logistics company will choose the lowest cost route to transport

the freight. Based on those two assumptions, the location selection problem becomes the lowest

transportation cost selection problem.

The structure of this report is as following. First, the past research will be reviewed. The

location selection problem, the application of GIS in location selection and also other areas are

briefly introduced. Then the methodology is shown and the detailed procedure is listed step by

step. After that, the result is discussed. Further analysis including closest facility, sensitivity

analysis and also service area are displayed. The application of this method is also elaborated. It

can be used in school and hospital location selection. In the last part, this project is concluded.

For future research, better estimation of travel time and also integration of other transportation

modes are all possible.

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2. LITERATURE REVIEW

Location selection is a problem faced by all companies, government agencies, education and

public services. In the field of business, distribution center location selection is a very important

issue faced by nearly all the companies. The most widely used method is to build optimization

models to find the best location. The models can be divided into continuous location models,

network location models and continuous location models [2]. In most of those researches, an

artificial network needs to be used first in solving the problem. The problem of using those

artificial networks lies in their inflexibility to the change of real networks. In addition, massive

inputs are needed to build the network. As the network correspond to the real world data and

those data are usually available as GIS data, more and more people are using GIS to analyze this

problem. In [3], the fundamental logic of network analyst is summarized as a meta-heuristic

algorithm based on Tabu Search. A multi facility location model is proposed in [3]. The dynamic

movements of customers are considered. The objective is to find the best locations of multi

facilities with maximized profits. They consider the revenue as well as the logistics cost. The

authors first build an optimization model and input them into GIS using programming language

C++. Compared with the traditional method, they save a lot of time in the network generation

and make it more flexible and closer to the real situations.

Network Analyst is an important extension in ArcGIS. In the past few years, massive

research has been done using network analyst. Network analyst can solve best route problem,

closest facility, service area, O-D matrix and vehicle routing problem [4]. Before using the

network analyst, a network dataset has to be built in Arc Catalog. In the settings, impedance need

to be chosen as the evaluation criteria used in ArcMap. The most commonly used impedance is

length and time. People can also generate their own cost attributes as the impedance. Djokic et al

[5] divides the impedance into different types based on their applications in 1993. Both time and

length can be defined as impedance or cost. In their work [5], the optimal route is the route with

minimum length, which has the same result with Dijkstras algorithm. A transportation routing

problem is studied by Jourquin et al [6] in 1996. The objective is to minimize the total cost of

various transportation modes. The cost is assumed to be proportional to the quantity. Two set of

cost functions are used in [6]. The first set is load and unload cost generated when the freight is

moved from one mode to another. The other cost is the transportation cost of each mode.

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Boil e [7] summarizes the formulations in multimodal transport and describes the advantages in using GIS to study multimodal transport problem in 2000. For most of the models,

it requires a lot of time to input the data of the network. In addition to that, those models are not

flexible enough if they are used to solve a different network. GIS data can be collected from

various sources and can be directly used for network analysis. This provides a good reason for

the growing use of GIS in transportation routing problems. Standifer [8] divides the data needed

into two kinds, which are geographical data and attribute data separately. The geographic data

can be obtained from sources such as NTAD, BTS and so on. The attribute data is comparatively

difficult to get because the department of transportation is not willing the share those information

with public. In the attribute data for rail or roadways, speed limit is one of the most important

variables. In [7], the roadway data is obtained from the Texas Reference Marker System, which

is developed by Texas Department of Transportation. Two formulas are tested to estimate the

speed. Based on those formulas, the speed is estimated as the speed limit multiplied by an

adjustment factor. The factor is based on the functional class of the road. It is not difficult to

download the real network data for both railways and roadways. The main problem is that the

railways are operated by many companies. They dont really share all their tracks. Another big

problem is that the terminal information is usually not open to public. The additional problem

would be the connectivity between different transportation modes and the transfer cost.

According to those considerations, only truck transportation is considered in this project.

Comber et al [9] used network analyst to study the closure of UK post offices. The

objective they want to achieve it to minimize the increased distance due to the closure of post

offices. Accessibility to post offices is analyzed in this article. It provides a good tool for policy

making.

3. METHODOLOGY

Network analyst is the main tool used in this project. The data used is National Highway

Planning Network of 1998 It is downloaded from Bureau of Transportation Statistics North

American Transportation Atlas Data (NORTAD) [10]. The transportation cost rate is 0.30 dollars

per minute per ton for all the materials.

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Data: National Highway Planning Network of 1998

Study area: An area completely within Texas.

Coordinate System of the data frame: GCS_North_American_1983

Step 1: As the study area is completely within Texas, there is no need to use the highway

network of the national system. Therefore only the data of Texas is needed. In order to have the

data of Texas, intersect is used. The state data that we used in class is adopted here to intersect

with the highway network data. Before intersect, the coordinate system of the state and the

national highway system are adjusted as the same. Although the data we used in class is older

than the data of highway network, there exist a far away distance from the border of Texas. The

little difference will not influence the result. After step 1, the highway network of Texas is

generated.

Step 2: Select by the attribute of Fclass (Function Class). Export them one by one. For each of

those new shape files, add two fields named speed limit and cost respectively. The main attribute

we want to get is cost, which is based on the transportation rate and travel time. The only

attribute available is length of the road.

Travel time is very hard to estimate. In this project, the method in [5] is used to estimate the

travel time. In the data we obtained from BTS, the roadway is divided by their function class. All

kinds of roads are included such as state highway, urban local and so on. Based on their function

class, the speed limit is assigned to them.

The real speed limit data is not open to public. Those speed limits might not be exactly the same

as the real data. They might be smaller than the real speed limit. However, when we take into

account of some delays on the roads, it is acceptable to use a smaller data to estimate the travel

time. The correction factor is exactly the same as [5]. Then the estimated travel time is as

following:

Travel time = Length of RoadSpeed Limit Correction Factor * 60 (minute) (1)

Cost= Travel time * 0.30 (dollars per minute per ton) (2)

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Table 1: Speed limit data and correction factor data used in this project

Function Class Road Type Speed Limit Correction Factor

00 Interstate 80 1.00 01 Rural Principal Arterial 75 1.00 02 Rural Principal Arterial - Other 70 1.00 06 Rural Minor Arterial 60 0.90 07 Rural Major Collector 45 0.90 08 Rural Minor Collector 35 0.80 11 Urban Principal Arterial - Interstate 60 1.00 12 Urban Principal Arterial-Other

Freeways & Expressways 50 1.00

14 Urban Principal Arterial - Other 45 0.75 16 Urban Minor Arterial 40 0.60 17 Urban Collector 35 0.60

Step 3: Merge all the roadway files by function class. After this step, we get a Texas road network with cost attribute.

Figure 2: Attributes table of the highway network after adding cost and speed limit

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Figure 3: Map of the highway network with cost attribute data

Step 4: Use ArcCatalog to build a network dataset and add to ArcMap. The impedance is chosen

as cost we added in step 3.

Step 5: Create routes connecting the distribution center with supply and demand cities. The

locations are found by the ZIP code. There is a function in finding address when creating route.

There are three options for this distribution center. For each of them, there are nine routes. Those

nine routes are from distribution center to five supply cities and from distribution center to four

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demand cities. Then those nine routes are merged as a new file. Three files are obtained

corresponding to those three optional distribution centers.

Table 2: The nine routes created for each option city of distribution center (origin to destination)

Route Origin Destination 1 Bellville Distribution Center 2 Lufkin Distribution Center 3 Marlin Distribution Center 4 Smithville Distribution Center 5 Taylor Distribution Center 6 Distribution Center Austin 7 Distribution Center Dallas 8 Distribution Center Houston 9 Distribution Center San Antonio

Step 6: Input the demand information for the three files created in step 6. This can be done by adding a new field and edit it. The demand amount is for a month.

Table 3: Supply amount for raw materials City Supply(ton) Bellville 500 Lufkin 500 Marlin 400 Smithville 300 Taylor 300

Table 4: Demand amount for products City Demand(ton) Austin 400 Dallas 600 Houston 400 San Antonio 600

Step 7: Calculating the total cost for those three distribution centers. Based on step 7, add a field

called tonnagecost, which is used to calculate the cost multiplied by the flow. Then use statistics

to get three total transportation costs of the distribution centers.

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Step 8: Compare them and find out the best location.

Table 5: Total cost of the three optional distribution centers

Distribution Center Location Total Cost(dollars) Conroe 151,000 College Station 135,000 Waco 147,000

From this table, we can see that the total cost is lowest for College Station. Compared with Waco and Conroe, College Station has 7% and 10% less cost respectively.

Figure 4: Route map of all three optional locations

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4. APPLICATION AND RESULT DISCUSSION

4.1 Result Discussion

Based on the methodology used above, there are still some problems that need to be discussed.

Due to the complexity of the location selection problem, there are still a lot of things to do in this

field. This project just solve a simplified problems based on a series of assumptions. It is

necessary to discuss the result to find out improvement.

4.1.1 Sensitivity Analysis

The method used in this project highly relies on the forecast of demand. For the supply, the

company can adjust the amount for each supplier. However, the demand is may change with

time.

Case I: If we keep increasing the demand of Houston from 400 tons per month to 1400 tons per

month, the total cost for those three cities are shown in the following table. In this case, Conroe

will be a better location is we just consider the transportation cost.

Table 6: Total cost of the three cities after changing the demand of Houston Distribution Center Location Total Cost(dollars) Conroe 162,000 College Station 163,000 Waco 197,000

Case II: If we keep increasing the demand of Dallas from 600 tons per month to 1200 tons per

month, the total cost for those three cities are shown in the following table. In this case, Waco

will be the best location given the amount after change.

Table 7: Total cost of the three cities after changing the demand of Dallas Distribution Center Location Total Cost(dollars) Conroe 183,000 College Station 166,000 Waco 163,000

4.1.2 Multimodal Transport

If we consider multimodal transport, which means both truck and railcar can be used to transport

the freight, the result might change. The railway distance of the United States is highest in the

World. Nearly all the railway systems in Texas are for freight transportation. Railway network

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data is also available in BTS. However, terminal information is not available in the internet.

When using multimodal transport, transfer happens within a terminal. There is a transfer fee for

each loading and unloading process. The problem will become more complicated.

4.1.3 Service Area Analysis

If College Station is chosen as the location of the distribution center, the service area can be

analyzed using a function of network analyst. In this analysis, the service area is decided by the

cost. There are three polygons generated. The first area is cost less than 18 dollars per ton. The

second area is cost between 18 dollars per ton and 27 dollars per ton. The third area is cost

between 27 dollars and 36dollars per ton. If we consider the 0.30 transportation rate, those three

costs correspond to the travel time of 60, 90 and 120 minutes.

Figure 5: Service area of College Station based on cost (dollars / ton)

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4.1.4 Closest Facility Analysis

In case of emergency need, the closest facility function can be used to find out which city is the best place to supply products. Take Houston as an example, if the store in Houston is short of product and needs product urgently, we need to find out whether to supply from the distribution center or from another store in other cities.

4.2 Application

The method used in this project is to make use the shortest cost route and amount information to

find out the lowest total transportation cost location, which is defined as the best location for the

distribution center. In other areas, it can also be used. To find out the best location of a school or

hospital, an area is usually divided into small study zones with total population data. For a

primary school, the main considered age group is kids between 5 and 12. Then those population

data can be seen as demand amount data. The cost data can be travel time. Given the roadway

network of the study area, the method in this project can be used to evaluate different locations.

If the best location of a hospital needs to be found, the population data can still be used for

analysis. For different age groups, the probability of going to hospital differs. The amount can be

analyzed with a probability model. Then the total cost for different locations can be found.

In addition to those applications, network can also study problems when the facility of a location

is already fixed. For example, there is a house in fire and the closest fire station need to be

identified with minimum travel time. This can easily be done with GIS network analyst.

However, in this case, the estimation of travel time needs to be very accurate. The temporal

change and spatial change of travel time need to be taken into account.

5 CONCLUSIONS

In this project, GIS Network Analyst is used to analyze a distribution center location problem.

The main data used is downloaded from Bureau of Transportation Statistics North American

Transportation Atlas Data (NORTAD). In order to analyze this problem, a cost attribute is

created as the impedance used in Network Analyst. The unit of this cost is dollars per ton, which

is generated from the travel time and transportation cost rate. Speed limit data is added according

to the function class of the roads. The travel time is obtained using the length and speed limit

data.

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After those preparations, a network dataset is created and used for analysis. For each of

the three options of distribution center, nine routes that connect the nine cities and the

distribution center are built. By merging those nine routes together and add the amount data, the

total cost are obtained with statistics function. The comparison shows that College Station has

the lowest total cost.

To better assess the result, a sensitivity analysis is done. It indicates that if the demand of

Houston increases from 400 tons per month to 1300 tons per month, then Conroe would be the

best place for this distribution center. If the demand of Dallas increases from 600 tons per month

to 1200 tons per month, then Waco is the best location for this distribution center. The service

area of College Station is also analyzed and shown in this project. This will be helpful if more

stores will be opened in other areas of Texas.

This method is a demand based and cost based method. As a result, the demand forecast

is very important. The other assumptions include the cost structure are similar in those three

locations. Also transportation cost is the most important cost.

For future research, multimodal transportation can be used to assess the cost. The

transportation will be finished by trucks and railcars. Other costs will be introduced such as

transfer cost, facility using costs.

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6. REFERRENCES

[1] ESRI 2010. http://www.esri.com/software/arcgis/extensions/networkanalyst/index.html.

[2] Andreas Klose , Andreas Drexl (2003), Facility location models for distribution system

design, European Journal of Operational Research.

[3] Burcin Bozkaya , Seda Yanik, Selim Balcisoy (2010), A GIS-Based Optimization

Framework for Competitive Multi-Facility Location-Routing Problem, Netw Spat Econ

(2010) 10:297320 DOI 10.1007/s11067-009-9127-6.

[4] ESRI 2006. ArcGIS 9, ArcGIS Network Analyst Tutorial.

[5] Dean Djokic, David Maidment(1991), APPLICATION OF GIS NETWORK ROUTINES

FOR WATER FLOW AND TRANSPORT, Journal of Water Resources Planning and

Management, Vol. 119, No. 2, March/April, 1991. 9 ISSN 0733-9496/93/0002-0229.

[6] B. JOURQUIN and M. BEUTHE, TRANSPORTATION POLICY ANALYSIS WITH A

GEOGRAPHIC INFORMATION SYSTEM: THE VIRTUAL NETWORK OF FREIGHT

TRANSPORTATION IN EUROPE, Transpn Res.-C, Vol. 4, No. 6, pp. 359-371, 1996

[7] Maria P.Boile (2000), INTEWODAL TRANSPORTATION NETWORK ANALYSIS - A GIS

Application, loh Mediterranean Electrotechnical Conference, MEleCon 2000, Vol. I.

[8] Glenn Standifer and C. Michael Walton(2000), Development of a GIS Model for

Intermodal Freight, Combined final report for the following two SWUTC projects:

GIS-Based Intermodal Freight Analysis 167509.

[9] Alexis Comber, Chris Brunsdon, Jefferson Hardy and Rob Radburn( 2009), Using a GIS

Based Network Analysis and Optimisation Routines to Evaluate Service Provision: A Case

Study of the UK Post Office, Appl. Spatial Analysis (2009) 2:4764 DOI 10.1007/s12061-

008-9018-0.

[10] http://www.bts.gov/publications/north_american_transportation_atlas_data/.

giGIS ProjectABSTRACT1. INTRODUCTION1.1 Background1.2 Problem Description

2. LITERATURE REVIEW3. METHODOLOGY4. APPLICATION AND RESULT DISCUSSION4.1 Result Discussion4.1.1 Sensitivity Analysis4.1.2 Multimodal Transport4.1.3 Service Area Analysis4.1.4 Closest Facility Analysis

4.2 Application

5 CONCLUSIONS6. REFERRENCES