A DESIGN OF TAPIOCA CHIP TRANSPORTATION MODEL IN THAILAND
1WITCHAYUT TIMABOOT, 2NANTHI SUTHIKARNNARUNAI
1,2Department of Logistics Engineering, School of Engineering, University of the Thai Chamber of Commerce, Bangkok Thailand E-mail: [email protected], 2 [email protected], [email protected]
Abstract- Among the current economic situation regarding logistic activities in Thailand, one of the most crucial factors to be priory considered is transportation which needs to be managed effectively with the lowest possible cost for the best profitability. This research studies about transportation problem of tapioca chip product from production plant to harbor port. The problem of high transportation cost arises in the current transportation system of tapioca chip product because the lack of the correct assignment of river port to the plant. Therefore, the prototype of the optimization model for transportation of the tapioca chip is recommended in this paper. Microsoft Excel with Solver software is used as tools in solving the model. The total transportation cost reveals from the model is THB 1,374,078,500. It gives the reduction of 8.54% compare to the current practice.
Keywords- transportation model, allocation model, optimization, tapioca chips.
I. INTRODUCTION
Thailand is the biggest cultivator and the major exporter of cassava in Asia. The major importer of cassava in the world is China. China imports cassava and use it as an input to produce ethanol and various kinds of product. Thailand locates in the tropical area, therefore, several kinds of agricultural plants can be planted, including cassava. The cassava has been grown in many areas throughout the country. Cassava is considered as one of the economic crop that can be produced many types of semi-product such as tapioca pellets, cassava powder, tapioca chip, flour and starch, etc. This research had been considered for tapioca chip that high volume export more than 56 % Fig. 2. Tapioca Production Plant in Thailand (circle dot); River of total cassava product. port along Chao Phraya River (star mark); Srichang Harbor Port (square shape) According to the supply chain of exported tapioca chip product which is shown in Fig. 1, the collected Currently, the logistics of the cassava from the field cassava from the cassava filed will be delivered to after harvesting to the destination river port are tapioca production plant and then to the customer controlled by the middleman or traders. Prices will be using multi-modal transportation. First, trucks will be agreed according to a contract but the transportation used in order to deliver cassava from the harvesting cost is paid by the farmer. From the observation and field to the tapioca production plants which are from the interviewing of the researcher, two major mostly located in different regions of Thailand as problems which cause higher transportation cost to shown as the circle dots in Fig. 2. After the the farmer are 1) the river ports which is assigned to production, the tapioca chip is delivery by truck to the the farmer are often far from the origin and 2) the river ports located along the Chao Phraya River or assigned destination river ports are occupied and the Bangpakong river as shown as the star marks in Fig. emergency change has been made or the truck must 2. The barges are then delivered tapioca chip to the wait at the port until it is available to be unloaded. Srichang harbor port as shown as the square shape in The researchers, therefore, aim to introduce the Fig. 2. Lastly, the shipment will be made to prototype of the transportation model of the river international customer by sea transportation. ports to match with the tapioca production plant in order to minimize the total transportation cost.
II. LITERATURE REVIEW
When explode “allocation” wording that research in Fig. 1. Supply chain of exported tapioca chip product to the exist literature usually find out objective of international market “location selection, port selection, hub & spoke
Proceedings of Academics World 5th International Conference, Paris, France, 11th October 2015, ISBN: 978-93-85832-08-6 65 A Design Of Tapioca Chip Transportation Model In Thailand decision, optimization of total cost that contain with Port many inventory cost concerning, how to make lot Nakornluang, Ayutthaya size- order size, purchase cost, EOQ ordering” which
Barge they use decision model by parameter of quantity loading Habour time windows lot size constraint, minimum ordering, Srichang Port (Port of loading) maximum of capacity which location, allocation Bangsai, Ayutthaya selection SPPTTWCC (Yohanes 2014) ANOVA test loading Barge Bangprakong, ,etc. They use programmes of CPLEX, Matlab, C++, Chacheongsao
Lingo, etc. for instance, to consider and make a Loading Barge decision in which the programmes are controlled by a Bangkok Port, Bangkok parameter, varied by desired coefficients. Barge loading
III. METHODOLOGY Fig.4. Transportation Network of Tapioca Chip from river port to harbor port 3.1 Scope of the Study In this prototype research, the scope of the study are The mathematical model represents the transportation as follows: model of the above network is shown in equation (1) 1) The origin of the products are from four tapioca – (7). The decision variables in the model are Qij and production plants which are located in Nakhon Qjk. Qij is the amount of tapioca chip transported Ratchasima, Kamphaeng Phet, Kanchanaburi and from plant i to river port j, while Qjk is the amount of Chaiyaphum Provinces. Each plant has its own tapioca chip transport from river port j to harbor port production capacity. k. Cij and Cjk are the transportation cost per ton-km 2) The destinations of the products are Srichang from plant i to river ports j and from river port j to Habor Port. harbor port k, consecutively. Parameter U is the 3) The river ports are Port of Nakhon Luang in amount of total tapioca chip in this case study. Ui is Ayutthaya Province, Port of Bangsai in Ayutthaya the amount of tapioca chip at plant i and Pj is the Province, Bangkok Port in Bangkok, and Port of capacity of each river port j. Bangpakong in Chacheangsao Province. Each port has its own capacity and delivery time table. Minimize total Cost (TC) Z ; 4) Road transportation is made from plant to river I J J K port. CijQij + CjkQjk (1) 5) Water way transportation using barges is made i1 j 1 j 1 k 1 from river port to harbor port. 6) Transportation cost from plant to river depends on Subject to the distance between port and plant. Quantity of I J delivery is also a factor affecting cost. Table Qij Qjk U;k (2) 7) Transportation cost from river port to harbor port is i1 j1 different from port to port and depend on quantity I of delivery. Qij Ui;ij (3) i 1 This research scope is to design the transportation J (4) model for tapioca chip from the production plant to Qij Pi;ij harbor port in order to minimize the total j 1 transportation cost. Fig. 3 and Fig. 4 represent the I network of the supply chain explained above. Qij Qjk;jk (5) i1 Qij 0 (6) Qjk 0 (7)
Equation (1) shows the objective function to minimize the total transportation cost which includes cost from plant to river port and from river port to harbor port. Equation (2) and (3) ensure that the shipment of each plant will be made while the capacity of each river port constraint is shown in equation (4). Equation (6) shows the conservation equation of the network where the input of the transshipment node which are the river ports equal to
Fig 3. Transportation Network of Tapioca Chip from plant to the output. Last two equations which are (6) and (7) river port are non-negativity variable constraints.
Proceedings of Academics World 5th International Conference, Paris, France, 11th October 2015, ISBN: 978-93-85832-08-6 66 A Design Of Tapioca Chip Transportation Model In Thailand Table 1 shows the transportation cost from each Table5: The assignment of the tapioca chip from each plant to origin to the river ports, while Table 2 transportation each river port cost from each river port to the harbor port. Table 3 shows the capacity of each river port. Table 4 shows the capacity of each tapioca chip production plant.
Table1: Transportation cost from plant to river port (THB/km-tonnage)
CONCLUSIONS
This research is a prototype model of transport tapioca chip from production plant to the harbor port. The current practice depends on the mandated of the middleman, while the transportation is on the farmer or the plant side. This model seeks to recommend the Table2: Transportation cost of each river port to harbor port model of how to assign ports to the plant in order to (THB/tonnage) minimize the transportation cost. The result reveals that total cost from this case study is THB 1,374,078,500. It gives the reduction of 8.54% compare to the previous practice. However, data used in this model is a collected and static data from a point of time period. The next research, the model should be expanded to handle the dynamic data from
the various time period. The model also should be Table3: Capacity of each river port (Ton) expanded to cover all area of the origin and destination in Thailand.
REFERENCES
[1]. Ngai Kheong Ng, Jianxin Jiao,“A domain-based reference model for the conceptualization of factory loading allocation problems in multi-site manufacturing supply chains” Technovation Volume 24, Issue 8, August 2004, Pages 631–642. Table4: Capacity of production plant (Ton) [2]. Nuno Monteiro, Rosaldo Rossetti, Pedro Campos, Zafeiris Kokkinogenis, “A Framework for a Multimodal Transportation Network: an Agent Based Model Approach” Transportation Research Procedia, Volume 4, 2014, Pages 213-227. [3]. Masahiro Ishii, Paul Tae-Woo Lee, Koichiro Tezuka, Young- Tae Chang, “A game theoretical analysis of port competition”, Transportation Research Part E: Logistics and Transportation Review, Volume 49, Issue 1, January 2013, Pages 92-106 IV. RESULT [4]. Elias Olivares-Benitez, RogerZ.Rı´os-Mercado, Jose´ Luis Gonza´ lez-Velarde, “A metaheuristic algorithm to solve the selection of transportation channels in supply chain design., This research uses solver software which is installed International Journal of Production Economics, Volume 145, in Microsoft Office Excel to solve this problem. The Issue 1, September 2013, Pages 161-172. answer to the problem is shown in Table 5. Product [5]. Yohanes Kristianto, Angappa Gunasekarn, Petri Helo, from plant Nakornratchasima has to be distributed to Yuqiuqe Hao, “A model of resilient supply chain network design: A two-stage programming with fuzzy shortest path.”, port Nakornluang-Ayutthaya, Bangprakong- Expert Systems with Applications, Volume 41, Issue 1, Chacheongsao and Bangkok Port, Bangkok while January 2014, Pages 39-49. product from plant Kumpangpet has to be distributed [6]. M. Jung “A Multi-Echelon And Multi-Indenture Repairable to port Bangsai-Ayutthaya only. Product from plant Item Queuing Model During Emergencies.”, Mathematical and Computer Modelling, Volume 12, Issue 7, 1989, Pages Kanjanaburi has to be distributed to Bangkok Port- 851-864. Bagkok and while product from plant chaiyaphumi to [7]. Behdad Masih-Tehrani, Susan H. Xu, Soundar Kumara, Nakornluang-Ayutthaya only. Consist primarily total Haijun Li, “A single-period analysis of a two-echelon cost from plant to river port is THB 977,018,000.00 inventory system [8]. -with dependent supply uncertainty.” Transportation with secondary total cost THB 279,600,000.00. The Research Part B: Methodological Volume 45, Issue 8, total amount of transportation cost is THB September 2011, Pages 1128–1151. 1,256,618,000.00.
Proceedings of Academics World 5th International Conference, Paris, France, 11th October 2015, ISBN: 978-93-85832-08-6 67 A Design Of Tapioca Chip Transportation Model In Thailand [9]. Yohanes Kristianto, Angappa Gunasekaran, Petri Helo, [19]. Deniz Özdemir, Enver Yücesan, Yale T. Herer, “Multi- Yuqiuqe Hao, “A model of resilient supply chain network location transshipment problem with capacitated design: A two-stage programming with fuzzy shortest path”, transportation”, European Journal of Operational Research, Expert Systems with Applications, Volume 41, Issue 1, Volume 175, Issue 1, 16 November 2006, Pages 602-621 January 2014, Pages 39-49. [20]. M. SteadieSeifi, N.P. Dellaert, W. Nuijten, T. Van Woensel, [10]. M. Jung, E.S. Lee, “A multi-echelon and multi-indenture R. Raoufi, “Multimodal freight transportation planning: A repairable item queuing model during emergencies”, literature review”, European Journal of Operational Research, Mathematical and Computer Modelling, Volume 12, Issue 7, Volume 233, Issue 1, 16 February 2014, Pages 1-15. 1989, Pages 851-864. [21]. Loo Hay Lee, Ek Peng Chew, Suyan Teng, Yankai Chen, [11]. Behdad Masih-Tehrani, Susan H. Xu, Soundar Kumara, “Multi-objective simulation-based evolutionary algorithm for Haijun Li, “A single-period analysis of a two-echelon an aircraft spare parts allocation problem”, European Journal inventory system with dependent supply uncertainty”, of Operational Research, Volume 189, Issue 2, 1 September Transportation Research Part B: Methodological, Volume 45, 2008, Pages 476-491. Issue 8, September 2011, Pages 1128-1151. [22]. Seyed Mohsen Mousavi, Najmeh Alikar, Seyed Taghi [12]. B. Kumar Sett, Biswajit Sarkar, A. Goswami, “A two- Akhavan Niaki, Ardeshir Bahreininejad, “Optimizing a warehouse inventory model with increasing demand and time location allocation-inventory problem in a two-echelon varying deterioration”, Scientia Iranica, Volume 19, Issue 6, supply chain network: A modified fruit fly optimization December 2012, Pages 1969-1977. algorithm”, Computers & Industrial Engineering, Volume 87, [13]. Moncer A. Hariga, Abdulrahman Al-Ahmari, “An integrated September 2015, Pages 543-560. retail space allocation and lot sizing models under vendor [23]. Mohammad Hossein Gorji, Mostafa Setak, Hossein Karimi, managed inventory and consignment stock arrangements”, “Optimizing inventory decisions in a two-level supply chain Computers & Industrial Engineering, Volume 64, Issue 1, with order quantity constraints”, Applied Mathematical January 2013, Pages 45-55. Modelling, Volume 38, Issue 3, 1 February 2014, Pages 814- [14]. Ruth Banomyong, Vinh V. Thai, Kum Fai Yuen, “Assessing 827. the National Logistics System of Vietnam”, The Asian [24]. Matt Bassett, Leslie Gardner, “Optimizing the design of Journal of Shipping and Logistics, Volume 31, Issue 1, global supply chains at Dow AgroScience”, Computers & March 2015, Pages 21-58. Chemical Engineering, Volume 34, Issue 2, 8 February 2010, [15]. X.T. Sun, S.H. Chung, Felix T.S. Chan, “Integrated Pages 254-265. scheduling of a multi-product multi-factory manufacturing [25]. P. Kochel, “Order optimisation in multi-location models with system with maritime transport limits, Transportation hub-and-spoke structure”, International Journal of Production Research Part E: Logistics and Transportation Review, Economics, Volume 108, Issues 1–2, July 2007, Pages 368- Volume 79, July 2015, Pages 110-127. 387. [16]. Wooseung Jang, Daeki Kim, Kwangtae Park, “Inventory [26]. Erdem Eskigun, Reha Uzsoy, Paul V. Preckel, George allocation and shipping when demand temporarily exceeds Beaujon, Subramanian Krishnan, Jeffrey D. Tew, “Outbound production capacity”, European Journal of Operational supply chain network design with mode selection, lead times Research, Volume 227, Issue 3, 16 June 2013, Pages 464- and capacitated vehicle distribution centers”, European 470. Journal of Operational Research, Volume 165, Issue 1, 16 [17]. Qin Geng, Suman Mallik, “Inventory competition and August 2005, Pages 182-206. allocation in a multi-channel distribution system”, European [27]. Vinh Van Thai, Devinder Grewal, “Selecting the location of Journal of Operational Research, Volume 182, Issue 2, 16 distribution centre in logistics operations: A conceptual October 2007, Pages 704-729. framework and case study”, Asia Pacific Journal of [18]. Maria Salonen, Tuuli Toivonen, “Modelling travel time in Marketing and Logistics, Vol. 17 Iss: 3 pp. 3 – 24 urban networks: comparable measures for private car and [28]. Leandro C. Coelho, Jean-François Cordeau, Gilbert Laporte, public transport”, Journal of Transport Geography, Volume “The inventory-routing problem with transshipment”, 31, July 2013, Pages 143-153. Computers & Operations Research, Volume 39, Issue 11, November 2012, Pages 2537-2548.
Proceedings of Academics World 5th International Conference, Paris, France, 11th October 2015, ISBN: 978-93-85832-08-6 68