ะ้ An Application of Fuzzy Technique for Order Preference by Similarity to Ideal Solution for Selecting Multimodal Transportation Route
Keywords:
selecting transportation route, multimodal transportation, multiple criteria decision-making, fuzzy logic, technique for order preference by similarity to ideal solutionAbstract
The objective of this study was to propose an approach for selecting a route in multimodal transportation networks using fuzzy technique for order preference by similarity to ideal solution (FTOPSIS). the selection of multimodal transportation route between Thailand and Ho Chi Minh City was employed to test the proposed approach. The scales for importance assessment and alternative rating with a triangular membership function can be collected from the eligible decision makers based on the Delphi method. Then, the fuzzy positive ideal solution and the negative ideal solution were calculated. Finally, the closeness coefficient (CC) was computed and used to select and rank the multimodal transportation route. The results demonstrated that the multimodal transportation route A5 was the best optimal multimodal transportation route. This route responds effectively to all decision criteria because it has the highest CC. Regarding, the other multimodal transportation routes were arranged in descending order, according to the CCs’ values. Moreover, the results also revealed that the proposed approach is suitable for solving multiple criteria decision-making in the real-world problems, especially for multimodal route selection.
References
Afful-Dadzie, E., Nabareseh, S., Afful-Dadzie, A. and Oplatková, Z.K. 2015. A fuzzy TOPSIS framework for selecting fragile states for support facility. Quality & Quantity 49(5): 1835-1855.
Agrawal, S., Singh, R.K. and Murtaza, Q. 2016. Disposition decisions in reverse logistics by using AHP-fuzzy TOPSIS approach. Journal of Modelling in Management 11(4): 932-948.
Arunyanart, S., Ohmori, S. and Yoshimoto, K. 2016. Selection of export route option in GMS region recom-mendation for current situation. Journal of Japan Industrial Management Association 67(2E): 193-201.
Awasthi, A., Chauhan, S.S., Omrani, H. and Panahi, A. 2011. A hybrid approach based on SERVQUAL and fuzzy TOPSIS for evaluating transportation service quality. Computers & Industrial Engineering 61(3): 637-646.
Azadeh, A., Moghaddam, M., Nazari, T. and Sheikhalishahi, M. 2016. Optimization of facility layout design with ambiguity by an efficient fuzzy multivariate approach. The International Journal of Advanced Manu-facturing Technology 84(1-4): 565-579.
Castillo, J., Caruana, C.J., Morgan, P.S. and Westbrook, C. 2015. Radiographer managers and service develop-ment: a Delphi study to determine an MRI service portfolio for year 2020. Radiography 21(1): e21-e27.
Chen, S.Y. and Lu, C.C. 2015. Assessing the competitiveness of insurance corporations using fuzzy correlation analysis and improved fuzzy modified TOPSIS. Expert Systems 32(3): 392-404.
Dash, R., Samal, S., Dash, R. and Rautray, R. 2019. An integrated TOPSIS crow search based classifier ensemble: in application to stock index price movement prediction. Applied Soft Computing 85: 105784.
Hassan, M., Ali, M., Aktas, E. and Alkayid, K. 2015. Factors affecting selection decision of auto-identification technology in warehouse management: an international Delphi study. Production Planning & Control 26(12): 1025-1049.
Hwang, C.L. and Yoon, K. 1981. Multiple attribute decision making: methods and applications. Springer-Verlag, Berlin.
Kaewfak, K. and Ammarapala, V. 2018. The decision making of freight route in multimodal transportation. Suranaree Journal of Science and Technology 25(1): 1-10.
Kengpol, A. and Tuammee, S. 2016. The development of a decision support framework for a quantitative risk assessment in multimodal green logistics: an empirical study. International Journal of Production Research 54(4): 1020-1038.
Kengpol, A., Meethom, W. and Tuominen, M. 2012. The development of a decision support system in multimodal transportation routing within Greater Mekong sub-region countries. International Journal of Production Economics 140(2): 691-701.
Kengpol, A., Tuammee, S. and Tuominen, M. 2014. The development of a framework for route selection in multimodal transportation. The International Journal of Logistics Management 25(3): 581-610.
Ko, H.J. 2009. A DSS approach with fuzzy AHP to facilitate international multimodal transportation network. KMI International Journal of Maritime Affairs and Fisheries 1: 51-70.
Koohathongsumrit, N. 2017a. Optimization route selecting by multi-criteria decision making analysis. RMUTP Research Journal 11(1): 137-150. (in Thai)
Koohathongsumrit, N. 2017b. Selecting optimal distribution route by zero-one goal programming model. Research Journal Phranakhon Rajabhat: Science and Technology 12(2): 78-91. (in Thai)
Koohathongsumrit, N. 2019. Application of fuzzy logic system based risk assessment to select an optimal multimodal transportation route. KMUTT Research & Development Journal 42(4): 343-364. (in Thai)
Meethom, W. and Koohathongsumrit, N. 2018. Design of decision support system for road freight transportation routing using Multilayer Zero One Goal Programming. Engineering Journal 22(6): 185-205.
Meethom, W. and Koohathongsumrit, N. 2020. A decision support system for road freight transportation route selection with new fuzzy numbers. Foresight 22(4): 505-527.
Sangaiah, A.K., Subramaniam, P.R. and Zheng, X. 2015. A combined fuzzy DEMATEL and fuzzy TOPSIS approach for evaluating GSD project outcome factors. Neural Computing and Applications 26(5): 1025-1040.
Seo, Y.J., Chen, F. and Roh, S.Y. 2017. Multimodal transportation: the case of laptop from Chongqing in China to Rotterdam in Europe. The Asian Journal of Shipping and Logistics 33(3): 155-165.
Sharmila, D. and Uthayakumar, R. 2017. Integrated supply chain model for deteriorating item with exponential demand and triangular fuzzy on holding costs. Journal of Control and Decision 4(4): 234-262.
Singh, R.K. and Agrawal, S. 2018. Analyzing disposition strategies in reverse supply chains: fuzzy TOPSIS approach. Management of Environmental Quality: An International Journal 29(3): 427-443.
Vilko, J.P.P. and Hallikas, J.M. 2012. Risk assessment in multimodal supply chains. International Journal of Production Economics 140(2): 586-595.
Walczak, D. and Rutkowska, A. 2017. Project rankings for participatory budget based on the fuzzy TOPSIS method. European Journal of Operational Research 260(2): 706-714.
Wang, Y. and Yeo, G.T. 2016. A study on international multimodal transport networks from Korea to Central Asia: focus on secondhand vehicles. The Asian Journal of Shipping and Logistics 32(1): 41-47.
Wang, Y. and Yeo, G.T. 2017. Intermodal route selection for cargo transportation from Korea to Central Asia by adopting fuzzy Delphi and fuzzy ELECTRE I methods. Maritime Policy & Management 45(1): 3-18.
Woo, S.H., Kim, S.N., Kwak, D.W., Pettit, S. and Beresford, A. 2017. Multimodal route choice in maritime transportation: the case of Korean auto-parts exporters. Maritime Policy & Management 45(1): 19-33.
Yang, X., Low, J.M.W. and Tang, L.C. 2011. Analysis of intermodal freight from China to Indian Ocean: a goal programming approach. Journal of Transport Geography 19(4): 515-527.
Zadeh, L.A. 1965. Fuzzy sets. Information and Control 8(3): 338-353.
Downloads
Published
How to Cite
Issue
Section
License
The content and information in the article published in Journal of Rajamangala University of Technology Srivijaya It is the opinion and responsibility of the author of the article. The editorial journals do not need to agree. Or share any responsibility.
