THESIS
AN ANALYSIS OF TRUCK TERMINAL AND FREIGHT FACILITIES IN THAILAND
PALITA MEENAPINAN
GRADUATE SCHOOL, KASETSART UNIVERSITY Academic Year 2019
THESIS APPROVAL GRADUATE SCHOOL, KASETSART UNIVERSITY
Master of Engineering (Sustainable Energy and Resources DEGREE: Engineering) MAJOR FIELD: Sustainable Energy and Resources Engineering FACULTY: Engineering
TITLE: An Analysis of Truck Terminal and Freight Facilities in Thailand
NAME: Miss Palita Meenapinan
THIS THESIS HAS BEEN ACCEPTED BY
THESIS ADVISOR (Associate Professor Varameth Vichiensan, Ph.D.)
GRADUATE COMMITTEE (Associate Professor Thongchai Rohitatisha Srinophakun, CHAIRMAN Ph.D.)
APPROVED BY THE GRADUATE SCHOOL ON
DEAN (Associate Professor Somwang Khantayanuwong, Ph.D.)
THESIS
AN ANALYSIS OF TRUCK TERMINAL AND FREIGHT FACILITIES IN THAILAND
PALITA MEENAPINAN
A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Engineering (Sustainable Energy and Resources Engineering) Graduate School, Kasetsart University Academic Year 2019
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Palita Meenapinan : An Analysis of Truck Terminal and Freight Facilities in ABSTRACT Thailand. Master of Engineering (Sustainable Energy and Resources Engineering), Major Field: Sustainable Energy and Resources Engineering, Faculty of Engineering. Thesis Advisor: Associate Professor Varameth Vichiensan, Ph.D. Academic Year 2019
Road transportation has an important role in freight transportation across Thailand. Instead of directly shipping between origin-destination pair, a truck terminal is a facility that provides transshipment and sorting activities’ place in many-to-many distribution systems. Regardless of these benefits, there are currently a limited number of public truck terminals in Thailand to support logistics activities while contrast policies and regulations against promoting shipping business have been published incessantly. This study aims to evaluate truck terminal locations and freight transport efficiency between Bangkok and other parts of the country. Given the road network data and annual supply-demand data, the median model is developed to determine the location of regional truck terminals. The model aims to determine the optimal locations with the objective function for minimizing location cost and transportation cost. As the main findings, the study suggests the most appropriated provinces in each region to locate truck terminals and also compares the results of the number of terminals in each region. Topography, road networks, and demand volumes have a significant impact on the direction of results.
______/ ____ / ____ Student's signature Thesis Advisor's signature
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ACKNOWLEDGEMENTS
ACKNOWLEDGEMENTS
I am also grateful to Dr. Shinya Hanaoka, my thesis advisor, for his hospitality and enthusiastic discussions, and for always take care of me while I stayed in Tokyo for a year.
I would also like to thank Dr. Thongchai Rohitatisha Srinophakun, the head department, for his always support and continuing to confirm that everything would be fine.
I would also like to thank my colleagues at the Hanaoka lab for a pleasant working environment and warm welcome me as one of the lab members. Meilinda and Chatumi, thank you for your advice and ideas and for always taking care of me.
I also wish to acknowledge all the members in the Transport group at Kasetsart University for much advice, and TAIST friends for always cheer me up.
Finally, I would like to express my special thanks to my family for their support and love during a tough time.
Palita Meenapinan
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TABLE OF CONTENTS
Page
ABSTRACT ...... C
ACKNOWLEDGEMENTS ...... D
TABLE OF CONTENTS ...... E
LIST OF TABLES ...... I
LIST OF FIGURES ...... K
Chapter 1 Introduction ...... 1
1. Introduction ...... 1
2. Objective ...... 3
3. Scopes ...... 4
4. Contribution ...... 5
Chapter 2 Literature Review ...... 6
1. Freight transportation in Thailand ...... 6
2. Transportation network ...... 12
2.1 Direct shipment network ...... 12
2.2 Hub & spoke network ...... 12
2.3 Current Thailand network ...... 13
3. Truck terminal development in Thailand ...... 14
3.1 The feasibility study for a regional truck terminal by JICA ...... 14
3.2 The feasibility study for a regional truck terminal in 2008 ...... 15
3.3 The feasibility study for a regional truck terminal in 2015 ...... 16
4. A role of truck terminal in other studies ...... 19
5. A review of methodologies for facility location problem ...... 20
5.1 Multi criteria decision making (MCDM) ...... 20
5.2 Mathematical model ...... 21
5.2.1 Discrete network location models ...... 23
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6. Review of data sources ...... 31
6.1 Automatic data collection system at the Bangkok terminal’s gates ...... 31
6.2 Survey data ...... 36
6.2.1 Road freight vehicle survey ...... 36
6.2.2 Origin-destination survey ...... 37
Chapter 3 Methodology ...... 43
1. Road network data ...... 43
1.1 Origin nodes ...... 44
1.2 Destination nodes ...... 44
1.3 Candidate truck terminal nodes ...... 44
1.4 Road network ...... 45
1.4.1 Phutthamonthon truck terminal ...... 47
1.4.2 Khlong Luang truck terminal ...... 48
1.4.3 Rom Klao truck terminal ...... 49
2. Data manipulation ...... 50
2.1 Buffer area ...... 50
2.2 Data transformation ...... 52
2.3 OD generation ...... 53
2.4 Destination’s demand ...... 54
2.5 Shipped product calculation ...... 55
2.6 Forecast of freight volume ...... 57
3. Related costs ...... 58
3.1 Investment cost ...... 58
3.1.1 Land price ...... 59
3.1.2 Building construction cost ...... 60
3.1.3 Maintenance cost ...... 62
3.1.4 Operation cost ...... 62
3.2 Transportation cost ...... 62
4. Mathematical model ...... 63
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4.1 Mathematic program ...... 63
4.2 Model structure ...... 63
4.2.1 Weekly freight demand model ...... 65
4.2.2 Annual freight demand model ...... 67
Chapter 4 Result and Discussion ...... 70
1. Route analysis ...... 70
2. Weekly freight demand results ...... 71
2.1 North and Central regions ...... 71
2.2 Northeast and East regions ...... 74
2.3 South region ...... 77
3. Annual freight demand results ...... 78
3.1 North and Central regions ...... 78
3.2 Northeast and East regions ...... 81
3.3 South Region ...... 84
3.4 Upper part of the country ...... 85
4. Truck terminal capacity ...... 87
4.1 North and Central regions ...... 87
4.2 Northeast and East regions ...... 88
4.3 South region ...... 89
4.4 Upper part of the country ...... 89
5. Facility functions ...... 92
Chapter 5 Conclusion ...... 94
1. Conclusion ...... 94
2. Recommendation for further study ...... 96
LITERATURE CITED ...... 98
APPENDICES ...... 102
Truck terminal development document ...... 102
Survey document ...... 107
Coordination nodes ...... 108
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Data manipulation ...... 112
Costs ...... 119 CURRICULUM VITAE ...... 121
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LIST OF TABLES
Page
Table 1 Among of freight transport in Thailand...... 7
Table 2 Truck terminals in Bangkok metropolitan area...... 8 Table 3 Advantages and disadvantages of MCDM methods and mathematical models ...... 23
Table 4 Percentage of product types in each destination ...... 55
Table 5 Growth truck freight rate in each province ...... 58
Table 6 Land price of the candidate locations ...... 60
Table 7 Size of a candidate truck terminal ...... 61
Table 8 Construction cost ...... 61
Table 9 Maintenance cost ...... 62
Table 10 Demand volumes in the North and Central regions’ truck terminals ...... 87
Table 11 Demand volumes in the Northeast and East regions’ truck terminals ...... 88
Table 12 Demand volumes in the South region’s truck terminal ...... 89
Table 13 Demand volumes in the upper part’s truck terminals ...... 90
Table 14 Conclusion of selected truck terminal location and size ...... 92 Table 15 The methodology to selected potential provinces in the feasibility and strategy study on the project of the economic zone ...... 103
Table 16 Origin node location ...... 108
Table 17 Destination node location ...... 108
Table 18 Candidate truck terminal location ...... 111 Table 19 Number of vehicles passing though Bangkok truck terminals with showing direction ...... 112
Table 20 Load capacity in each type of vehicles ...... 113
Table 21 Average shipping volume (ton) from the Phutthamonthon terminal ...... 113
Table 22 Average shipping volume (ton) from the Khlong Luang terminal ...... 115
Table 23 Growth freight rate ...... 116
Table 24 Detail of land price in the candidate locations ...... 119
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Table 25 Detail of transportation cost ...... 120
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LIST OF FIGURES
Page
Figure 1 Platform building (1) ...... 9
Figure 2 Platform building (2) ...... 9
Figure 3 Warehouse ...... 10
Figure 4 Terminal gate sensor ...... 10
Figure 5 Small terminal and warehouse building (1) ...... 11
Figure 6 Small platform terminal and warehouse building (2) ...... 11
Figure 7 Direct shipment network ...... 12
Figure 8 Hub and spoke network...... 13
Figure 9 Proposed truck terminal location in 1992 ...... 15
Figure 10 Proposed truck terminal location in 2015 ...... 18
Figure 11 Relationship of UDC and satellite ...... 19
Figure 12 Vehicle classification...... 32
Figure 13 Types of vehicle passing through the Phutthamonthon truck terminal ...... 33
Figure 14 Types of vehicle passing through the Khlong Luang truck terminal ...... 33
Figure 15 Types of vehicle passing through the Rom Klao truck terminal ...... 34 Figure 16 Weekly average number of trucks passing through the Bangkok truck terminals ...... 34 Figure 17 Weekly average number of semitrailers passing through the Bangkok truck terminals ...... 35 Figure 18 Weekly average number of trailers passing through the Bangkok truck terminals ...... 35
Figure 19 Average number of vehicles in the survey data ...... 36
Figure 20 Shipped product in the Phutthamonthon truck terminal (inbound) ...... 38
Figure 21 Shipped product in the Phutthamonthon truck terminal (outbound) ...... 38
Figure 22 Shipped product in the Khlong Luang truck terminal (inbound) ...... 39
Figure 23 Shipped product in the Khlong Luang truck terminal (outbound) ...... 39
Figure 24 Shipped product in the Rom Klao truck terminal (inbound) ...... 40
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Figure 25 Shipped product in the Rom Klao truck terminal (outbound) ...... 40
Figure 26 Destination areas from the Phutthamonthon truck terminal ...... 41
Figure 27 Destination areas from the Khlong Luang truck terminal ...... 42
Figure 28 Destination areas from the Rom Klao truck terminal ...... 42
Figure 29 An overview of methodology ...... 43
Figure 30 Routes from the Bangkok truck terminals to destination nodes ...... 46
Figure 31 Phutthamonthon truck terminal route analysis ...... 47
Figure 32 Khlong Luang truck terminal route analysis ...... 48
Figure 33 Rom Klao truck terminal route analysis ...... 49
Figure 34 Buffer area ...... 51
Figure 35 Methodology to find number of vehicles with showing direction ...... 53
Figure 36 Methodology to find number of vehicles in each destination ...... 54
Figure 37 Methodology of how to calculate products shipped by each vehicle types 54 Figure 38 Outbound shipped products from the Phutthamonthon truck terminal when applying the buffer area ...... 56 Figure 39 Outbound shipped products from the Khlong Luang truck terminal when applying the buffer area ...... 56
Figure 40 Cost category in the investment cost ...... 59
Figure 41 A simplified network of the model ...... 65
Figure 42 Three regions of Thailand ...... 70 Figure 43 Costs of North and Central regions when located p terminals (weekly data pattern) ...... 72 Figure 44 Optimal solution in North and Central regions when located p terminals (weekly data pattern) ...... 73 Figure 45 Costs of Northeast and East regions when located p terminals (weekly data pattern) ...... 75 Figure 46 Optimal solution in Northeast and East regions when located p terminals (weekly data pattern) ...... 76
Figure 47 Costs of South region when located p terminals (weekly data pattern) ..... 77 Figure 48 Optimal solution in South region when located p terminals (weekly data pattern) ...... 78
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Figure 49 Costs of North and Central regions when located p terminals (annual data pattern) ...... 79 Figure 50 Optimal solution in North and Central regions when located p terminals (annual data pattern) ...... 80 Figure 51 Costs in Northeast and East regions when located p terminals (annual data) ...... 82 Figure 52 Optimal solution in Northeast and East regions when located p terminals (annual data pattern) ...... 83
Figure 53 Costs in South region when located p terminals (annual data) ...... 84 Figure 54 Optimal solution in South region when located p terminals (annual data pattern) ...... 84 Figure 55 Costs in the upper part of Thailand when located p terminals (annual data) ...... 86
Figure 56 Comparing optimal results from different methods ...... 86
Figure 57 Comparing optimal results from different scenarios ...... 91
Figure 58 A simplify layout of the truck terminal study in 2008 ...... 102 Figure 59 Survey form ...... 107
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Chapter 1 Introduction
1. Introduction
There has been an issue with transportation around the world. Most studies addressed freight transport problems both industry and regional planning. In industry, many shipping companies are focusing on service level management for more efficient logistics system and increasing customer satisfaction. While a regional scale, national and city governments have faced several problems, including traffic congestion, high energy consumption, negative environmental impacts, such as noise, air pollution, and vibration. These problems have not been concerned with road and car traffic alone. In fact, the dynamic and rapid growth of economic activities has generated the need for freight movement exceeding the capacity of transport facilities. These increasingly result for considering to public road policies, for example, a truck ban policy, regulation of parking, time window for operation, and limited emission have been promoted in urban areas around the world that contrast with transportation and economic development (Crainic et al., 2004; Taniguchi and Thompson, 2002). In Thailand, truck ban policies in big cities have become widely promoted. In Bangkok, more than a 10-wheel truck is limited to access the downtown areas, the most heavily used thoroughfare, from 6:00 – 21:00. Several roads have been restricted for parking. Some U-turns and turning are prohibited for trucks. These regulations have been published to reduce heavy traffic congestion in the city but affected delivery businesses instead. Now, these regulations have been expanding to other big cities such as metropolitan areas, Chiang Mai, Khon Kaen, Phu Khet, Rayong. Some long holidays in Thailand also implement as well (Gazette, 2015, 2018).
While big-sized vehicles have been banned for entering downtown areas, only small trucked vehicles freely enter the restricted zones of many cities. On the contrary, a larger capacity shipping has been promoted for economies of scale leading to lower cost per unit. With high competition markets, low-cost freight shipping
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services are an important pricing strategy. Then, shipping carriers try to assemble near destination products in the same container. However, it is not that easy when there have not a place for logistic activities in the destination and enough waiting times in current services to have economic sizes. During these challenging times, big shipping carriers could have huge financial investments for having their own terminal in destinations. They can travel to their terminals first for unpacking into small bulks and changing vehicle sizes. While small shipping companies are making various efforts to overcome current by modifying or changing accommodation buildings or blank spaces to a transshipment point. Many are against the law like that in living areas or use wrong purposes of buildings (OTP, 2015).
To cope with these problems, among capable solutions to increase delivery efficiency and reduce the negative impacts of freight transport are considered. A hub distribution center (DC), as a transshipment point, is one of a promising choice for goods interchanging in coverage areas (Simoni et al., 2018). The hub DC is where goods are received from entering vehicles, sorted, and consolidated for environmentally friendly delivery vehicles. Various names are used in researches to call but the concept of the consolidation and transshipment point is similar. Several terms refer to the hub DC such as a hub, logistics center, distribution center, consolidation center, freight distribution, freight village, load center, a logistics node, central warehouse, freight/transport/logistics terminal, and truck terminal (Uyanik et al., 2018). Then, this study uses a “truck terminal” that not specific functions except a transshipment point and commonly used in Thailand as a representative name.
Sopha et al. (2016) highlighted that a truck terminal located near urban areas can reduce many aspects of urban logistics problems, such as economic, environmental, and social impacts. According to Jacyna (2013), the effect of goods flow arrangement by the terminal leads to a reduction of traffic congestion in intercity and urban areas, infrastructure destruction, and pollutant emission from vehicle transportation. Some advantages of value-added activities, for example, off-sited stockholding, re-packing, and labeling can also be provided. In addition, to reduce negative environmental impacts, the truck terminal also includes solving social
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problems through the implementation of an advanced information system. This would be helped to reduce the number of vehicles, more efficient routing and scheduling while maintaining the service level. The truck terminal can be cooperated by government and third-party logistics providers by promoting renting public space at a reduced cost (Quak et al., 2008; Taniguchi et al., 1999).
Current facility locations and freight flows have several problems, some related to data used for study and some related to the preferred methods. This study would like to evaluate potential regional truck terminal locations and current freight transport flows from the Bangkok truck terminals. The p-median model is developed to determine the location of regional truck terminals for receiving goods from Bangkok terminals and shipping to other province destinations. In the network, facility nodes assumed as regional truck terminals are transshipment points in order to take advantage of carrying volume and increase utility transport vehicles. The different varieties of commodities and fixed supply-demand pairs are added in the model. An appropriate truck terminal development is recommended to enhance suitable ways of freight facilities.
2. Objective
1. To review truck terminal development in Thailand.
2. To analyze truck terminal optimal location from Bangkok freight flow across the country.
3. To recommend an appropriate of truck terminal development to enhance freight facilities in Thailand.
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3. Scopes
• This study only uses automatic gate control system data by the Department of Land Transport and surveys data by Kasetsart university to calculation the OD freight flow table. The provided demand and OD information is used as an example of freight transportation (population) from Bangkok in the year 2018. Other demands, for example, a private company or other government sections, do not include in this study. • The automatic gate control system data provided from February to August and the OD data provided from the 500 surveys per terminal. The left month data and OD table are simulated randomly regardless of seasonality effect. • Only outbound direction demand is focused, from the Bangkok truck terminals to the representative domestic destination nodes. International freight to neighborhood countries does not include. • Forecast freight data for the next 20 years from a grow truck freight equation is assumed practical. The equation calculated from a GDP and population in each province is adequate. No other factors are considered. • Potential provinces from the previous research are assumed feasible to be a candidate facility location. The candidate facility nodes are assumed from proposed locations and nearby railway station areas. Functions of the candidate facility are only sortation and consolidation, no inventory activity. • The transportation cost considers only vehicle operation cost excluding time cost, environment cost or social cost. The cost is assumed to have a linear relationship between distance and cost. • All costs, the investment cost and the transportation cost, are combined to a present cost, with no inflation and interest rate in the future.
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4. Contribution
1. This thesis will discover presently freight transport movement networks.
2. Potential provinces for truck terminal locations are proposed that will lead to improved future freight transport strategy.
3. Truck terminal development direction will be suggested to enhance freight transport efficiency.
4. Truck utilization will be increased while reducing transportation costs and environmental impacts.
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Chapter 2 Literature Review
1. Freight transportation in Thailand
Freight transport has been a crucial role in aspects of the economy and the day to day society, generating a critical part in social and environmental impacts. Increasing demand for goods and services have resulted from population and economic growth by commercial and domestic users. Table 1 shows the significance of road transportation in Thailand. More than 80% of freight transport in Thailand each year rely on road transportation. The majority of products are agricultural products, consumer goods, and construction goods. The freight volume is expected to be increased by about 38% by 2037 (OTP, 2015). Thereby, infrastructures have been needed to invest to link the transport networks and also consolidation and distribution points in potential areas (Department of Land Transport, 2015).
Currently, there are three public truck terminals for a transshipment point in the Bangkok metropolitan region located in the North, West, and South parts following in Table 2. These truck terminals were constructed in 2000 to organize and develop freight delivery systems and also relieved problems from truck transportation, for example, reduction of empty backhaul runs, before publishing the ban truck policy in the Bangkok area (Office of Transport and Traffic Policy and Planning (OTP), 2011). The functions of provided renting spaces are the same in all truck terminals, only the Phutthamonthon truck terminal that has another building, a small platform building with warehouse.
These terminals currently have been used to sort and consolidate products from industrial zones in the lower central region and various nearby areas, and then distribute to other provinces across the country. Each truck terminal supports a budget cost in renting spaces to run shipping businesses. Full truckload shipment is tried to apply to reduce the transportation cost per unit. After consolidation, some vehicles
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directly go to customer’s destinations. While some big shipping carriers that have their own terminal in destinations go to their terminals first for unpacking into small bulks. Some small carriers modify or change accommodation buildings or blank spaces as a transshipment point that near local villages that are against the law.
Table 1 Among of freight transport in Thailand.
Freight transport Among of freight Vehicle in country Year transport Percentage Percentage modes (Million ton- (Million ton/year) km) 2016 Road 706.543 86.94 229,600 92.20 Rail 11.632 1.43 2,670 1.07 Waterway 94.391 11.61 16,671 6.69 Air 0.134 0.02 79 0.03 Sum 812.699 100 249,021 100 2015 Road 709.69 87.06 230,600 92.28 Rail 11.562 1.42 2,654 1.06 Waterway 93.805 11.50 16,566 6.63 Air 0.132 0.02 78 0.03 Sum 815.189 100 249,898 100 2014 Road 724.625 87.14 235,200 92.31 Rail 11.667 1.40 2,666 1.05 Waterway 95.092 11.44 16,856 6.61 Air 0.132 0.02 78 0.03 Sum 831.516 100 254,800 100 2013 Road 704.013 87.50 228,315 92.58 Rail 11.253 1.40 2,490 1.01 Waterway 89.125 11.08 15,723 6.38 Air 0.130 0.02 77 0.03 Sum 804.521 100 246,605 100 Source: Office of Transport and Traffic Policy and Planning (2017).
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Ten Five Rom Klao Latkrabang,Bangkok motorway and next to ICD ICD tomotorway next and he east side of nearby he east Bangkok, side T
of Bangkok,
Four Eight 94 parking slots Phutthamonthon he west side T road, province. Nakhon Phanom nearby Borommaratchachonnani nearby Borommaratchachonnani
.
Ten Five Khlong Luang Pathumtani provincePathumtani he north side of Bangkok, he north side (outer Bangkok ring(outer road), Bangkok T nearby east Kanchanaphisek road nearby Kanchanaphisek east
Truck terminals in Bangkok metropolitan area metropolitan in Bangkok Truck terminals Customer Customer Service System, Truck Terminal Station, Department of Land Transport, Retrieve from https://ttms.dlt.go.th/ttms
Truck Terminal 2 building No. of platform terminals No. of warehouses platformNo. of small and warehouse terminal Location Source: eservice/public Table Table 9
Figure 1 Platform building (1)
Figure 2 Platform building (2)
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Figure 3 Warehouse
Figure 4 Terminal gate sensor
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Figure 5 Small terminal and warehouse building (1)
Figure 6 Small platform terminal and warehouse building (2)
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2. Transportation network
There are several channels of physical distribution network. One of the fundamental things in goods distribution is to choose between directly ship products to the customer or whether shipping pass thought the intermediaries.
2.1 Direct shipment network
The most immediate way to connect the customer is through a direct shipment network. The manufacturer or supplier deliver their products directly from their plant by their own vehicle. When the number of production nodes or destination nodes are increased, the number of links also significantly rise (Chopra and Meindl, 2016).
Figure 7 Direct shipment network Source: Chopra and Meindl (2016)
2.2 Hub & spoke network
In the hub and spoke network, all goods are collected in the central warehouse, called the hub. At there, goods going to the same destination are consolidated and arranged into the same vehicle. Then, prepared goods are distributed to each terminal, called nodes. If the vehicle visits only one node, this is called the hub and spoke network (Liu et al., 2003).
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Figure 8 Hub and spoke network.
2.3 Current Thailand network
In the current situation, road and railway transportation are plans and strategies relating to long-term freight transportation. However, excluding road transportation, other mode shares have accounted for less than 20% in freight transportation. This is because other modes of transport are limited and low quality of services. Transport by rail is unreliable. This is because the development to sync other modes of transport has been limited. Facilities at the connection points between the transport modes are still inadequate in both quality and quantity. As a result, multi- modal transport in Thailand has not been successful. While road transport is the most convenient to move people and freights from points to points. Road network can reach to everywhere which door-to-door service is provided easily. However, facilities in freight transportation still have not been enough to support the shipping business. Changing vehicles is limited due to lack of places and facilities while many deprived policies have been published to restrict freight transportation. One of fundamental infrastructure is a place to change size of vehicles and sort commodities for shipping efficiency. Now, there is only provided in the metropolitan area whereas other cities have grown in economic and transportation. In a big freight forwarding company, to construct or rent the transshipment place could be a good strategy to boost their business. They can run business in any kinds of transportation, both a directly shipment or a hub-and-spoke. However, to consider in a small shipper, they have limited choices and alternatives in this competition filed. A full truck load
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is the most desirable choice to reach economic of scale. The direct shipment is preferable that their vehicle also limited.
3. Truck terminal development in Thailand
3.1 The feasibility study for a regional truck terminal by JICA
At that time, the Bangkok metropolitan would like to public a truck ban policy in the downtown area. Then, to prepare for the policy, there would have a truck terminal to support the transfer between vehicles. This project then was conducted by the cooperation between the Department of Land Transport and the Japan International Cooperation Agency (JICA) in 1992. The results suggested to have two types of truck terminals; Bangkok truck terminals and regional truck terminals in six potential provinces; Chiang Mai, Nakhon Sawan, Khon Kaen, Nakhon Ratchasima, Songkhla and Hat Yai. This result stimulated a truck terminal pattern that was popular in Japan at that time. The Bangkok truck terminals had a role to consolidate goods and delivery by a line-haul truck to other regional truck terminals. Then, an urban vehicle was maintained the left delivery until reaching the destination. Simultaneously, the Bangkok truck terminals are used to transfer vehicles from a line-haul truck to a smaller truck before entering the Bangkok downtown area. Moreover, JICA also recommended having a line-haul truck bus between the Bangkok terminal and others (JICA, 1992).
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Figure 9 Proposed truck terminal location in 1992 Source: Department of Land Transport (2015)
3.2 The feasibility study for a regional truck terminal in 2008
This project was conducted by the Department of Land Transport in 2008. The project studied and selected the candidate potential provinces where to locate a truck terminal in terms of economy, investment, and management by gathering data. They also pointed out problems of the three Bangkok truck terminals. The problems were low efficiency and capacity of the terminals, regulatory issues, and also
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environmental issues such as noise and traffic. However, to solve the problems was complicated that many roots of the problems came from a lack of professional management.
The result also firstly suggested 12 potential provinces and then seven provinces in the last for both boundary and regional truck terminals. The seven provinces are Chiang Mai, Songkhla, Ubon Ratchathani, Sa Klao, Nhong Khai, Tak, and Mukdaharn. A simplify layout proposal was also studied following Figure 58 in the appendix A.
3.3 The feasibility study for a regional truck terminal in 2015
The project was focused on the feasibility of special economic zones in Thailand. The special economic zones have summarized as an industry, a custom-free zone, a bonded warehouse, logistics infrastructures, and a truck terminal. Not only provinces having international boundary gates, but the main provinces in each region also had significant development in the economy through real estates, households, and department stores. These reflect to dramatically increase in logistics and transportation activities. Truck terminals and distribution centers were invested by carriers to boost logistics activities. However, it was out of control. Then, the government would like to develop in infrastructures to support a small-medium carrier, 97% shipper of the whole country, with transportation management instantaneously.
In the feasibility project, the methodology to selected potential provinces came a ranking by six factors: 1) Population – This factor describes a consumer and production rate. 2) Gross primary production (GPP) - This factor describes an economic level, consumer and production rate, and population income. 3) Factory - This factor describes a production and transportation rate. 4) Buffer zone – This factor was conducted to specific a buffer zone, 100 kilometers, from Bangkok and Chon Buri province.
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5) Freight flow - This factor uses the national model (NAM) in 2014 to estimate the freight flow in each province. 6) Government policies – There are three policies weighted the score; (1) Railway network accessibility (2) Special economic zone (SEZ) in the boundary provinces (3) The number 1 city policy
The population, GPP, and factory factors are divided into four ranges following the 25th, 50th, and 75th percentile. The results concluded that ten boundary provinces that have the special economic zone and seven potential provinces are selected to locate a truck terminal. The results showed in an appendix A, Table 15 and the all selected provinces are shown in Figure 10.
Currently, the feasibility and strategy study on the project of the economic zone is a plan for the logistics development. However, the method to conduct the result was not clear. The method is quite similar to Multi criteria decision making (MCDM). This technique mainly uses a hierarchy of criteria. There are gathering information and consider. Then, scores are assigned to each option by a decision- maker. Finally, determining a global score for each option is a weighted sum of all criteria. But the feasibility and strategy study on the project of the economic zone has a simple method to assign scores. And this project did not describe a criterion choosing the quantity of a truck terminal.
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Airport Chiang Khong Study province Railway network Chiang Rai Asia highway Boundary province Chian Potential province g Mai Special economic zone phase 1
Special economic zone phase 2 Nong Khai
Nakhon Phanom Phitsanulok Khon Kaen Tak Mukdahan Nakhon Sawan Ubon Ratchathani
Nakhon Ratchasima Prachin Buri
Kanchanaburi Sa Kaeo
Trat
Surat Thani
Songkhla
Narathiwat
Figure 10 Proposed truck terminal location in 2015 Source: Department of Land Transport (2015)
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4. A role of truck terminal in other studies
In many urban freight transportation studies, there is an urban distribution center (UDC) that receives goods traveling from other regions by trucks to distribute in urban areas. And there is a satellite located around coverage urban areas to receive goods transported from the UDC. At the satellite, goods are transferred from trucks to smaller and more friendly environmental vehicles to reduce congestion and emission in an urban area. Sopha et al. (2016) and de Assis Correia et al. (2012) highlighted that the UDC can reduce many aspects of urban logistics problems, such as economical, environmental, and social parameters. According to Jacyna (2013), the effect of goods flow arrangement by the satellite leads to a reduction of traffic congestion in intercity and urban areas, infrastructure destruction, and pollutant emission from vehicle transportation.
Figure 11 Relationship of UDC and satellite Adapted from Jacyna (2013)
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5. A review of methodologies for facility location problem
The selection of location for facilities has been traditional problems, therefore, become crucial. There are more than a hundred papers related to the location problem. Location problems consist of demand nodes and a set of potential sites where facilities can be located. The selection of appropriate locations should be studied carefully since improper locations can lead to inefficient logistics systems, congestions, and cause of more environmental impacts (Sopha et al., 2016). According to (Uyanik et al., 2018), they roughly classified methodologies into two groups for the location problem; multi criteria decision making (MCDM) and mathematical model.
5.1 Multi criteria decision making (MCDM)
In MCDM, there are involved techniques such as Analytic Hierarchy Process (AHP), Fuzzy AHP, Elimination and Choice Expressing Reality (ELECTRE), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), etc. One of the most popular techniques is AHP. It is considered the weight of the individual criteria. The AHP is used to obtain a set of evaluation weighted criteria, and a set of alternative options which assigns a score to each option according to the decision maker’s pairwise comparisons of the options based on that criterion. Finally, determining a global score for each option is a weighted sum of all criteria This method accounts for a professional interview then, the subjective opinion may acquire. To ensure the result reliability, calculating the consistency ratio (CR) is conducted. CR means consistent and acceptable when CR value ≤ 0.1.
Regmi and Hanaoka (2013) conducted the APH model combined with the goal programming (GP) to indicate the location of logistics centers in Laos. The five criteria used in the AHP were set as goals that would minimize transportation and operation cost, transportation time, and environmental impacts and maximize the intermodal transport connectivity and regional economic development. Sopha et al. (2016) implement the AHP model in spatial analysis. The six factors are considered to
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locate potential areas of UDC. The results from the AHP were examined through the development of multi-objective mixed-integer linear programming (MOMILP) by a trade-off between total costs and carbon emission.
5.2 Mathematical model
Problems in optimization are the most common applications of mathematics. In the mathematical model, typology of location problems can be considered on the analytic, continuous, network or discrete problems. When demands are assumed to be distributed uniformly over the region with a demand density ρ demands per unit area, the problem is an analytic location problem. Continuous location problems assume demands as discrete sites however facilities can be located at any place in a region. Network location problems locate all demands and facilities on a network composed of nodes and links. Discrete location problems give specific places or coordinates of demand nodes and candidate facilities. Even though the problem is continuous by nature, most of the results in the literature are discretized. In particular, many studies focused on discrete location problems. Most models can be formulated as integer linear programming problems. Linear programing is necessary to formulate and review many facility location problems. Then this leads to a notion of formulations and solutions of facility location problems. Though the location problem contexts, there are primary classified into four categories using objective function criteria: (1) covering problem, (2) center problem, (3) median problem, (4) fixed charge facility location problem. These problems solve the location of facilities and allocate demand from one or multiple facilities to destination points (Daskin, 2011; Farahani and Hekmatfar, 2009).
Another method in mathematical models, for example, is published by (Taniguchi et al., 1999). They proposed the genetic algorithm to construct public logistics terminals near expressways interchanging freight in large cities, Osaka and Kyoto, in Japan. The goods movement was assumed that traveling from long- distances by line-haul to the logistics terminal and then transferring to local
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pickup/delivery for urban roads. Occasionally goods may be stored at logistics terminals, but no inventory in this study. The model has two levels of problems. The upper-level problem defines the behavior of the planner for minimizing the total cost (transportation cost and facility cost) and determining the optimal size and location of logistics terminals. The lower-level problem defines the behavior of each company and each truck in choosing optimal terminals and transportation routes.
Some paper uses another transport system program to perform the result. de Assis Correia et al. (2012) used a GIS program to solve the location and routing problems in Belo Horizonte city (Brazil). The program can reduce the complexity of vehicle routes to the city. To this end, an integrated freight traffic assignment methodology is presented to consider traffic flow network. Uddin and Huynh (2015) proposed a freight traffic assignment in a large-scale road-rail intermodal network. The lower level in minimizing delivery time is considered. They also developed the user-equilibrium model for the rail-road freight flow problem.
Numerous MCDM methods and mathematical models have seen an incredible amount of use over the last several decades. Its role in different application areas has increased significantly, especially as new methods develop and as old methods improve (Farahani et al., 2010). The observed advantages and advantages are summarized in Table 3.
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Table 3 Advantages and disadvantages of MCDM methods and mathematical models
Methodologies Multi criteria decision making Mathematical model Advantages • Easy to use and scalable • Quick and easy to produce • Hierarchy structure can easily • Can accurately represent reality adjust to fit • A complex situation can be • Many sized problems simplified • Not data intensive • Certain variables can readily be • Takes uncertainty into account changed • Predictions enable to be made • Insight and information can be acquired Disadvantages • Needs a lot of input • The model is a simplification of • Selecting methods based on the real problem and does not decision-maker preference include all aspects of the • Problems due to the problem interdependence between criteria • The accuracy of input data may and alternatives be unknown
• Can lead to inconsistencies • Results greatly depend on the between judgment and ranking accuracy of input data criteria • The model may only work in certain situations Source: Daskin (2011); Farahani and Hekmatfar (2009); Sabaei et al. (2015); Velasquez and Hester (2013)
5.2.1 Discrete network location models
The discrete location has been studying location decision-making for a long and voluminous history of location researches. The discrete location problem has been a well-established research area within Operations Research (OR). Due to its rich literature and importance on this topic, it is not surprising that reviews have been published regularly. Particularly, we focused on discrete models because they have
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received most of the attention in the literature. A unique of the discrete location model is that the selection of the sites where new facilities are located is constrained to a finite set of available candidate locations (Current et al., 2002). There are many different models involving location problems. In this section, four basic facility location models are presented: covering problems, center problems, median problems, and fixed charge facility location problems.
5.2.1.1 Covering problems
In location problems, service to customers depends on the distance between a customer and a facility that is assigned to the customer. The pair between a customer and a facility is linked if the customer is in the given distance of the facility, which means the customer can receive a service if the customer is under a certain distance. While some customers are not linked if the distance exceeds some critical value. This given distance is normally called a coverage distance or coverage radius (Fallah et al., 2009). Then, the concept of the location covering model is to find a minimum number of facilities needed but cover all demand nodes. In addition, these problems are divided into two problems based on total covering demand nodes and partial covering demand nodes. The covering model usually uses in location problems of analysis of markets, archaeology, crew scheduling, emergency services, metallurgy, nature reserve selection (García and Marín, 2015).
In this section, a general covering model is presented as particular cases of the main covering location problems. The set covering problem is to find a minimum cost of located facilities from among candidate facilities so that every demand node is covered by at least one facility. The model can be formulated using the following notations (Daskin, 2011);
Inputs 1 if candidate site j ∈J can cover demands at node i ∈I 푎 = { 푖푗 0 if not
푓푗 = cost of locating a facility at candidate site j ∈J
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Decision variables 1 if we locate a facility at candidate site j ∈J 푋 = { 푗 0 if not
With this notation, we can formulate the set covering problem as follow:
Minimize ∑j ∈J 푓푗 푋푗 (2.1)
∑j ∈J 푎푖푗 푋푗 ≥ 1 Ɐ i ∈ I (2.2)
푋푗 ∈ {0,1} Ɐ j ∈ J (2.3)
The objective function (2.1) is to minimize the total cost of located facilities in the candidate site. Constrain (2.2) stipulate each demand node i ∈ I is covered by at least one located facility. Constrain (2.3) are the integrality constrains.
5.2.1.2 Center problems
In the set covering model, the coverage distance between a demand node and a located facility is exogenously specified distance. However, in the set centering model, a distance is determined endogenously by one of the candidate facilities. A different strategy of both models is discussed. The concept of the centering model is to minimize the coverage distance while still tries to cover all demand nodes by a given facility. Then, the number of demand nodes are maximum covered. The model is also introduced as the p-center model or a minimax problem as well. The objective of the set centering model is to find the location of p facilities that cover all demand nodes and the maximum coverage distance between a demand node and the nearest facility is minimized (Daskin, 2011).
In the p-center model, each demand node has a different weight. The weight may be time, cost or loss. So, the problem would find a center to minimize a maximum of these time, cost or loss. The p-center model would use in these applications: hospital emergency services, fire stations, police stations, computer
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network services, distribution industries, location-allocation for post boxes and bus stops, etc. (Biazaran and SeyediNezhad, 2009).
The basic model can be formulated using the following notations;
Inputs
푑푖푗 = distance between demand node i ∈I and candidate facility site j ∈J 푝 = number of facilities to locate
Decision variables 1 if we locate a facility at candidate site j ∈J 푋 = { 푗 0 if not 1 if demand node i ∈I is assigned to a facility at candidate site j ∈J 푌 = { 푖푗 0 if not 푧 = maximum distance between a demand node and the nearest facility
With this notation, we can formulate the set covering problem as follow:
Minimize 푧 (2.4)
∑j ∈J 푌푖푗 = 1 Ɐ i ∈ I (2.5)
∑j ∈J 푋푗 = 푝 (2.6)
푌푖푗 ≤ 푋푗 Ɐ i ∈ I ; j ∈ J (2.7)
푧 ≥ ∑푗 푑푖푗푌푖푗 Ɐ i ∈ I (2.8)
푋푗 ∈ {0,1} Ɐ j ∈ J (2.9)
푌푖푗 ∈ {0,1} Ɐ i ∈ I ; j ∈ J (2.10)
The objective function (2.4) is to minimize the maximum distance between set of demand nodes and its nearest facilities in the candidate site. Constrain (2.5) stipulates each demand node i ∈I is assigned by at least one located facility. Constrain (2.6) shows that p facilities are located. Constrain (2.7) ensures that assignments can only be made to located facilities. Constrain (2.8) is conjunction with the objective function. Constrain (2.9) – (2.10) are the integrality constrains.
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5.2.1.3 Median problems
In the covering model and the centering model, a demand node receives benefits from a located facility if the node is in the coverage distance, and no benefits if it is not in the coverage distance. However, the benefits, in many cases, relate to a distance between a demand node and the nearest facility. In the median problem, the concept is to increase the benefit from a set of new facilities such that the cost from facilities to demand nodes is minimized and an optimal number of located facilities in an area of interest is satisfied the customer demand. The facilities are proposed into the median points so that the sum of costs can be minimized through the objective function. The target of the objective function is minisum, mainly a distance that reflects transportation cost. The relationship between the distance and cost associated with the facility-demand pairs is various but usually linear. For example, costs serving between located facilities and demand nodes may depend on distances between facility-demand pairs. The median model is used in many kinds of problems including the establishment of public services, schools, hospitals, firefighting, ambulance, warehouses, and distribution centers, etc. (Daskin, 2011; Jamshidi, 2009).
More research has been carried out in the field of median location problems after Hakimi (1964) published covering and median problems’ study for locating one or more facilities on a network. Given an integer p in this problem, the objective was to minimize the total cost of transportation between clients and the p opened facilities. The development and trend of location problems can follow from several journal articles such as Reese (2006) and Tansel et al. (1983).
The p-median problem is to find specified p facilities from the candidate set to locate in the network. This problem can simply formulate using the following notations;
Inputs
ℎ푖 = demand at node i ∈I
푑푖푗 = distance between demand node i ∈I and candidate facility site j ∈J
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푝 = number of facilities to locate
Decision variables 1 if we locate a facility at candidate site j ∈J 푋 = { 푗 0 if not 1 if demand node i ∈I is served by a facility at candidate site j ∈J 푌 = { 푖푗 0 if not
With this notation, we can formulate the set covering problem as follow:
Minimize ∑i ∈I ∑j ∈J ℎ푖푑푖푗푌푖푗 (2.11)
∑j ∈J 푌푖푗 = 1 Ɐ i ∈ I (2.12)
∑j ∈J 푋푗 = 푝 (2.13)
푌푖푗 − 푋푗 ≤ 0 Ɐ i ∈ I ; j ∈ J (2.14)
푋푗 ∈ {0,1} Ɐ j ∈ J (2.15)
푌푖푗 ∈ {0,1} Ɐ i ∈ I ; j ∈ J (2.16)
The objective function (2.11) is to minimize the total demand-weighted distance between each demand node and the nearest facility. Constrain (2.12) requires each demand node i ∈I is served by one located facility. Constrain (2.13) shows that p facilities are located. Constrain (2.14) ensures that demand nodes can only be linked to located facilities. Constrain (2.15) – (2.16) are the integrality constrains.
5.2.1.4 Fixed charge facility location problems (FCFLP)
The p-median problem has important assumptions but may not fit for realistic scenarios. First, all locating facility costs are assumed to equal in the set of candidates. Secondly, it assumes that the facilities do not have capacities on the demand that they can serve. Third, how many facilities should be opened is specific. The FCFLP relaxes all these three assumptions. The objective of the FCFLP is to minimize total facility and transportation costs, the same as the p-median problem. However, the different between the p-median and the FCFLP is that in the FCFLP, the
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capacities of facilities are given. So that served demand may not be assigned to its nearest facility as in the p-median model.
The basic model can be formulated using the following notations (Current et al., 2002);
Inputs
ℎ푖 = demand at node i ∈I
푓푗 = fixed cost of locating facility at candidate site j ∈J
퐶푗 = Capacity of facility at candidate site j ∈J
푑푖푗 = distance between demand node i ∈I and candidate facility site j ∈J 훼 = cost per unit demand per unit distance
Decision variables 1 if we locate a facility at candidate site j ∈J 푋 = { 푗 0 if not 1 if demand node i ∈I is served by a facility at candidate site j ∈J 푌 = { 푖푗 0 if not
With this notation, we can formulate the set covering problem as follow:
Minimize ∑j ∈J 푓푗푋푗 + 휶 ∑i ∈I ∑j ∈J ℎ푖푑푖푗푌푖푗 (2.17)
∑j ∈J 푌푖푗 = 1 Ɐ i ∈ I (2.18)
푌푖푗 − 푋푗 ≤ 0 Ɐ i ∈ I ; j ∈ J (2.19)
∑j ∈J ℎ푖푌푖푗 − 퐶푗푋푗 ≤ 0 Ɐ i ∈ I (2.20)
푋푗 ∈ {0,1} Ɐ j ∈ J (2.21)
푌푖푗 ∈ {0,1} Ɐ i ∈ I ; j ∈ J (2.22)
The objective function (2.17) is to minimize the total fixed facility location cost and the total transportation cost from a demand-weighted distance. Constrain (2.18) requires each demand node i ∈I is served by one located facility. Constrain (2.19) ensures that demand nodes can only be linked to opened facilities. Constraint
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set (2.20) forces demands to be assigned only to open facilities as well as constrain (2.19), but also control the upper bound of facility capacities to serve demand nodes. Constrain (2.21) – (2.22) are the integrality constrains.
5.2.1.5 Hub location problem
Hub location problem (HLP) is one of the booming research fields in location problems. The hub location problem relates though flows, for example, passengers, freight, that transport from origins to destinations. Origin-destination (OD) pairs may be connected by direct trips or via hubs. The hub location problem is a network that requires at least one hub facility which can allow flows to be redirected at a hub node and allow flows to be broken and consolidated to the same vehicles for the same destination. These provide many origins and destinations can be connected with fewer links than the direct connections. This transport is called a hub-and-spoke network (Liu et al., 2003). For example, a fully direct connection with k nodes totally has k(k- 1) origin-destination pairs. By purposing a hub node to connect all other nodes in a center, there will reduce connections to only 2(k-1) for all origin-destination pairs (Farahani and Hekmatfar, 2009). In addition to commodity sorting, cost reduction from economies of scale is especially strong advantages in transportation sectors.
The hub location concept is extended in the airline industries and postal industries. However, the telecommunication industry is one of pioneer hub location users. Nowadays, shipping companies in many fields like maritime, freight transportation, railways, transits, and messenger delivery can take benefits of the hub concept.
In the mathematical model, most networks can be formulated as linear programming problems. The preliminary paper, addressing in the network hub location problem area, initiated with the pioneering work of O'kelly (1986, 1987). The research studied airline passenger networks and referred to the mathematical formulation for the single allocation p-hub median problem. The objective function focused on minimized total transportation costs. Later, four fundamental hub location
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strategy models and the difference between single and multiple allocation hub location problems with similar objective functions are proposed by Campbell (1994, 1996). The development and trend of hub location problems can follow from several review papers such as Alumur and Kara (2008); Campbell and O'Kelly (2012); Farahani et al. (2013).
In many location problems, service from origins to destinations is related to the distances between OD pairs and located facilities to which are assigned. Various mathematical models are formulated to solve location problems. According to Campbell (1994), discrete hub location problem models, which have been wildly applied in the literature, are consisted of (1) The p-hub median problem (2) The uncapacitated hub location problem (3) The p-hub center problem (4) Hub covering problems.
These models have been used in sitting public and private facilities. To deal with furthermore solutions, these mathematical models need to be developed to cover a variety of ways.
6. Review of data sources
6.1 Automatic data collection system at the Bangkok terminal’s gates
This data is provided by the Department of Land Transport. To collect data, there is a sensor at the Bangkok truck terminal’s gate. When a vehicle passes through the gate, the sensor will collect data including a number of vehicles and vehicle types automatically for 24 hours. The vehicle types consist of a 4-wheel truck, a 6-wheel truck, a 10-wheel truck, a 12-wheel truck, a 14-wheel semitrailer, an 18-wheel semitrailer, a 20-wheel semitrailer, a more than 20-wheel semitrailer, a 14-wheel
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trailer, an 18-wheel trailer, a 20-wheel trailer, and a more than 20-wheel trailer. Figure 12 shows each type of vehicle used for freight transportation in Thailand.
This study used data in a weekly format, 27 weeks, from February 2018 till August 2018. Only weekday (Monday to Friday) data is provided. The limitation of this data is the direction of vehicles when passing through the gate is not available. Then, there has to find other ways of helping to distinguish directions.
Pick-up truck 4-wheel truck
6-wheel truck 10-wheel truck
Trailer Semitrailer
Figure 12 Vehicle classification
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The vehicle data in each terminal show in Figure 13 to 15 and comparing the number of vehicles in three truck terminals also summarize in Figure 16 to 18.
Phutthamonthon Truck Terminal 10-wheel truck Other 5.07% 2.87%
6-wheel truck 10.42%
4-wheel truck 81.64%
Figure 13 Types of vehicle passing through the Phutthamonthon truck terminal
Khlong Luang Truck Terminal Other 0.73% 20-wheel semitrailer 23.12% 4-wheel truck 28.22%
18-wheel semitrailer 10.48%
6-wheel truck 10-wheel truck 20.62% 16.83%
Figure 14 Types of vehicle passing through the Khlong Luang truck terminal
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Rom Klao Truck Terminal Other 20-wheel 0.91% semitrailer 9.33% 4-wheel truck 25.28% 18-wheel semitrailer 24.75% 6-wheel truck 10.19%
12-wheel truck 10-wheel truck 14.30% 15.25% Figure 15 Types of vehicle passing through the Rom Klao truck terminal
Truck Types
12-wheel truck
10-wheel truck
6-wheel truck
4-wheel truck
0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 Number of vehicles
Phutthamonthon Truck Terminal Khlong Luang Truck Terminal Rom Klao Truck Terminal
Figure 16 Weekly average number of trucks passing through the Bangkok truck terminals
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Semitrailer Types
> 20-wheel semitrailer
20-wheel semitrailer
18-wheel semitrailer
14-wheel semitrailer
0 200 400 600 800 1,000 1,200 1,400 Number of vehicles
Phutthamonthon Truck Terminal Khlong Luang Truck Terminal Rom Klao Truck Terminal
Figure 17 Weekly average number of semitrailers passing through the Bangkok truck terminals
Trailer Types
> 20-wheel trailer
20-wheel trailer
18-wheel trailer
14-wheel trailer
0 2 4 6 8 10 12 14 Number of vehicles
Phutthamonthon Truck Terminal Khlong Luang Truck Terminal Rom Klao Truck Terminal
Figure 18 Weekly average number of trailers passing through the Bangkok truck terminals
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6.2 Survey data
This survey was conducted by Department of Land Transport (2017b) in January 2017 for collecting data in the three truck terminals in a week. The survey was made in two parts which collected data in different time periods.
6.2.1 Road freight vehicle survey
The first part collected a number of road freight vehicles with providing vehicle types and directions, inbound or outbound, for seven days in a row, 24 hours a day. The summarized data shows below in Figure 19. In the Phutthamonthon truck terminal, there has the highest number of vehicles passing through the terminal. Most of vehicles are truck types. In the Khlong Luang truck terminal, trucks and trailers are mostly used. The ratio of an inbound and outbound vehicle is almost exactly the same. In the Rom Klao truck terminal, semitrailer is a majority of used vehicles.
Average No. of Vehicles per Day
1245
1154
1046
959
715
660
331
322
264
256
228
221
198
195
167
166
166
163
150 150
144
144
142
136
121
109
79
76
70
58
46
46
35
30
26
25
23
21
21
20
20
18
16
15
9
9
8 8
p i c k u p 4 - w h e e l s u m 4 - 6 - w h e e l 10- w h e e l > 1 0 - t r a i l e r s e mi - t r u c k w h e e l t r u c k t r u c k w h e e l t r a i l e r v e h i c l e t r u c k
Phutthamonthon inbound Phutthamonthon outbound Khlong Laung inbound Khlong Laung outbound Rom Klao inbound Rom Klao outbound
Figure 19 Average number of vehicles in the survey data
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6.2.2 Origin-destination survey
The second part is the origin-destination (OD) survey. The survey separated directions, inbound and outbound survey, into two groups. Both surveys collected information including vehicle types, commodity types, origin/destination province, origin/destination place, number of rests along the previous trip, etc. 500 surveys were conducted per terminal. The results show that each terminal has various shipped products. The major shipped products in all terminals are consumer products, construction materials, and electronic parts. The summarized of shipped products shows below in Figure 20 to 25.
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Phutthamonthon Truck Terminal Inbound Agricultural Chemical Products, 6.87% Products, 2.15% Construction Materials, Plastics, 6.87% 12.02%
Others, 5.58% Fuel products, 0.86%
Electronic Parts, 9.01%
Electronic Devices, 3.00%
Consumer Products, 53.65%
Figure 20 Shipped product in the Phutthamonthon truck terminal (inbound)
Phutthamonthon Truck Terminal Outbound
Others, 0.37%
Consumer Products, 99.63% Figure 21 Shipped product in the Phutthamonthon truck terminal (outbound)
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Khlong Luang Truck Terminal Inbound Electronic Parts, 1.16%
Construction Materials, 46.51% Consumer Products, 52.33%
Figure 22 Shipped product in the Khlong Luang truck terminal (inbound)
Khlong Luang Truck Terminal Outbound Others, 1.15% Electronic Parts, Chemical 2.30% Products, 0.46%
Consumer Construction Products, 44.70% Materials, 51.38%
Figure 23 Shipped product in the Khlong Luang truck terminal (outbound)
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Rom Klao Truck Terminal Inbound
Others, 3.47% Plastics, 1.49% Agricultural products, 3.47% Electronic Parts, Chemical 3.96% Products, 0.99% Electronic Construction Devices, 3.47% Materials, 3.47%
Consumer Products, 79.70%
Figure 24 Shipped product in the Rom Klao truck terminal (inbound)
Rom Klao Truck Terminal Outbound Fuel products, Plastics, 0.54% Agricultural 2.45% Others, 3.81% Products, 6.81% Chemical Electronic Parts, Products, 1.36% 5.45% Construction Electronic Materials, 5.72% Devices, 3.27%
Consumer Products, 70.57%
Figure 25 Shipped product in the Rom Klao truck terminal (outbound)
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Considering destination points, although these three-truck terminal’s purpose mainly is to support goods distribution in the Bangkok metropolitan area, various destinations are found from the survey showed in Figure 26 to 28. All terminals not only ship products into the metropolitan area, but also ship into other regions of the country. The Phutthamonthon truck terminal is noticeable from shipping products into every region, mostly in the north, northeast and south regions. The Khlong Luang truck terminal mainly shipped to the Bangkok metropolitan area and center region. The Rom Klao truck terminal largely shipped to the Bangkok metropolitan area and eastern region. The sum of both areas already exceeds 86% of total freight transportation.
Phutthamonthon Truck Terminal
Northeast North 27.24% 32.46%
East 4.10%
West 2.61% South Bangkok Metropolitan Area 21.64% 3.36% Center 8.58%
Figure 26 Destination areas from the Phutthamonthon truck terminal
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Khlong Luang Truck Terminal Northeast North 8.02% 2.83% East South 9.20% 4.48% West 2.12% Center 15.57%
Bangkok Metropolitan Area 57.78%
Figure 27 Destination areas from the Khlong Luang truck terminal
Rom Klao Truck Terminal North South Northeast 0.54% 1.34% 2.42% Center East 8.60% 16.94%
West 1.08%
Bangkok Metropolitan Area 69.09%
Figure 28 Destination areas from the Rom Klao truck terminal
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Chapter 3 Methodology
The methodologies of this study are divided into 3 parts; data distribution, road network, and mathematical model. A conclusion and relation of methodologies are summarized in the Figure 29.
Figure 29 An overview of methodology
1. Road network data
Freight transportation in Thailand, nowadays, mostly has relied on road transportation. In the road network, there has to have three sets of nodes; origin nodes, destination nodes, and candidate truck terminal nodes. While the road network is provided by NAM. However, more adjustment is required.
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1.1 Origin nodes
In the Bangkok metropolitan area, there are currently three tuck terminals; Phutthamonthon, Khlong Luang, and Rom Klao. The roles of these truck terminals are very useful for freight activities inside the Bangkok metropolitan area and also other parts of the country. The location of terminals that were inputted in the program is shown in the appendix C, Table 16.
1.2 Destination nodes
Due to the national scale and objective to find suitable provinces to locate a truck terminal, then, each province is allowed to have only one node as a representative point of a province. To choose suitable points, information from the detailed survey was considered. Following the detailed survey, the most commodity type and its destination point is a consumer product and retail stores. Then, this study chose one of the popular retail stores in Thailand located in the downtown of each province as a representative point. The list of locations in each province is shown in the appendix C, Table 17.
1.3 Candidate truck terminal nodes
The candidate truck terminal location is one of the most important issues in this study. Which province is selected and what area to locate in that province is serious issues. According to the Department of Land Transport (2015), there is a rank of potential provinces by giving a score to each province. The factors consist of GPP per population, factory per population, commodity volume, railway network availability, and future scheme to support from the government. Excluding border provinces, these are the list of potential provinces chosen to be a candidate province; • North and center regions; Chiang Mai, Lampang, Lamphun, Phitsanulok, Nakhon Sawan, Lopburi • Northeast and east regions; Khon Kaen, Ubon Ratchathani, Udon Thani, Prachin Buri, Nakhon Ratchasima
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• South regions; Surat Thani, Nakhon Si Thammarat
To choose the exact proposed location, more research should conduct after considering the results of this study. However, another way to assume location for this study is the following in the Department of Land Transport (2015). There is information about the purposed location in some provinces. One of the criteria to select location is nearby downtown area and railway station for intermodal freight transportation activities. The list of top potential provinces is shown in the appendix C, Table 18.
1.4 Road network
A road network in this study comes from National Model (NAM) provided by the Department of Land Transport. The model consists of many links combined until being transportation networks including roads, railways, air, and marine. The NAM normally is used to simulate traveling, both passenger and freight transportation, between provinces. Results could be used to plan, analyze, and evaluate related studies in national scale logistics and transportation (OTP, 2015).
In this study, a road network from the NAM was mainly used. Links are connected to be various types of roads. However, this study focuses on big vehicle transportation, then the hierarchy of each route from the Thai highway numbering system and type of roads needs to be added. Firstly, a road number was added following the numbered highway system by the Department of Highhways (2017). A one-digit number is for the main highway connected between Bangkok and other regions in Thailand. Some of them are the Asia highway. A two-digit number is for a major road. This type of road links a one-digit number’s road to important areas in many provinces. A three-digit number is for a sub-major road. This road linked from a one-digit or two-digit number’s roads to important areas. A four-digit number is for a minor road. Some road is not available for big vehicle accessibility. Other more minor roads are excluded from this study. After adding numbered highway, a hierarchy of
46
roads was specific. The one-digit numbered road is the first priority and then two- digit, three-digit, and four-digit numbered road, respectively.
In a GIS program, a network dataset was created to input the NAM network for checking that each link is connected perfectly. After checking, the route between each node was calculated. To find the routes, there are assumptions below: • U-turn is allowed at the intersections • Higher hierarchy road is selected first
An example of the routes from Bangkok truck terminals to destination nodes in each province is shown in Figure 30 to 33. Other routes between Bangkok truck terminals and candidate facilities and also between candidate facilities and destination nodes were calculated to find distances.
Province boundary Road Destination node Shortest route from the Bangkok truck terminals to destination nodes
Figure 30 Routes from the Bangkok truck terminals to destination nodes
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1.4.1 Phutthamonthon truck terminal
For the North region, road no.1 and 11 are the main roads that has high transportation activity passing thought. It clearly sees that there are high volume commodities shipped to the North showing in Figure 31. For the Northeast region, road no.2 and 24 are the main roads for transportation activity. They are separated at the Northeastern gate province, Nakhon Ratchasima. For the South region, road no. 4 and 41 are the main roads. A star province means a province having high volume shipping, mostly are high economical provinces.
Province boundary Road Destination node Bangkok truck terminals Level of freight volume Provinces with high-volume shipping
Figure 31 Phutthamonthon truck terminal route analysis
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1.4.2 Khlong Luang truck terminal
Most transportation is in the Bangkok metropolitan area and northeastern provinces. In the Northeast region, road no.2 and 24 have significantly high shipment volumes for delivery to northeastern provinces. The highest shipping volume, the star province in Figure 32, is Ubon Ratchathani province.
Province boundary Road Destination node Bangkok truck terminals Level of freight volume Provinces with high-volume shipping
Figure 32 Khlong Luang truck terminal route analysis
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1.4.3 Rom Klao truck terminal Most transportation is mainly in the Bangkok metropolitan area and some provinces in the East region. However, shipments spread in many cities across the country. One noticeable high shipment destination, the star province, is Chon Buri with having the biggest marine port of Thailand.
Province boundary Road Destination node Bangkok truck terminals Level of freight volume Provinces with high-volume shipping
Figure 33 Rom Klao truck terminal route analysis
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From the data, the possible location of terminals is along to the road no. 1, 2, 24, 4 and 41. Considering from among of trips, the North and Northeast are both regions that appropriate for investment. However, the upper and central parts of the center region also have to consider.
2. Data manipulation
Following the review of data sources, there are two types of data using in the study. Both data, the terminal gate data and the survey data, are used to calculate the amount in OD pairs from Bangkok terminals to other provinces across the country. The centroid of each province is assumed to be a destination node. However, due to the objective of the study, to locate hub truck terminals for supporting goods distribution in other parts of the country, then a specific area of study is introduced.
2.1 Buffer area
The goal of this study is to evaluate potential regional truck terminal locations for supporting the shipping sectors in other regions of Thailand. Then, to achieve the goal, the buffer area is introduced to prevent locating facilities near Bangkok and nearby provinces. Demands shipped into the buffer area are assumed to be zero to omit a weight-distance factor in the mathematical model. According to Figure 26 to 28, many destinations are in the Bangkok metropolitan region and nearby provinces. These provinces have high shipping volume from economic concentration. If these provinces do not omit from the calculation, the results of the facility location can be located in these provinces or nearby due to a huge shipment instead of supporting transshipment for regional delivery. Then, provinces that less than a few hours driving from existing terminals with normally do not need a rest along a trip are chosen. Chon Buri and Rayong, one of main industrial provinces, are also omitted demand. When considering more details, the main destination points in Chou Buri are Laem Chabang port and in Rayong are factories. These types of destination do not need a transshipment point for reconsolidation. A direct shipment is more suitable for these
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deliveries. Provinces in the buffer include the Bangkok metropolitan area, following Figure 34.
The remain shipping percentages from the Phutthamonthon, Khlong Luang and Rom Klao truck terminals across the country excluding the buffer area are 91.8%, 30.4%, and 7.3% respectively. It can notice that shipping percentage from the Rom Klao terminal is small amount. This is a result from, a one reason, the terminal location that is next to the Lat Krabang Inland Container Depot (ICD). Normally, the ICD is used to support containers which are unloaded from the ship at the Laem Chabang port. Then, there is special interchanging movement between the Rom Klao truck terminal, Lat Krabang ICD, and Laem Chabang port.
Then, Rom Klao data are omitted in the mathematical model. Only Phutthamonthon and Khlong Luang truck terminal terminals are used to analyze location of proposed truck terminals.
Province boundary Buffer province Bangkok truck terminal
Figure 34 Buffer area
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2.2 Data transformation
The automatically collected data at the terminal’s gate is useful on account of a real number of vehicles passing through the terminal gates but do not provide directions, inbound or outbound. While the survey data also counted a number of vehicles as well but using people counted manually and out-of-date compared to the automatically collected data. More than that, the detail survey also provided samples of OD pairs from these Bangkok truck terminals. Then, combining these data can be more useful to predict a recent number of vehicles.
Firstly, due to the limited period of the automatically collected data at the terminal’s gate, from February 2018 to August 2018, 30 weeks of data are received. Other left data would be forecasted to become annual data. Skewness and Kurtosis are used to check data distribution first before forecasting the data. The data distribution is checked that it is a normal distribution or not. If there is not the normal distribution, a transformation that gives the best Skewness and Kurtosis values is applied. Then, random under normal distribution is used to find the left 22 weeks fulfilling a year data. The transformation back to the original distribution is the last procedure.
Secondly, the automatically collected data is used as a base data then, ratios of inbound/outbound vehicles from the survey data are combined to find an exact number of vehicles in each direction. Following Table 19 in the appendix, all ratios are seemed to around one that means a number of vehicles entering and going out the terminals are almost the same. These vehicles could mostly come to deliver or pick up goods or both simultaneously. The average number of vehicles per week also shows in appendix D, Table 19 and the methodology conclusion shows in Figure 35. After this section, only the number of outbound vehicles is analyzed to find the optimal truck terminal location.
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Figure 35 Methodology to find number of vehicles with showing direction
2.3 OD generation
In this study, origin points are the Bangkok truck terminals that in this state are Phutthamonthon and Khlong Luang truck terminals. While destination points are the centroid of each province in Thailand. One province has only one destination node.
After we knew the number of vehicles passing through the Bangkok terminals both inbound and outbound, in this section, destination points of each trip were discussed. In the detailed survey, there are questions in a destination point and a used vehicle. Then, the destination percentage in each type of vehicles was calculated. For example, at the Phutthamonthon truck terminal, the detailed survey shows that there are 19 trips to Chiang Mai from a total of 167 trips shipped by a 10-wheel truck. Then, there is 11.38% of a 10-wheel truck driving to Chiang Mai from the Phutthamonthon truck terminal. This percentage is used to calculate the weekly data from the previous calculation. Figure 36 shows the conclusion of the methodology.
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Figure 36 Methodology to find number of vehicles in each destination
2.4 Destination’s demand
There are many types of vehicles using nowadays for freight transportation. Different type of vehicles responds to different purpose and strategy of delivery. In Thailand, shipping products across the country use various types of vehicles depending on shipper and land transportation policies in each city. In the Bangkok terminals, it can categorize delivery vehicles following Table 20 in the appendix D. The load capacity of each vehicle also reports as well.
In this step, the carrying capacity combined with the number of trips by each vehicle type can result in shipped volume to each province from Bangkok terminals. The procedure is shown in Figure 37.
Figure 37 Methodology of how to calculate products shipped by each vehicle types
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2.5 Shipped product calculation
Although each province knows shipping volume, normally there is no one type of shipped product or combined every product type at the same time shipping. Then, finding shipped volume in each product type was presented in this step.
According to outbound shipped products shown in Figure 21 and 23, various product types are presented. However, after applying the buffer area, five types of products are left after. The summarize of shipped products after applying the buffer area is shown in Figure 38 and 39.
To find the percentage of product types in each destination, the detailed survey was applied again. The product type percentage was calculated in each destination separately following the answer in the detailed survey. The example of the calculation is shown in Table 4.
Table 4 Percentage of product types in each destination
Destination Province: Songkla Origin Point Consumer Construction Chemical Electronic Other Sum product material product part Phutthamonthon 100.00% 0.00% 0.00% 0.00% 0.00% 100% Khlong Luang 0.00% 83.33% 0.00% 16.67% 0.00% 100%
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Phutthamonthon Truck Terminal Outbound
Others, 0.41%
Consumer Products, 99.59%
Figure 38 Outbound shipped products from the Phutthamonthon truck terminal when applying the buffer area
Khlong Luang Truck Terminal Outbound Others, Electronic Parts, 0.72% 4.32% Chemical Products, 1.44%
Consumer Construction Products, 51.08% Materials, 42.45%
Figure 39 Outbound shipped products from the Khlong Luang truck terminal when applying the buffer area
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The product type percentage was combined with weekly shipped volume to find the amount of product in each type. The average shipping volume result from the Phutthamonthon truck terminal shows in Table 21 and from the Khlong Luang truck terminal shows in Table 22 in the appendix D.
2.6 Forecast of freight volume
A truck terminal can help many aspects of road transportation; cost reduction, increase delivery efficient, control transport activity. The new located truck terminal comes with significant investments. More than that results of new truck terminal are critical. The impact of the truck terminal location should also positively contribute to the long term. Therefore, the location selection should scrupulously consider in a long-term strategy.
According to the current result, there are weekly data for one year. However, the location selection is a sensitive topic with a long lifetime using. Then, the forecast data was implemented in this section. The data were forecast for the next 30 years which equal the supposed lifetime of the truck terminal.
In accordance with the Department of Land Transport (2015), they purposed a commodity forecast for freight transportation in Thailand. The Growth Model was calculated from two factors; population and gross provincial product (GPP). The equation is shown below:
Growth Truck Freight = 0.1004*Population + 0.4909*GPP (3.1)
The forecast results are separated into four periods; 1st – 4th year, 5th – 8th year, 9th – 22nd year, and after 23rd year. Only after the 23rd year, the growth rate is constantly 3.00% per year and in every province. The example of forecast results is shown in Table 5 and the full table is shown in the appendix D, Table 23.
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Table 5 Growth truck freight rate in each province
Province 1st – 4th 5th – 8th 9th – 22nd After 23rd Year Year Year Year Chiang Mai 4.20% 3.93% 3.51% 3.00% Songkhla 4.72% 4.45% 4.02% 3.00% Note: unit = growth rate/year
This growth rate was applied with current data to find the volume of a commodity in each province for the next 30 years. Then, there are 31 years of data as a result, one year from current data and 30 years from the forecast. After this step, data will be used in the mathematical model to find the optimal location and number of a truck terminal.
3. Related costs
To locate one facility, there are related costs in many aspects for consideration; transportation cost, construction cost, land price, operation cost, etc. In this section, there are two types of costs; investment cost that relates to facility location and transportation cost that relates to vehicle movement.
3.1 Investment cost
There are many costs when locating a facility. A Chiang Khlong intermodal facility project (Department of Land Transport, 2017a) and the cost estimation in the study of potential transshipment points (Department of Land Transport, 2015), both the latest related truck terminal studies by Department of Land Transport, are used as an example to find a suitable price in this section.
In the Chiang Khlong study, the costs are classified into five categories; land price (compensation cost), construction cost, start-up cost, operation cost, and maintenance cost. While in the study of potential transshipment points classified costs
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into land price, construction and operating cost, start-up cost and maintenance cost, and operation cost.
This study has chosen costs showing in Figure 40 for locating a truck terminal. There are four cost categories applied to this study.
Land price • varies on location
Construction cost • varies on the number of platforms
Maintainance cost • varies on types of equipment
Operation cost • assumed to equal all truck terminals
Figure 40 Cost category in the investment cost
3.1.1 Land price
In the study of potential transshipment points, land price varies on selected locations in provinces. The land sizes are around 100 to 150 rai for the potential provinces. This study, land price data is based on the Department of Treasury of Thailand from the information of year 2016 to 2019.
To calculate the land price for the candidate facilities, the Chiang Khlong intermodal facility schema is used for comparison. According to the Department of Treasury data, land price in the Chiang Khlong area is 4,100 THB/wa and the size of the facility in the schema is 330 rai. While in the feasibility study, there is the conclusion of land price equal to 779.76 million THB. The Chiang Khlong ratio of
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land price and size was used to adapt with other provinces to find the results showing in Table 6 and calculation in the appendix E, Table 24.
Table 6 Land price of the candidate locations
Province Land Price (Million THB)
Khon Kaen 198.83 Nakhon Ratchasima 129.67 Chiang Mai 561.91 Ubon Ratchathani 172.90 Surat Thani 129.67 Nakhon Si Thammarat 129.67 Prachin Buri 86.45 Udon Thani 216.12 Phitsanulok 86.45 Nakhon Sawan 216.12 Lopburi 259.34 Lampang 129.67 Lamphun 345.79 Saraburi 432.24 Chiang Khlong 779.76 Note: The land sizes are assumed to equal 150 rai in the candidate facilities
3.1.2 Building construction cost
In the Chiang Khlong intermodal facility schema, five building construction cost is 660.8 million baht or around 132.16 million baht per building. Then, this study assumed that the building construction cost is varied on a number of buildings constructed in a truck terminal.
According to the study of potential transshipment points (Department of Land Transport, 2015), a truck terminal has several types of buildings, for example, a main
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office, a Department of Land Transport office, two platform buildings, two warehouses, a customs warehouse, a maintenance building, an accommodation, a checkpoint building, a canteen, a weighing station building and opened spaces for other activities that calculate to equal one building. Then, total buildings are assumed to equal 13 buildings in a truck terminal at the first scenario. However, the number of the platform building and warehouse should vary on commodity volumes in a truck terminal. According to the Bangkok truck terminal information, this study assumed that one platform building can support commodity 300 tons per day. A sizing scale of a truck terminal is shown in the Table 7 that the platform building and warehouse are varied depend on size. The number of platform terminal starts from two building from the study of potential transshipment points.
Table 7 Size of a candidate truck terminal
Truck Terminal Size No. of platform Supported No. of building Commodity Volume warehouse (ton/day) S 2 600 2 M 8 2,400 3 L 12 3,600 4 XL 16 4,800 5
Then, the construction cost is shown in Table 8.
Table 8 Construction cost
Truck Terminal Size Construction cost (Million THB) S 1,718.08 M 2,643.20 L 3,304.00 XL 3,964.80
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3.1.3 Maintenance cost
The maintenance cost is divided into two categories; office equipment maintenance and facility equipment maintenance following in the study of potential transshipment points (Department of Land Transport, 2015). The office equipment has maintenance every five years and the facility equipment has maintenance every 15 years. Moreover, the facility equipment cost is varied on the number of platform terminal as same as the construction cost. The maintenance cost is shown in Table 9.
Table 9 Maintenance cost
Maintenance cost Cost (Million THB) Office equipment (every 5 years) 0.3 Facility equipment (every 15 years) 10 (per platform)
3.1.4 Operation cost
The annual operation cost is assumed from the study of potential transshipment points (Department of Land Transport, 2015). The cost is assumed to be ten million THB in every terminal.
3.2 Transportation cost
Nowadays, there are many types of shipping vehicles in Thailand. At least 13 vehicle types have been driven passing though the Bangkok truck terminals shipping products to other parts of the country. However, to reduce complexity, two vehicle types are selected as representatives in the model. A 4-wheel vehicle was chosen for a small vehicle traveling in a short distance and a 10-wheel vehicle was chosen for a line-haul vehicle traveling in a long distance. The transportation cost was calculated following the study of transportation and distribution cost by Department of Land
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Transport (2017b). The transportation cost for a 4-wheel vehicle and a 10-wheel vehicle are 8.36 THB/km-ton and 1.00 THB/km-ton, respectively. The detail of transportation cost calculation is shown in the appendix E, Table 25.
4. Mathematical model
4.1 Mathematic program
Operational research (OR) has an important role in problem-solving techniques and methods applied in the detection of improved decision-making and efficiency. For large-size problems, it was not possible to carry it out manually, which is now possible with the use of computers. CPLEX is an optimization software with providing flexible and high-performance mathematical programming solvers for linear programming, mixed integer programming, quadratic programming by simplex or interior-point methods, and linear/convex quadratic integer programs by a branch- and-bound procedure. These solvers include a distributed parallel algorithm for mixed integer programming to leverage multiple computers to solve difficult problems.
This study used the CPLEX version 12.8 to solve the mathematical model of the location problems. All data from both the freight volume and the distances between nodes were imported into the program. The location model was written in the CPLEX language to find the optimal solutions.
4.2 Model structure
There are four fundamental facility location models; covering problems, center problems, median problems, and fixed charge facility location problems. Each model has the same goal to find the optimal facility location. But results are different from the different objective function of the models.
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In this section, the median location model was developed according to current situations. According to a distance between a demand node and the nearest facility, the median model can vary the benefit from a relationship between demand volume and distance such that the cost from facilities to demand nodes is minimized and an optimal number of located facilities in an area of interest is satisfied the customer demand. While the covering model and center model both are considered only a distance factor. Then, the medium model is applied to this study. The amounts of supplied demands to each province destination are fixed, then the mathematical model was adjusted to fix this pattern of data. The problem was to determine the truck terminal locations to satisfy all demand nodes and the total demand-weighted distance was minimized. The assumptions used in this problem were:
(1) The quantities of products between supply-demand pairs of origin (Phutthamonthon and Khlong Luang truck terminal) and destination (province) are known, (2) The number of potential facilities is known with an uncapacitated condition, (3) The demand at destination nodes are supplied from only one located candidate facility, (4) The distribution between origins and candidate facilities and between candidate facilities and destinations is by a line-haul truck and a small vehicle respectively. The transportation cost varies up to a vehicle type, (5) The lifetime distribution of a facility is assumed to equal 21 years, (6) All costs are assumed to be a present cost. There is no interest of inflation.
Figure 41 easily presents a supply chain of this problem that including origin nodes (Bangkok terminals), candidate facility nodes, and destination nodes (provinces). A 10-wheel truck as a line-haul vehicle and a 4-wheel vehicle as a small vehicle are chosen as representative data of vehicles in the model.
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Figure 41 A simplified network of the model
4.2.1 Weekly freight demand model
The model runs 52 weeks of data first before finding the best potential locations according to the objective function. The investment cost is calculated from land price, construction cost, and start-up cost divided by lifetime of the facility and plus a year of operation cost. The following notations for the model are introduced:
Sets I set of origin node i (Phutthamonthon, Khlong Luang) J set of destination node j K set of candidate facility node k H set of product type h W set of week w
Parameters
퐶푘 fixed location cost of candidate facility k
푑푖푘 distance between origin node i to candidate facility node k
푑푘푗 distance between candidate facility node k to destination node j 푤 푤푖푗ℎ total demand of product type h between origin node i and destination node j in week w P number of located facilities
퐹푎 , 퐹푏 fuel consumption cost of line-haul trucks and small vehicles, respectively (THB/ton-km)
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Decision Variables 1 if it has flow between origin node i and candidate hub node k 푤 푋푖푘ℎ = { with shipping product h in week w 0 otherwise 1 if it has flow between candidate hub node k and destination node j 푤 푋푘푗ℎ = { with shipping product h in week w 0 otherwise 1 if candidate hub is located at node k 푌 = { 푘 0 otherwise 푤 푞푖푘ℎ amount of product type h flow from origin i to candidate facility k in week w 푤 푞푘푗ℎ amount of product type h flow from candidate facility k to destination j in week w
The problem can be formulated as follows:
푤 푤 Minimize ∑푘∈퐾 푌푘퐶푘 + ∑푤∈푊 ∑푖∈퐼 ∑푘∈퐾 ∑ℎ∈퐻 푑푖푘퐹푎푞푖푘ℎ + ∑푗∈퐽 ∑푘∈퐾 ∑ℎ∈퐻 푑푘푗퐹푏푞푘푗ℎ (3.2) subject to 푤 푋푖푘ℎ ≤ 푌푘 ∀ 푤 ∈ 푊; 푖 ∈ 퐼; 푘 ∈ 퐾; ℎ ∈ 퐻 (3.3) 푤 ∑푘 푋푖푘ℎ ≥ 1 ∀ 푤 ∈ 푊; 푖 ∈ 퐼; ℎ ∈ 퐻 (3.4) 푤 ∑푘 푋푘푗ℎ = 1 ∀ 푤 ∈ 푊; 푗 ∈ 퐽; ℎ ∈ 퐻 (3.5) 푤 푤 푋푘푗ℎ − ∑푖 푋푖푘ℎ ≤ 0 ∀ 푤 ∈ 푊; 푗 ∈ 퐽; 푘 ∈ 퐾; ℎ ∈ 퐻 (3.6)
∑푘 푌푘 = P (3.7) 푤 푤 푤 푞푖푘ℎ ≥ ∑푗 푤푖푗ℎ ∗ 푋푖푘ℎ ∀ 푤 ∈ 푊; 푖 ∈ 퐼; 푘 ∈ 퐾; ℎ ∈ 퐻 (3.8) 푤 푤 푤 푞푘푗ℎ ≥ ∑푖 푤푖푗ℎ ∗ 푋푖푘ℎ ∀푤 ∈ 푊 ; 푗 ∈ 퐽; 푘 ∈ 퐾; ℎ ∈ 퐻 (3.9) 푤 푤 ∑푗 푞푘푗ℎ − ∑푖 푞푖푘ℎ = 0 ∀ 푤 ∈ 푊 ; 푘 ∈ 퐾; ℎ ∈ 퐻 (3.10) 푤 푞푖푘ℎ ≥ 0 ∀ 푤 ∈ 푊; 푖 ∈ 퐼; 푘 ∈ 퐾; ℎ ∈ 퐻 (3.11) 푤 푞푘푗ℎ ≥ 0 ∀ 푤 ∈ 푊; 푗 ∈ 퐽; 푘 ∈ 퐾; ℎ ∈ 퐻 (3.12)
The objective function (3.2) minimizes the sum of the fixed facility location costs and transportation costs from line-haul vehicles and small vehicles. Constraint (3.3) represents flows between origin and facility can be assigned if that facility is located. Constraint (3.4) allows origin node can link more than one facility. Constraint
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(3.5) states that each destination node must be assign to one located facility. Constraint (3.6) ensures that flows between facility and destination node can’t be assigned if no flows between origin node and that facility. Constraint (3.7) indicates that exactly P facilities are to be located. Constraint (3.8) and (3.9) are number of products shipped between origin node and facility and between facility and destination node. Constraint (3.10) is the flow balance equation. Constraint (3.11) and (3.12) are the standard integrality conditions.
4.2.2 Annual freight demand model
The model runs 21-year data from the collected one-year demand and the 20- year forecasted freight demand. The investment cost is calculated from land price, construction cost, start-up cost, maintenance cost, and plus 21 years of operation cost. The following notations for the model are introduced:
Sets I set of origin node i (Phutthamonthon, Khlong Luang) J set of destination node j K set of candidate facility node k H set of product type h Y set of year y
Parameters
퐶푘 fixed location cost of candidate facility k
푑푖푘 distance between origin node i to candidate facility node k
푑푘푗 distance between candidate facility node k to destination node j 푦 푤푖푗ℎ total demand of product type h between origin node i and destination node j in year y P number of located facilities
퐹푎 , 퐹푏 fuel consumption cost of line-haul trucks and small vehicles, respectively (THB/ton-km)
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Decision Variable 1 if it has flow between origin node i and candidate 푦 푋푖푘ℎ = { hub node k with shipping product h in year y 0 otherwise 1 if it has flow between candidate hub node k and destination 푦 푋푘푗ℎ = { node j with shipping product h in year y 0 otherwise 1 if candidate hub is located at node k 푌 = { 푘 0 otherwise 푦 푞푖푘ℎ amount of product type h flow from origin i to candidate facility k in w year y 푦 푞푘푗ℎ amount of product type h flow from candidate facility k to destination j in year y
The problem can be formulated as follows:
푦 푦 Minimize ∑푘∈퐾 푌푘퐶푘 + ∑푤∈푊 ∑푖∈퐼 ∑푘∈퐾 ∑ℎ∈퐻 푑푖푘퐹푎푞푖푘ℎ + ∑푗∈퐽 ∑푘∈퐾 ∑ℎ∈퐻 푑푘푗퐹푏푞푘푗ℎ (3.13) subject to 푦 푋푖푘ℎ ≤ 푌푘 ∀ 푦 ∈ 푌; 푖 ∈ 퐼; 푘 ∈ 퐾; ℎ ∈ 퐻 (3.14) 푦 ∑푘 푋푖푘ℎ ≥ 1 ∀ 푦 ∈ 푌; 푖 ∈ 퐼; ℎ ∈ 퐻 (3.15) 푦 ∑푘 푋푘푗ℎ = 1 ∀ 푦 ∈ 푌; 푗 ∈ 퐽; ℎ ∈ 퐻 (3.16) 푦 푦 푋푘푗ℎ − ∑푖 푋푖푘ℎ ≤ 0 ∀ 푦 ∈ 푌; 푗 ∈ 퐽; 푘 ∈ 퐾; ℎ ∈ 퐻 (3.17)
∑푘 푌푘 = P (3.18) 푦 푦 푦 푞푖푘ℎ ≥ ∑푗 푤푖푗ℎ ∗ 푋푖푘ℎ ∀ 푦 ∈ 푌; 푖 ∈ 퐼; 푘 ∈ 퐾; ℎ ∈ 퐻 (3.19) 푦 푦 푦 푞푘푗ℎ ≥ ∑푖 푤푖푗ℎ ∗ 푋푖푘ℎ ∀ 푦 ∈ 푌; 푗 ∈ 퐽; 푘 ∈ 퐾; ℎ ∈ 퐻 (3.20) 푦 푦 ∑푗 푞푘푗ℎ − ∑푖 푞푖푘ℎ = 0 ∀ 푦 ∈ 푌; 푘 ∈ 퐾; ℎ ∈ 퐻 (3.21) 푦 푞푖푘ℎ ≥ 0 ∀ 푦 ∈ 푌; 푖 ∈ 퐼; 푘 ∈ 퐾; ℎ ∈ 퐻 (3.22) 푦 푞푘푗ℎ ≥ 0 ∀ 푦 ∈ 푌; 푗 ∈ 퐽; 푘 ∈ 퐾; ℎ ∈ 퐻 (3.23)
The objective function (3.13) minimizes the sum of the fixed facility location costs and transportation costs from line-haul vehicles and small vehicles. Constraint (3.14) represents flows between origin and facility can be assigned if that facility is
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located. Constraint (3.15) allows origin node can link more than one facility. Constraint (3.16) states that each destination node must be assign to one located facility. Constraint (3.17) ensures that flows between facility and destination node can’t be assigned if no flows between origin node and that facility. Constraint (3.18) indicates that exactly P facilities are to be located. Constraint (3.19) and (3.20) are number of products shipped between origin node and facility and between facility and destination node. Constraint (3.21) is the flow balance equation. Constraint (3.22) and (3.23) are the standard integrality conditions.
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Chapter 4 Result and Discussion
1. Route analysis
The ArcGIS was used to have a basic network analysis by a function named the Network Analyst. Not only distances between each point calculated, the road network also distinguished in each region following Figure 42. Then, it could separate Thailand into three parts; North and central regions, Northeast and East regions, and South region. Each region has its own main highways for Bangkok to the farthest of the regions. Although there are roads linking between regions, there are not the main highway or higher/same hierarchy.
Province boundary Road Destination node Shortest route from the Bangkok truck terminals to destination nodes
Figure 42 Three regions of Thailand
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For the North and central regions, road no.1 and 11 are the main road passing through Chiang Rai and Chiang Mai, respectively. For the Northeast region, road no.2 and 24 are the main roads for transportation activity. They are separated at the Northeastern gate province, Nakhon Ratchasima. The road no.2 passes through many high economic and important provinces until reaches the Thai and Lao crossing border in Nong Khai. The road no.24 also passes through many high economic and important provinces until reaches Ubon Ratchathani. For the South region, road no. 4, 41, 42, and 43 are the main roads from Bangkok until the southernmost.
2. Weekly freight demand results
Then, the experiments were conducted on three regions separately; North and Central regions, Northeast and East regions, and South region by using road network separation. The investment cost includes land price, construction cost, and start-up cost divided by lifetime of the facility, 21 years, and plus a year of operation cost. The size of all candidate truck terminals is assumed to be S size, two platform buildings. The mathematical model was implemented and computation for optimization purpose was conducted using CPLEX 12.8.
2.1 North and Central regions
In North and Central regions, there are two main vertical highways across the regions. The main highway from Bangkok to the North is split into two highways starting from Nakhon Sawan city which is located at the border of the two regions. Along the regions there are many horizontal roads linked two main highways but the main horizontal one is at Phitsanulok city, one of the candidate provinces. One main vertical highway continues to Lumphun and then Chiang Mai, the highest demand city in the region while another highway travels north and ends at the country’s boundary in Chiang Rai, the major cross-border trading city.
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First, there are six candidate locations; Chiang Mai, Lampang, Lamphun, Phitsanulok, Nakhon Sawan, Lopburi. In the experiment, the number of allowed located facilities is varied until two. Then, the total costs turn to increase. When only one terminal is allowed, the model selected Lamphun as the main location due to its costs and demand-weighted distances from Chiang Mai, the city with highest demand in the regions. However, when there are two terminals, the model decides to put truck terminals in Chiang Mai to feed high demands in the North and in Lopburi to support the Central region. Both total cost and transportation cost are sharply decreased. When we could have up to three terminals, the model chooses Chiang Mai, Phitsanulok, and Lopburi as the locations of the facilities. After that, the transportation cost does not significantly decrease any longer. When four and five terminals are allowed, Nakhon Sawan and Lampang are added as the fourth and fifth terminal, respectively. Figure 43 shows costs for a year when p truck terminals are opened. The result shows that with two terminals, at Chiang Mai and Lopburi, the total cost is the lowest point while transportation costs sharply decrease from having two terminals.
North and Central Regions 2,500
2,000
1,500 Investment cost 1,000 Transportation cost
Total cost Cost (Million THB) (Million Cost 500
- 1 2 3 4 5 6 Number of located p truck terminal Figure 43 Costs of North and Central regions when located p terminals (weekly data pattern)
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p = 1 p = 2 p = 3
p = 4 p = 5 p = 6 Figure 44 Optimal solution in North and Central regions when located p terminals (weekly data pattern)
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2.2 Northeast and East regions
In the Northeast, there is a main vertical highway, from Saraburi, one of high demand province and the starting point of the Northeast, to the northernmost of the region. Along the way, there are three main branched major roads that pass through the region to the eastern boundary. These major roads are split at Nakhon Ratchasima, Khon Kaen, and Udon Thani, ordering by latitude. Then these three provinces are ranked in the top of best performing economies in the region. While in the East region, there is a motorway from Bangkok directed to Chon Buri and Rayong, the main industrial provinces of the country. The highway goes along the coastline until reaching the border of Cambodia. And there is another highway starting separately from Saraburi to Cambodia as well.
At first, there are five candidate provinces to locate a facility. The candidate provinces consist of Khon Kaen, Nakhon Ratchasima, Ubon Ratchathani, Prachin Buri, and Udon Thani. When only one terminal is allowed, the model chooses Khon Kaen, the city in the center of the regions. This follows the same logic with the North and central regions' case as it selects the location in the center as the solution to obtain the lowest transportation cost from a small vehicle. With two terminals allowed, Khon Kaen and Ubon Ratchathani are chosen to support high demands in both and nearby provinces. Another reason is the same pattern with the North and central regions’ case that it chooses provinces with a highway junction. Nakhon Ratchasima, Ubon Ratchathani, and Udon Thani are chosen when the parameter is changed to three terminals. The lowest total costs are found when locating three terminals, moreover, the transportation cost significantly decreases from locating two terminals. However, the location of the third terminal, Ubon Ratchathani, has to be deeply considered in border-trade businesses and the terminal’s roles. If there are nearby Northeastern boundary terminals, how to divide their roles from a regional truck terminal or choose to assemble them into a big logistic center. When we could have up to four terminals, the model chooses Khon Kaen, Ubon Ratchathani, Prachin Buri, and Udon Thani to the solution. Nonetheless, the transportation cost still decreases but does not significantly as much as when locating two and three terminals. Figure 45 shows costs
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for a year when p truck terminals are open. The result shows that with three terminals, the total cost is the lowest point while transportation cost significantly decrease until having three terminals. After that, the total cost turns back to increase.
Northeast and East Regions 1,400
1,200
1,000
800 Investment cost 600 Transportation cost
400 Total cost Cost (Million THB) (Million Cost 200
- 1 2 3 4 5 Number of located p truck terminal Figure 45 Costs of Northeast and East regions when located p terminals (weekly data pattern)
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p = 1 p = 2 p = 3
p = 4 p = 5 Figure 46 Optimal solution in Northeast and East regions when located p terminals (weekly data pattern)
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2.3 South region
In the South region, the topography of the region is the long narrow shape. Then, the main highway, from Bangkok, passes though almost all of the provinces in the region. There are two main economic provinces, and both are chosen as the only two candidate provinces in this region.
With one terminal, the result shows that Surat Thani is the best option because of its location advantage. From Surat Thani, it is easier to access other provinces on the left-hand side of the region outside the main road. When there are two terminals, the total costs increase, while transportation costs reduction is minimal. The second terminal location is on the right side of the region that a delivery network is similar to located one terminal. However, the provinces on the left-hand side of the region do not have enough high economics to be chosen as candidate provinces. Figure 47 shows costs for a year when p truck terminals are open. The result shows that with one terminal at Surat Thani, the total cost and transportation cost are the lowest point.
South Region 1,000 900 800 700 600 Investment cost 500 Transportation cost 400 Total cost
Cost (Million THB) (Million Cost 300 200 100 - 1 2 Number of located p truck terminal Figure 47 Costs of South region when located p terminals (weekly data pattern)
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p = 1 p = 2 Figure 48 Optimal solution in South region when located p terminals (weekly data pattern)
3. Annual freight demand results
There are 21 years of data inputted in the mathematical model, one year from current data and 20 years from the forecast by the growth rate equation. The investment cost includes land price, construction cost, start-up cost, operation cost, and maintenance cost. The size of all candidate truck terminals is assumed to be S size, two platform buildings. The candidate facility location is still the same as the previous method.
3.1 North and Central regions
In the experiment, located two terminals, in the southern and northern of the regions sprawling along the main highway, give the optimal result as same as the weekly freight demand model. However, the selected location is different. When only one terminal is allowed, the model selected Lamphun, the province next to Chiang
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Mai, as the main location due to road network accessibility that easier to manage huge freight volume shipping across the regions and commodity volume that is high in the northern part. When there are two terminals, the model decides to still put truck terminals in Lamphun to feed high demands in the North instead of Chiang Mai and in Lopburi to support the Central region. The total cost and transportation are sharply decreased. The number of allowed located facilities is varied until two, then, the total costs turn to increase, and the transportation cost does not significantly decrease any longer. When we could have up to three terminals, the model chooses Lamphun, Phitsanulok, and Lopburi as the locations of the facilities. When four and five terminals are allowed, Chiang Mai, Lampang, Phitsanulok, and Lopburi are selected when four terminals and Nakhon Sawan is added more when five terminals. Figure 49 shows costs for 21 years when p truck terminals are opened. The result shows that with two terminals, the total cost is the lowest point while transportation cost dramatically decreases when having two terminals and gradually decrease after having more truck terminals truck terminals.
North and Central Regions 50,000 45,000 40,000 35,000 30,000 25,000 Investment cost Transportation cost 20,000 Total cost
Cost (Million THB) (Million Cost 15,000 10,000 5,000 0 1 2 3 4 5 6 Number of located p truck terminal Figure 49 Costs of North and Central regions when located p terminals (annual data pattern)
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p = 1 p = 2 p = 3
p = 4 p = 5 p = 6 Figure 50 Optimal solution in North and Central regions when located p terminals (annual data pattern)
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3.2 Northeast and East regions
In the Northeast and East regions, having located three terminals gives the optimal result as same as the previous result. The selected provinces both are the same. However, there are some different when located in the other number of facilities. When only one terminal is allowed, the model chooses Khon Kaen, as same as the weekly freight demand model although demands are greater. With two terminals allowed, Nakhon Ratchasima and Udon Thani are chosen instead of Khon Kaen and Ubon Ratchathani, the result from the weekly freight demand model. Using annual freight demands, due to higher demands, Nakhon Ratchasima’s location is better to support the bottom of the regions as the northeastern roads start at this province. While Udon Thani supports the northern part of the regions. When three terminals are allowed, Nakhon Ratchasima, Ubon Ratchathani, and Udon Thani are chosen as same as the previous result. Ubon Ratchathani has an important role to support high freight transportation in its own province and eastern provinces. The lowest total costs are also found when locating the three terminals as well. The transportation cost still significantly decreases from locating two and three truck terminals. After located three terminals, the transportation cost does not sharply decrease. When we could have up to four terminals, the model chooses Khon Kaen, Ubon Ratchathani, Prachin Buri, and Udon Thani to the solution and Nakhon Ratchasima is added more when all truck terminals, five, are located.
According to the results, no provinces in the East region are selected before the total cost turning up. The cities with the highest demands in the East region are Chon Buri and Rayong which are the buffer area. However, the main destinations in these two provinces are a port city and many industrial zones. Therefore, a truck terminal does not require in this delivery behavior. Figure 51 shows costs for 31 years when p truck terminals are open. The result shows that with three terminals, the total cost is the lowest point while transportation cost significantly decrease until having three terminals. After that, the transportation cost is no longer sharply decreased.
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Northeast and East Regions 35,000
30,000
25,000
20,000 Investment cost 15,000 Transportation cost Total cost
Cost (Million THB) (Million Cost 10,000
5,000
0 1 2 3 4 5 Number of located p truck terminal
Figure 51 Costs in Northeast and East regions when located p terminals (annual data)
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p = 1 p = 2 p = 3
p = 4 p = 5 Figure 52 Optimal solution in Northeast and East regions when located p terminals (annual data pattern)
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3.3 South Region
In the South region, all results are the same as the previous experiment. With one terminal, the model still suggests that Surat Thani is the best option because of its location advantage. that easier to access other provinces. When two terminals are located, the total costs gradually increase and transportation cost reduction only 5% is also minimal following in Figure 53.
South Region 25,000
20,000
15,000 Investment cost 10,000 Transportation cost Total cost Cost (Million THB) Cost(Million 5,000
0 1 2 Number of located p truck terminal Figure 53 Costs in South region when located p terminals (annual data)
p = 1 p = 2 Figure 54 Optimal solution in South region when located p terminals (annual data pattern)
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3.4 Upper part of the country
In the previous experiment, there are divided the country into three parts; North and central regions, Northeast and East regions, and South region. In this section, the North and Central regions and Northeast and East regions are combined. The total number of the candidate truck terminal changes to 11 locations before running the model to find the optimal result.
When two terminals are located, the model chooses Lamphun and Khon Kaen, one in the western part and another one in the eastern part, for the optimal locations. When three terminals, Lopburi is added to the result for supporting the lower part. When the model changes to four, Lamphun is selected to support shipping for the North region, Lopburi for the central and left-side of the Northeast regions, Ubon Ratchathani for the East and right-side of the Northeast regions, and Udon Thani for the up-side of the Northeast region. When five truck terminals are located, the number of the optimal results in the North and Central regions plus the Northeast and East regions are definitely same. However, the total cost is different showing in Figure 56. The total cost of combing the upper area is lower, around 300 million THB for 21 years delivery. However, with six terminals allowed, the model selected Chiang Mai, Phitsanulok, Lopburi, Nakhon Ratchasima, Ubon Ratchathani, and Udon Thani for the optimal result and gave the lowest point of the total cost showing in Figure 55 and 56. Nevertheless, the difference of the total cost between locating five and six truck terminals gives slightly lower, only 95 million THB for 21 years.
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Upper part of Thailand 80,000
70,000
60,000
50,000
40,000 Investment cost Transportation cost 30,000 Total cost Cost (Million THB) Cost(Million 20,000
10,000
0 2 3 4 5 6 7 8 9 Number of located p truck terminal Figure 55 Costs in the upper part of Thailand when located p terminals (annual data)
Comparing optimal results from different methods 60,000
50,000
40,000
30,000 Investment cost Transportation cost 20,000
Total cost Cost (Million THB) (Million Cost
10,000
0 NNE p=5 NNE p=6 sum of N p=2 and NE p=3 Number of located p truck terminal Figure 56 Comparing optimal results from different methods
Note: NNE = results from the upper part of the country (North, Central, Northeast, and East regions) N = result from the North and Central regions NE = result from the Northeast and East regions
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4. Truck terminal capacity
In the weekly freight demand results and annual freight demand results, the construction cost was assumed for an S size platform terminal. However, the numbers of commodities flowing through each terminal are different. This section varied sizes of a platform terminal following commodity volumes and ran the mathematical model to confirm the optimal location. Scenarios of situations when changing the number of platform terminals and locations are presented.
4.1 North and Central regions
The optimal result shows that locating a truck terminal at Lamphun and Lopburi would give the best decision for the regions. The number of demands passing through each truck terminal is different depended on serviced provinces. Table 10 shows demand volumes passing Lamphun and Lopburi of current data and the next 20 years.
Table 10 Demand volumes in the North and Central regions’ truck terminals Demand volumes (ton per day) Year Lamphun Lopburi Current 2,280 550 Next 20 years 4,720 1,360
According to Table 7, The size of Lamphun truck terminals is proper an XL size in the next 20 years while Lopburi truck terminal should be an M size. Then, the scenarios following the future demands are shown below; • Scenario 1: The size of Chiang Mai and Lamphun are fixed as an XL size and one truck terminal is located there. Another one is located at Lopburi. • Scenario 2: There are fixed three truck terminals located at Lamphun, Phisanulok, and Lopburi. • Scenario 3: Lamphun in scenario 2 is changed to Chiang Mai.
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After running the model, scenario 1 selected to locate an XL size truck terminal at Lamphun and another M size one at Lopburi. Scenario 2 and 3 still shipped huge demand volumes to Lamphun and Chiang Mai resulted in an XL size truck terminal regardless of located three truck terminals already. The sizes of Phisanulok and Lopburi were also still an M size. Then, the optimal result was scenario 1, located truck terminals at Lamphun and Lopburi. However, the number of a truck terminal at Lamphun may consider dividing into two truck terminals due to the huge size of the truck terminal.
4.2 Northeast and East regions
Locating three truck terminals at Nakhon Ratchasima, Ubon Ratchathani, and Udon Thani gave the optimal result for the regions. The number of demands passing through each truck terminal is different depended on serviced provinces. Table 11 shows demand volumes passing Nakhon Ratchasima, Ubon Ratchathani, and Udon Thani of current data and the next 20 years.
Table 11 Demand volumes in the Northeast and East regions’ truck terminals Demand volumes (ton per day) Year Nakhon Ubon Ratchathani Udon Thani Ratchasima Current 500 250 1,100 Next 20 years 950 500 2,300
According to Table 7, The size of Nakhon Ratchasima, Ubon Ratchathani, and Udon Thani truck terminals are proper with an M size, S size, and M size in the next 20 year, respectively. Then, the scenarios following the future demands are shown below; • Scenario 1: Locate two truck terminals which one located at Khon Kaen with an L size. • Scenario 2: Located two truck terminals at Nakhon Ratchasima and Udon Thani. • Scenario 3: Located three truck terminals which one located at Khon Kaen.
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• Scenario 4: Located three truck terminals at Nakhon Ratchasima, Ubon Ratchathani, and Udon Thani.
When there are located two tuck terminals, one truck terminal is an L size and another one is a smaller size. In scenarios 1 and 2, Khon Kaen and Udon Thani truck terminals resulted in an L size and another located truck terminal was an M size. When changed to scenarios 3 and 4, having three truck terminals, the L size truck terminals, Khon Kaen and Udon Thani, reduced the size to the M size capacity and demand volumes were distributed smoothly. Among the scenarios, it was clear that scenario 4 gave the best solution with the lowest total cost and transportation cost among others with an M size of Nakhon Ratchasima and Udon Thani truck terminal and S size of Ubon Ratchathani truck terminal. Scenario 3 offered low investment cost (construction cost), however, the transportation cost was high.
4.3 South region
In the South region, locating one truck terminals at Surat Thani provided was the best solution. The number of demands passing through the truck terminal shows in Table 12. A number of demands to the southern part showed that having an L size truck terminal would support all shipped volumes there.
Table 12 Demand volumes in the South region’s truck terminal Demand volumes (ton per day) Year Surat Thani Current 1,120 Next 20 years 2,680
4.4 Upper part of the country
When the North and Central regions and the Northeast and East regions are combined, the commodity flows could flow smoothly across the country. The previous experiment suggested locating five to six truck terminals in the upper part
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area to meet the optimal results. Table 13 shows demand volumes passing though the selected truck terminals.
Table 13 Demand volumes in the upper part’s truck terminals Demand volumes (ton per day) Year Lamphun Lopburi Nakhon Ubon Udon Ratchasima Ratchathani Thani Current 2,030 620 430 300 980 Next 20 years 4,690 1,510 820 610 2,160
According to Table 7, The size of Chiang Mai and Lamphun truck terminals were set with an XL size. Lopburi, Nakhon Ratchasima, and Udon Thani were for an M size. And the left, Ubon Ratchathani, suited to an S size. Then, the scenarios following the future demands are shown below; • Scenario 1: Locate five truck terminals at Lamphun, Lopburi, Nakhon Ratchasima, Ubon Ratchathani, and Udon Thani. • Scenario 2: Lamphun in scenario 1 is changed to Chiang Mai. • Scenario 3: Locate six truck terminals at Chiang Mai, Phitsanulok, Lopburi, Nakhon Ratchasima, Ubon Ratchathani, and Udon Thani.
When located five truck terminals, scenario 1 gave a little lower total cost than scenario 2 due to land price in Chiang Mai. This result still highlighted that the location to construct the truck terminal should be between both cities as demand volume in Chiang Mai increased continuously. When one more truck terminal location was added, to be sixth truck terminals, the total cost rose up contrasting with the previous experiment that construction costs in all located truck terminals were same. Then, scenario 1 gave the optimal solution.
According to all the optimal solutions in each separated region, the selected truck terminal locations in the North and Central regions, scenario 1, and in the Northeast and East regions, scenario 4, gave the same selected truck terminal locations as in the included upper part of Thailand, scenario 1. This highlighted that located truck terminals in these potential locations could affect logistic activities of
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Thailand significantly. Figure 57 shows the conclusion of the lowest costs of each separated region. The sum costs of scenario 1 of the North and Central regions and scenario 4 of the Northeast and East regions gave almost same total cost, a little lower, compared to scenario 1 of the included upper part of Thailand. But scenario 1 of the included upper part of Thailand gave lower cost in transportation. Although the selected truck terminal locations were the same, however, the fright flow to each truck terminal was different and resulted in the different in the transportation cost and total cost.
Comparing optimal results of scenarios 70,000
60,000
50,000
40,000
Transportation cost 30,000 Investment cost
Cost (Million THB) Cost(Million 20,000
10,000
0 N s.1 NE s. 4 South NNE s.3 Scenarios
Figure 57 Comparing optimal results from different scenarios
Note: N = result from the North and Central regions NE = result from the Northeast and East regions NNE = results from the upper part of the country (North, Central, Northeast, and East regions) s. = scenario
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The conclusion table shows in Table 14. The size and main destinations of the selected potential truck terminal are descriptive to point out the service area.
Table 14 Conclusion of selected truck terminal location and size
Selected truck terminal Size Service area Lamphun XL North region Lopburi M Upper central region Nakhon Ratchasima M East region and southern part of Northeast region Ubon Ratchathani S Eastern part of Northeast region Udon Thani M Northern part of Northeast region
5. Facility functions
A different facility function between a public and a private truck terminal is one of an essential issue. In current the Bangkok truck terminals, there are a few types of buildings; a platform building, a warehouse, and, only in the Phutthamonthon truck terminal, a small platform with warehouse building. All provided buildings are an open space with a described specific function but no facilities.
In the platform buildings, the buildings were designed to support receiving and shipping activities by having a high level of the building's floor. The raised floor can help in load/unload product processes from a vehicle ergonomically. Each renting space has been separated by pillars. Normally, most of all activities excluding storage happen in these buildings. However, with limited space, there have to be well managed in preparation for receiving and shipping processes and a floor stack. In some carriers that rent many spaces or all the platform building, there are installed racks to storage their goods and all activities could conduct at there. Another one problem that there is limited provided office space in the same building. Then, a document problem is one of an interesting issue. While in a private company, only
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one company owner manages everything. There is an investment in suitable design infrastructures that support for receiving, put away, picking, and shipping activities.
Another one of big difference is a warehouse facility that the current Bangkok terminals have a rack for a storage activity. The storage rack is a basic type for products on pallet storage with using human workload. One of important problems here is the warehouse space is sharing space with other renters unless you rent all the areas. Then, damage or lost products could happen for the sharing warehouse without an unknown rogue.
To cope with these problems, there are the new buildings, small platform with warehouse buildings, for an optional in a small-medium carrier market. The first floor is an open space and the second floor is designed for an office. The space for one blog is smaller than the platform building, however, this rental space provides more privacy by a lockable door. With the small space, nevertheless, big size vehicles do not suitable for this building as a limited space size and there is no raised floor to support load/unload activities. Then, a chute or a forklift truck is required.
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Chapter 5 Conclusion
1. Conclusion
A truck terminal can lead to prerequisite efficient and cost-effective freight movement to a region’s economic viability, growth, prosperity, and livability. An inappropriate location for the truck terminal has significant implications for the freight transportation system and the efficiency at large. A comprehensive understanding of the facility location models would benefit transportation infrastructure planning decisions in terms of transportation infrastructure management. Therefore, there were truck terminal studies in Thailand starting since 1988 for the Bangkok truck terminal construction. The previous feasibility studies mainly considered in the qualitative term or weighted the individual criteria then find the sum scores to select the location. There are no solid methodologies or criteria to choose an appropriate truck terminal location yet. The capacity size of a truck terminal has not mentioned in the academic methodologies. In addition, the costs in the feasibility study are also not discussed or deficient compared to the current situation.
This study has extended the study of truck terminal location by demonstrated a new variant of the median location problem with fixed supply-demand pairs. The goal of the model is to determine the potential location of p regional truck terminals on a network, where products are transferred from line-haul vehicles to smaller vehicles. The objective function minimizes total facility location and transportation costs which give a linear cost-distance relationship. The buffer area has indicated in some provinces to avoid a weight-distance from provinces nearby Bangkok and ensure that the regional truck terminal’s roles promote delivery outside the buffer area.
According to road networks and the country’s topography, the experiments conducted into three regions of Thailand following different road networks and
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topography. The varying and non-varying in construction truck terminal cost and capacity of truck terminals are presented. In the North and Central regions, the selected provinces locate in the northern and southern regions sprawling along the main highway. Chiang Mai where the demand volume shipped the most is selected to locate a truck terminal there when using short-term data. When the forecast data of freight demand is used, the result shifts from Chiang Mai to the next province, Lamphun, because of road network accessibility that easier to distribute huge freight volumes to other areas. The size of the truck terminal is classified into an XL size, the biggest truck terminal, and able to support transferred products 4,800 ton per day. Another selected location is at Lopburi where is in the central region of Thailand. Lopburi also has an important role in logistic activities that supports to share freight distribution in the central and the beginning of the North region with capacity up to 2,400 ton per day. These locations highlight the impact of road network accessibility and demand-weight distances in the facility location problem.
The result also has the same direction in the Northeast and East regions. Both weekly and annual freight demand models result the same locations. The first location is Udon Thani located at the almost northernmost location of the Northeast region. This truck terminal has a significant role to subsidize huge demand volumes in the northern and northeastern area of the regions that the capacity size of terminal could reach 2,400 ton per day. The other selected location is at Nakhon Ratchasima, the gate province of the Northeast region, which is classified to have the same capacity as Udon Thani. This truck terminal supports freight distribution in the East region and southern part of Northeast region. Another truck terminal is located at Ubon Ratchathani, the easternmost province of the Northeast region. The truck terminal supports commodity volumes in nearby area with capacity 600 ton per day. All the optimal provinces in the Northeast and East regions are the biggest economic provinces that constructed a truck terminal at there can reduce demand-weight distances and boost the economy in the regions.
The South region is the easiest one to analyze as the shape of the region is the long shape. Then, having one truck terminal whereas using the weekly or annual data
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gives the optimal result. Surat Thani is selected to be the most potential province as the location is in the middle and nearby many economic cities. Then, we can conclude that in order to select a potential truck terminal location, road network accessibility, nearby the main highway that is expedient to access other cities, and high freight volumes are both efficient factors in the location problem. Long-term data should be used to predict transportation in the future as well due to different sizes of data could give different answers.
Furthermore, to clearly suggest for understanding the development of freight transportation in Thailand, the proposed capacity of a truck terminal also conducted from the same location problem model. Simplified commodity flow between Bangkok truck terminals to the selected truck terminals and destination provinces are presented to roughly see shipped demand volumes. The selected truck terminal location results have fluctuated from, one significant factor, the economic growth in nearby provinces. Hence, the trends of development and economic policies would influence the results and movement of the truck terminal location and shipping business dramatically.
2. Recommendation for further study
This study focuses on a large-scale domestic shipment of the entire country. The results present the potential provinces to locate a truck terminal for enhancing freight transport efficiency. In the next research, a more specific location to construct a truck terminal has played an important role in the success truck terminal and traffic level in the nearby area. In the Khlong Luang truck terminal, the terminal has faced a low usage from carriers although the truck terminal located in the good position, convenient to access main highways that linked to other parts of the country. However, the truck terminal accessibility is the problem because the direct access road passes through the residence area that has a terrible level of traffic jams while another access road from the ring road does not meet convenient usage. Then, nearby
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traffic and accessibility in the proposed truck terminal locations should deeply examine to find the most suitable place.
The costs; land price, construction cost, operation cost maintenance cost until transportation cost in this study, have been presented approximately by comparing from other feasibility studies and other terminal schemes and have converted to utilize in this study affairs. The shipping users were also assumed from all transportation in the Bangkok truck terminal regardless of other origins. This study provides strategic planning for the truck terminal role in Thailand. However, a further feasibility study should conduct to find suitable functions, delivery behavior of local areas, and financial operation of a truck terminal.
Another recommendation is the relationship between the truck terminals, in this study, for domestic delivery and the boundary truck terminals for commodity shipping to neighborhood countries. The domestic truck terminal can be used as a hub for sortation and consolidation products in the nearby area before shipping to the boundary truck terminals. This will affect the capacity and size of the truck terminals that in this study only focused on domestic shipping.
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LITERATURE CITED
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APPENDICES
Truck terminal development document
Figure 58 A simplify layout of the truck terminal study in 2008 Source: Department of Land Transport (2015)
103
4 4 4 4.75 4.75 4.25 3.75 Score SEZ policySEZ policySEZ policySEZ policySEZ SEZ policySEZ policySEZ policySEZ policySEZ policySEZ policySEZ
------√ √ √ √ √ The The number 1 city policy
------√ √ √ √ √ √ √ √ √ √ Zone Special Economic
- - - - √ √ √ √ √ √ √ √ √ √ √ √ √ Railway Network Accessibility
8,185 4,087 2,278 2,906 1,765 3,317 2,582 4,827 3,145 9,716 13,013 33,233 13,831 14,991 45,598 16,123 11,194 Flow Freight (ton/day)
6
17 15 6.3 9.8 13.5 17.7 18.8 17.2 19.1 12.5 17.1 14.3 26.4 29.4 23.8 12.2 ppl)
4 Factory/ Population (per 10
98,387 69,127 70,979 66,589 54,897 58,618 71,556 51,065 91,641 52,938 101,098 438,128 145,643 165,716 104,170 108,767 159,753 GPP/ Population
476,167 842,882 514,943 766,145 344,302 550,937 224,010 532,353 710,860 2,610,164 1,666,888 1,836,523 1,031,812 1,541,843 1,204,660 1,781,655 1,389,890 Population
e methodology to selected potential provinces in the feasibility and strategy study on the project of the economic zone thethe project of economic study on the feasibility and strategy provincespotential to selected in e methodology Province Th
15 Nakhon Ratchasima Chiang Mai Ubon Ratchathani Surat Thani Nakhon Si Thammarat Prachin Buri Kanchanaburi Chiang Rai Nong Khai Narathiwat Mukdahan Sa Kaeo Trat Tak Nakhon Phanom Khon Kaen Songkhla
9 5 6 7 8 1 2 3 4 17 13 14 15 16 10 11 12 No.
Table Table 104
3 3 3 2.5 2.5 3.5 3.5 3.5 3.5 3.5 3.25 2.75 2.75 2.75 2.75 2.25 2.25 2.25 3.75 3.25 Score
------√ √ The The number 1 city policy
------Zone Special Economic
------√ √ √ √ √ √ √ √ √ √ √ √ √ √ Railway Railway Network Accessibility
3,812 5,422 6,313 6,272 6,315 4,389 6,635 7,188 6,355 4,758 1,864 5,487 7,295 3,574 4,725 2,926 6,660 15,449 12,814 28,836 Flow Freight (ton/day)
8.4 9.5 12.5 10.1 26.7 16.1 15.5 10.5 22.1 23.7 12.6 21.1 10.6 42.2 14.4 12.3 12.9 13.9 13.5 13.8 ppl)
4 Factory/ Population (per 10
45,808 60,998 81,506 44,549 53,899 95,510 67,732 42,291 150,289 152,085 118,372 100,180 102,354 114,255 179,400 129,686 152,674 321,291 120,107 192,464 GPP/ Population
369,522 678,838 471,087 524,260 450,890 728,631 636,043 757,970 754,862 405,268 520,271 850,162 456,074 848,066 856,376 1,462,028 1,388,194 1,573,438 1,563,964 1,073,142 Population
uri Province b
Phuket Pattani Phetchaburi Si Sa Ket Chanthaburi Surin Krabi Phet Kamphaeng Trang Lop Lampang Lamphun Prachuap Khiri Khan Ratchaburi Buri Ram Phrae Suphan Buri Udon Thani Phitsanulok Nakhon Sawan
37 32 33 34 35 36 27 28 29 30 31 23 24 25 26 19 20 21 22 18 No. 105
2 2 2 2 2 1.5 1.5 1.5 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 1.75 2.25 1.75 1.75 Score
------The The number 1 city policy
------Zone Special Economic
------√ Railway Railway Network Accessibility
2,059 3,245 3,014 3,027 5,080 3,362 3,372 3,862 3,056 3,282 1,708 3,559 2,757 2,375 4,798 2,765 2,602 3,837 2,399 1,775 Flow Freight (ton/day)
6.8 9.7 9.2 17.1 15.6 17.7 26.2 25.9 20.1 21.9 13.4 15.6 15.9 13.2 11.3 17.5 16.1 13.8 10.4 24.7 ppl)
4 Factory/ Population (per 10
44,859 96,974 80,485 50,475 49,178 40,586 49,569 46,729 66,951 97,157 71,049 64,111 73,720 39,429 102,785 123,327 162,050 110,963 121,842 134,059 GPP/ Population
174,776 632,205 212,690 602,713 507,137 540,383 506,138 309,793 955,644 984,030 332,769 518,021 548,855 994,397 460,995 498,294 259,420 1,308,958 1,134,322 1,135,723 Population
Province
Ranong Loei Sing Buri Sukhothai LamNong Bua Phu Yasothon Yala Satun Maha Sarakham Roi Et Sakon Nakhon Kalasin Chai Nat Chaiyaphum Phatthalung Phichit Phetchabun Uttaradit Chumphon Phangnga
57 53 54 55 56 48 49 50 51 52 43 44 45 46 47 39 40 41 42 38
No.
106
1 1 1.5 1.5 consider consider consider consider consider consider consider consider consider consider consider consider consider 1.25 1.25 1.25
Score t t t t t t t t t t t t t No No No No No No No No No No No No No
------√ √ √ √ √ The The number 1 city policy
------Zone Special Economic
------√ √ √ √ √ √ √ √ √ Railway Railway Network Accessibility
927 2,423 1,980 2,177 1,461 2,184 1,139 1,702 1,808 82,092 60,302 22,561 54,012 35,110 26,663 19,147 47,430 53,967 61,605 Flow 230,619 Freight (ton/day)
29 38 8.7 9.7 7.7 4.5 66.3 39.2 12.4 13.6 17.3 10.5 11.3 32.9 27.6 31.2 19.8 31.4 29.5 ppl)
110.8 4 Factory/ Population (per 10
86,522 91,806 92,919 84,218 51,424 62,573 36,931 42,377 36,739 559,156 476,966 472,205 320,364 648,217 155,019 246,136 288,230 614,875 411,366 GPP/ 1,238,426 Population
882,184 519,457 797,970 661,220 256,085 194,116 486,744 374,698 416,236 246,549 690,226 629,216 283,732 329,536 477,912 1,053,158 1,241,610 1,390,354 5,686,252 1,156,271 Population
Province
Nakhon Pathom Pathum Thani Samut Sakhon Phra Nakhon Si Ayutthaya Samut Prakan Rayong Nakhon Nayok Samut Songkhram Phayao Amnat Charoen Bueng Kan Mae Hong Son Chon Buri Chachoengsao Saraburi Bangkok Nonthaburi Ang Thong Uthai Thani Nan
77 73 74 75 76 68 69 70 71 72 63 64 65 66 67 59 60 61 62 58
No.
107
Survey document
Figure 59 Survey form
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Coordination nodes
Table 16 Origin node location Phutthamonthon Khlong Luang Rom Klao truck terminal truck terminal truck terminal Location 18, Moo 8, 133 Moo 1, Kanchanaphisek Rd, 14, ICD Rd., Baromrajchonnee Rd, Khlong Si, Khlong Sam Prawet, Bang Toei, Khlong Luang Lat Krabang District, Sam Phran District, District, Pathum Thani Bangkok 10520 Nakhon Pathom 73210 12120 Latitude, 13.789543,100.287651 14.080948,100.691899 13.734019,100.760416 Longitude
Table 17 Destination node location
Province Region Latitude Longitude Bangkok Capital 13.907474 100.595749 Chiang Rai North 19.956911 99.852550 Chiang Mai North 18.785217 99.028634 Nan North 18.754075 100.760009 Phayao North 19.155595 99.913726 Phrae North 18.117480 100.151397 Mae Hong Son North 19.300853 97.964140 Lampang North 18.275989 99.483880 Lamphun North 18.593422 99.042441 Uttaradit North 17.607880 100.081420 Krabi South 8.117918 98.872181 Chumphon South 10.504631 99.133157 Trang South 7.536954 99.620053 Nakhon Si Thammarat South 8.434843 99.927854 Narathiwat South 6.020495 101.960792
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Province Region Latitude Longitude Pattani South 6.867450 101.258624 Phangnga South 8.869769 98.352085 Phatthalung South 7.585760 100.055107 Phuket South 7.894532 98.363660 Yala South 6.558923 101.288326 Ranong South 9.931734 98.634359 Songkhla South 6.989856 100.486078 Satun South 6.671395 100.074179 Surat Thani South 9.121380 99.338139 Kamphaeng Phet Center 16.451388 99.537112 Chai Nat Center 15.182885 100.133792 Nakhon Nayok Center 14.226463 101.234067 Nakhon Sawan Center 15.695575 100.122075 Phra Nakhon Si Ayutthaya Center 14.319604 100.635003 Phitsanulok Center 16.792636 100.232185 Phetchabun Center 16.388839 101.127936 Lopburi Center 14.782624 100.686639 Samut Songkhram Center 13.401753 100.005766 Sing Buri Center 14.888069 100.428967 Sukhothai Center 17.014945 99.846279 Suphan Buri Center 14.482965 100.130558 Saraburi Center 14.551322 100.963114 Ang Thong Center 14.581221 100.457995 Uthai Thani Center 15.385964 99.844608 Kanchanaburi West 13.980987 99.584020 Tak West 16.724724 98.588138 Prachuap Khiri Khan West 12.423639 99.920030 Phetchaburi West 13.123930 99.888056 Ratchaburi West 13.541338 99.799983 Chanthaburi East 12.616611 102.143309
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Province Region Latitude Longitude Chachoengsao East 13.720022 101.042283 Chon Buri East 13.095938 100.896533 Trat East 12.259111 102.516424 Prachin Buri East 13.987475 101.761873 Rayong East 12.695752 101.263869 Sa Kaeo East 13.809236 102.078851 Kalasin Northeast 16.453724 103.531287 Khon Kaen Northeast 16.405048 102.814226 Chaiyaphum Northeast 15.775818 102.027166 Nakhon Phanom Northeast 17.388225 104.767062 Nakhon Ratchasima Northeast 14.994084 102.099080 Bueng Kan Northeast 18.365629 103.641419 Buri Ram Northeast 14.974883 103.070440 Maha Sarakham Northeast 16.211522 103.274878 Mukdahan Northeast 16.541106 104.708278 Yasothon Northeast 15.772972 104.179037 Roi Et Northeast 16.058309 103.630645 Loei Northeast 17.470266 101.723512 Si Sa Ket Northeast 15.107056 104.370182 Sakon Nakhon Northeast 17.174891 104.128912 Surin Northeast 14.870406 103.512066 Nong Khai Northeast 17.835456 102.755922 Nong Bua Lam Phu Northeast 17.209844 102.435022 Amnat Charoen Northeast 15.881740 104.623965 Udon Thani Northeast 17.390968 102.816727 Ubon Ratchathani Northeast 15.268616 104.833145 Nakhon Pathom Perimeter 13.804872 100.008553 Nonthaburi Perimeter 13.828526 100.484144 Pathum Thani Perimeter 13.974452 100.616681 Samut Prakan Perimeter 13.540983 100.619578 Samut Sakhon Perimeter 13.569302 100.303127
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Table 18 Candidate truck terminal location
Province Area Latitude Longitude Khon Kaen Nam Phong-Kranuan Rd. (2039), 16.644774 102.801489 Nam Phong District Nakhon Yuthasart Rd. (331) 14.899525 101.891775 Ratchasima Chiang Mai Liap Thang Rotfai Rd., Saraphi 18.733091 99.032586 District Ubon Ratchathani Ubon Ratchathani Bypass Rd. (231), 15.180059 104.830258 Warin Chamrap District Surat Thani Si Yaek Lamae-Phunphin Rd. (4112), 9.148323 99.164784 Phunphin District Nakhon Si AH2 and Kantang Rd. (403), Thung 8.148693 99.680767 Thammarat Song District Prachin Buri Chachoengsao-Kabinburi Rd. (304), 13.939519 101.701426 Kabinburi District Udon Thani Udon Thani Ring Road 17.429522 102.809272 Phitsanulok Phitsanulok Bypass Rd. 16.756118 100.264131 Nakhon Sawan Phayuha Khiri-Noen Makok Rd. 15.462100 100.150106 (3008), Phayuha Khiri District Lopburi Lopburi bypass Rd. (366) 14.773486 100.69127 Lampang Lampang-Mae Tha Rd. (1037), 18.250862 99.494244 Mueang District Lamphun Superhighway Chiang Mai-Lampang 18.590235 99.042746 Rd. (11) and San Pa Fai-Sankampang Rd. (1147)
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Data manipulation
Table 19 Number of vehicles passing though Bangkok truck terminals with showing direction
Phutthamonthon Khlong Luang No. of No. of No. of No. of Vehicle Type Ratio Ratio Inbound Outbound Inbound Outbound In:Out In:Out Vehicle Vehicle Vehicle Vehicle Pickup 1.02 160 154 1.08 8,401 7,187 4-wheel truck 1.02 33 32 6-wheel truck 1.03 1,028 975 0.90 94 115 10-wheel truck 1.01 491 482 0.86 74 99 12-wheel truck 0.90 81 99 1.18 6 4 14-wheel 0.98 11 11 0.96 1 1 semitrailer 18-wheel 0.98 70 73 0.96 51 56 semitrailer 20-wheel 0.98 68 71 0.96 112 122 semitrailer > 20-wheel 0.98 15 16 0.96 0 1 semitrailer 14-wheel trailer 1.03 7 6 0.89 0 0 18-wheel trailer 1.03 4 4 0.89 0 0 20-wheel trailer 1.03 4 4 0.89 2 2 > 20-wheel 1.03 1 1 0.89 0 0 trailer Note: 1. Number of vehicles is average per week 2. NA means the data is not available due to limited from the survey data
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Table 20 Load capacity in each type of vehicles
Vehicle Type Load capacity (ton) Pickup 1 4-wheel truck 2 6-wheel truck 5 10-wheel truck 10 More than 10-wheel truck 20 Trailer 45 Semi-trailer 30
Table 21 Average shipping volume (ton) from the Phutthamonthon terminal
Consumer Construction Chemical Electronic Destination Other product material product part Chiang Mai 4432.16 0 0 0 259.86 Chiang Rai 1023.37 0 0 0 0 Phetchabun 844.62 0 0 0 0 Loei 530.38 0 0 0 0 Phrae 72.02 0 0 0 0 Krabi 616.15 0 0 0 0 Kalasin 128.17 0 0 0 0 Khon Kaen 894.42 0 0 0 0 Chumphon 87.69 0 0 0 0 Trang 191.92 0 0 0 0 Tak 257.12 0 0 0 0 Nakhon 877.79 0 0 0 0 Ratchasima Nakhon Si 688.85 0 0 0 0 Thammarat Nakhon Sawan 315.58 0 0 0 0 Nan 157.79 0 0 0 0 Bueng Kan 29.62 0 0 0 0
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Consumer Construction Chemical Electroni Destination Other product material product c part Buri Ram 600.10 0 0 0 0 Prachuap Khiri 87.69 0 0 0 0 Khan Prachin Buri 87.69 0 0 0 0 Phayao 196.73 0 0 0 0 Phatthalung 29.62 0 0 0 0 Phitsanulok 72.02 0 0 0 0 Phuket 434.90 0 0 0 0 Mukdahan 157.79 0 0 0 0 Yasothon 133.46 0 0 0 0 Yala 291.25 0 0 0 0 Roi Et 215.87 0 0 0 0 Ranong 128.17 0 0 0 0 Lampang 1074.52 0 0 0 0 Lamphun 2351.06 0 0 0 0 Si Sa Ket 390.58 0 0 0 0 Sakon Nakhon 349.33 0 0 0 0 Songkhla 430.77 0 0 0 0 Satun 58.46 0 0 0 0 Saraburi 29.62 0 0 0 0 Sukhothai 186.63 0 0 0 0 Surat Thani 1889.71 0 0 0 0 Surin 320.10 0 0 0 0 Nong Khai 944.42 0 0 0 0 Nong Bua Lam 29.62 0 0 0 0 Phu Amnat Charoen 29.62 0 0 0 0 Udon Thani 1565.96 0 0 0 0 Ubon Ratchathani 492.60 0 0 0 0
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Table 22 Average shipping volume (ton) from the Khlong Luang terminal
Consumer Construction Chemical Electronic Destination Other product material product part Chiang Mai 51.63 103.27 0 0 0 Loei 23.08 0 0 0 0 Kalasin 23.08 0 0 0 0 Kamphaeng Phet 67.50 0 0 0 0 Khon Kaen 210.77 26.35 0 0 0 Nakhon 146.79 0.00 0 24.46 0 Ratchasima Nakhon Sawan 0 55.38 0 0 0 Prachuap Khiri 14.87 0 0 0 0 Khan Maha Sarakham 23.08 0 0 0 0 Roi Et 33.58 0 0 0 0 Lopburi 14.16 14.16 0 0 0 Songkhla 0 147.84 0 29.57 0 Saraburi 196.65 884.92 65.55 0 0 Surat Thani 345.43 69.09 0 0 0 Surin 36.78 36.78 0 0 0 Udon Thani 6.06 0 0 0 0 Ubon 23.08 0 0 0 0 Ratchathani Phetchabun 33.00 11.00 0 0 0 Prachin Buri 34.77 0 0 0 0 Phetchaburi 4.13 0 0 0 0 Chanthaburi 50.45 25.22 0 0 0 Sa Kaeo 32.50 16.25 0 0 0 Chiang Rai 44.62 0 0 0 0 Tak 23.08 0 0 0 0 Bueng Kan 44.62 0 0 0 0 Phayao 23.08 0 0 0 0
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Consumer Construction Chemical Electronic Destination Other product material product part Lamphun 23.08 0 0 0 0 Sakon Nakhon 27.69 27.69 0 0 0 Nan 0 6.06 0 0 0 Yasothon 0 6.92 0 0 0 Phrae 32.31 0 0 0 0 Chaiyaphum 0 71.54 0 0 0 Chumphon 0 32.31 0 0 0 Trat 0 32.31 0 0 0 Phitsanulok 28.08 0 0 0 0
Table 23 Growth freight rate
Freight Flow Growth Freight rate (per year) No. Province (ton/day) year 1-5 year 6-10 year 11-25 1 Krabi 5,422 5.21% 4.81% 4.23% 2 Bangkok 230,619 5.08% 4.78% 4.31% 3 Kanchanaburi 8,185 3.97% 3.81% 3.52% 4 Kalasin 3,282 5.47% 5.11% 4.55% 5 Kamphaeng Phet 6,313 5.33% 4.94% 4.35% 6 Khon Kaen 14,991 4.79% 4.53% 4.10% 7 Chanthaburi 6,660 5.10% 4.73% 4.17% 8 Chachoengsao 35,110 6.56% 6.08% 5.35% 9 Chon Buri 74,012 6.36% 5.89% 5.18% 10 Chai Nat 1,708 4.16% 3.95% 3.60% 11 Chaiyaphum 3,559 5.28% 4.96% 4.44% 12 Chumphon 5,080 5.50% 5.15% 4.60% 13 Chiang Rai 4,087 4.60% 4.28% 3.78% 14 Chiang Mai 9,716 4.20% 3.93% 3.51% 15 Trang 6,272 5.11% 4.76% 4.23% 16 Trat 2,582 4.62% 4.32% 3.85% 17 Tak 4,827 4.67% 4.35% 3.86% 18 Nakhon Nayok 3,145 4.21% 3.94% 3.52% 19 Nakhon Pathom 22,561 5.64% 5.29% 4.74% 20 Nakhon Phanom 1,808 4.73% 4.41% 3.93%
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Freight Flow Growth Freight rate (per year) No. Province (ton/day) year 1-5 year 6-10 year 11-25 21 Nakhon Ratchasima 45,598 2.19% 2.17% 2.12% Nakhon Si 13,013 4.37% 4.10% 3.69% 22 Thammarat 23 Nakhon Sawan 6,635 4.85% 4.55% 4.09% 24 Nonthaburi 19,147 6.35% 5.91% 5.23% 25 Narathiwat 2,906 5.49% 5.08% 4.47% 26 Nan 1,461 4.16% 3.90% 3.49% 27 Bueng Kan 1,702 4.53% 4.22% 3.74% 28 Buri Ram 4,758 4.58% 4.28% 3.82% 29 Pathum Thani 47,430 5.72% 5.36% 4.78% 30 Prachuap Khiri Khan 6,355 5.34% 5.02% 4.51% 31 Prachin Buri 23,233 2.64% 2.61% 2.54% 32 Pattani 3,574 3.18% 3.06% 2.85% Phra Nakhon Si 33 61,605 6.32% 5.86% 5.16% Ayutthaya 34 Phayao 2,184 4.49% 4.18% 3.71% 35 Phangnga 3,362 4.54% 4.23% 3.74% 36 Phatthalung 2,757 5.06% 4.69% 4.14% 37 Phichit 2,375 4.72% 4.40% 3.92% 38 Phitsanulok 4,389 4.57% 4.30% 3.87% 39 Phetchaburi 4,725 5.49% 5.16% 4.62% 40 Phetchabun 4,798 4.36% 4.04% 3.55% 41 Phrae 1,864 3.88% 3.65% 3.29% 42 Phuket 7,295 4.76% 4.41% 3.89% 43 Maha Sarakham 3,372 4.75% 4.43% 3.94% 44 Mukdahan 1,765 5.32% 4.98% 4.45% 45 Mae Hong Son 927 3.31% 3.10% 2.78% 46 Yasothon 2,059 4.63% 4.33% 3.86% 47 Yala 3,245 5.28% 4.93% 4.39% 48 Roi Et 3,862 4.88% 4.54% 4.02% 49 Ranong 2,765 3.17% 3.02% 2.78% 50 Rayong 70,302 6.80% 6.31% 5.54% 51 Ratchaburi 28,836 4.36% 4.11% 3.70% 52 Lopburi 15,449 4.03% 3.83% 3.49% 53 Lampang 12,814 2.66% 2.54% 2.34% 54 Lamphun 7,188 6.59% 6.12% 5.39% 55 Loei 2,602 4.33% 4.01% 3.54% 56 Si Sa Ket 2,926 5.11% 4.76% 4.23%
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Freight Flow Growth Freight rate (per year) No. Province (ton/day) year 1-5 year 6-10 year 11-25 57 Sakon Nakhon 3,056 4.59% 4.30% 3.84% 58 Songkhla 13,831 4.72% 4.45% 4.02% 59 Satun 3,014 4.20% 3.98% 3.61% 60 Samut Prakan 82,092 6.53% 6.09% 5.39% 61 Samut Songkhram 2,423 4.91% 4.64% 4.19% 62 Samut Sakhon 53,967 6.93% 6.42% 5.63% 63 Sa Kaeo 3,317 5.26% 4.92% 4.39% 64 Saraburi 26,663 5.97% 5.56% 4.93% 65 Sing Buri 3,837 4.89% 4.59% 4.12% 66 Sukhothai 2,399 4.11% 3.86% 3.46% 67 Suphan Buri 5,487 4.75% 4.48% 4.06% 68 Surat Thani 11,194 5.23% 4.86% 4.29% 69 Surin 3,812 4.55% 4.26% 3.80% 70 Nong Khai 2,278 4.43% 4.13% 3.67% 71 Nong Bua Lam Phu 1,775 3.81% 3.57% 3.19% 72 Ang Thong 1,980 3.82% 3.64% 3.33% 73 Amnat Charoen 1,139 4.38% 4.11% 3.69% 74 Udon Thani 6,315 4.23% 3.98% 3.59% 75 Uttaradit 3,027 3.39% 3.23% 2.96% 76 Uthai Thani 2,177 3.99% 3.76% 3.38% 77 Ubon Ratchathani 14,123 1.64% 1.61% 1.54%
Note: Since year 30, the growth freight rates are assumed to equal 3.00% per year
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Costs
Table 24 Detail of land price in the candidate locations
Land Land Price Province Area Price (Million THB) (THB/wa) Khon Kaen Nam Phong-Kranuan Rd. 2,300 198.83 (2039), Nam Phong District Nakhon Yuthasart Rd. (331) 1,500 129.67 Ratchasima Chiang Mai Liap Thang Rotfai Rd., 6,500 561.91 Saraphi District Ubon Ratchathani Ubon Ratchathani Bypass 2,000 172.90 Rd. (231), Warin Chamrap District Surat Thani Si Yaek Lamae-Phunphin 1,500 129.67 Rd. (4112), Phunphin District Nakhon Si AH2 and Kantang Rd. 1,500 129.67 Thammarat (403), Thung Song District Prachin Buri Chachoengsao-Kabinburi 1,000 86.45 Rd. (304), Kabinburi District Udon Thani Udon Thani Ring Road 2,500 216.12 Phitsanulok Phitsanulok Bypass Rd. 1,000 86.45 Nakhon Sawan Phayuha Khiri-Noen 2,500 216.12 Makok Rd. (3008), Phayuha Khiri District Lopburi Lopburi bypass Rd. (366) 3,000 259.34 Lampang Lampang-Mae Tha Rd. 1,500 129.67 (1037), Mueang District Lamphun Superhighway Chiang 4,000 345.79 Mai-Lampang Rd. (11) and San Pa Fai-Sankampang Rd. (1147) Saraburi Mittraphap Rd., 5,000 432.24 Kaeng Khoi District
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Table 25 Detail of transportation cost
Source: Department of Land Transport (2017b)
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CURRICULUM VITAE
CURRICULUM VITAE
NAME Palita Meenapinan
DATE OF BIRTH 20 September 1994
BIRTH PLACE Bangkok, Thailand
ADDRESS 2/118 Soi Vibhavadi 25, Vibhavadi Rd., Luk Si, Bangkok, Thailand, 10210 EDUCATION M. Eng. (Sustainable Energy and Resources Engineering), Kasetsart University\ B. Eng. (Industrial Engineering), Chulalongkorn University PUBLICATION The 14th International Congress on Logistics and SCM Systems