A Fuzzy AHP-TOPSIS Approach for Selecting the Multimodal Freight Transportation Routes

  • Kwanjira KaewfakEmail author
  • Van-Nam Huynh
  • Veeris Ammarapala
  • Chayakrit Charoensiriwath
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1103)


Multimodal transportation route selection strategy has become an important component in the main logistics and transportation. Route selection relies upon decision-based on real industry data and expert judgments. This paper proposes Fuzzy Analytic Hierarchy Process (AHP) and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for prioritizing effectively the multimodal transportation routes to improve logistics system performance by constructing the possible routes considering transport cost, time, risk, and quality factors. Fuzzy AHP is used to determine weights for evaluation criteria and Fuzzy TOPSIS is used to aid the ranking of possible route alternatives. The empirical case study of coal manufacturing is conducted to illustrate a proposed methodology that enables to provide a more accurate, practical, and systematic decision support tool.


Multimodal freight transportation Route selection Fuzzy set theory Fuzzy AHP Fuzzy TOPSIS 


  1. 1.
    Crainic, T.G.: Handbook of Transportation Science. Kluwer Academic Publishers, Norwell (2003)Google Scholar
  2. 2.
    SteadieSeifi, M., Dellaert, N.P., Nuijten, W., Van Woensel, T., Raoufi, R.: Multimodal freight transportation planning: a literature review. Eur. J. Oper. Res. 233, 1–15 (2014)CrossRefGoogle Scholar
  3. 3.
    Park, Y.I., Lu, W., Nam, T.H., Yeo, G.T.: Terminal vitalization strategy through optimal route selection adopting CFPR methodology. Asian J. Shipp. Logist. 35, 41–48 (2019)CrossRefGoogle Scholar
  4. 4.
    Huynh, N., Fotuhi, F.: A new planning model to support logistics service providers in selecting mode, route, and terminal location. Pol. Marit. Res. 20, 67–73 (2013)CrossRefGoogle Scholar
  5. 5.
    Banomyong, R., Beresford, A.: Multimodal transport: the case of Laotian garment ex-porters. Int. J. Phys. Distrib. Logist. Manag. 31(9), 663–685 (2001)CrossRefGoogle Scholar
  6. 6.
    Krile, S.: Efficient heuristic for non-linear transportation problem on the route with multiple ports. Pol. Marit. Res. 20(4), 80–86 (2013)CrossRefGoogle Scholar
  7. 7.
    Balakrishnan, A., Karsten, C.V.: Container shipping service selection and cargo routing with transshipment limits. Eur. J. Oper. Res. 263(2), 652–663 (2017)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Raza, Z.: The commercial potential for LNG shipping between Europe and Asia via the Northern Sea Route. J. Marit. Res. 11(2), 67–79 (2014)Google Scholar
  9. 9.
    Sheffi, Y., Mahmassani, H., Powell, W.B.: A transportation network evacuation model. Transp. Res. Part A Gen. 16(3), 209–218 (1982)CrossRefGoogle Scholar
  10. 10.
    Qu, L., Chen, Y.: A hybrid MCDM method for route selection of multimodal transportation network. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds.) ISNN 2008. LNCS, vol. 5263, pp. 374–383. Springer, Heidelberg (2008). Scholar
  11. 11.
    Carbone, V., Martino, M.D.: The changing role of ports in supply-chain management: an empirical analysis. Marit. Policy Manag. 30(4), 305–320 (2003)CrossRefGoogle Scholar
  12. 12.
    Cabral, A.M.R., Ramos, F.S.: Cluster analysis of the competitiveness of container ports in Brazil. Transp. Res. Part A Policy Pract. 69, 423–431 (2014)CrossRefGoogle Scholar
  13. 13.
    Dang, V.L., Yeo, G.T.: A competitive strategic position analysis of major container ports in Southeast Asia. Asian J. Shipp. Logist. 33(1), 19–25 (2017)CrossRefGoogle Scholar
  14. 14.
    Feng, L., Notteboom, T.: Small and medium-sized ports (SMPs) in multi-port gateway regions: the role of Yingkou in the logistics system of the Bohai sea. In: Notteboom, T. (ed.) Current Issues in Shipping, Ports and Logistics, pp. 543–563. University Press Antwerp, Brussels (2011)Google Scholar
  15. 15.
    Feng, L., Notteboom, T.: Peripheral challenge by small and medium sized ports (SMPs) in multi-port gateway regions: the case study of northeast of China. Pol. Marit. Res. 20, 55–66 (2013)CrossRefGoogle Scholar
  16. 16.
    Vujić, M., Skorput, P., Mandžuka, B.: Multimodal route planners in maritime environment. Pomorstvo 29(1), 1–7 (2015)Google Scholar
  17. 17.
    Rostamzadeh, R., Sofian, S.: Prioritizing effective 7Ms to improve production systems performance using fuzzy AHP and fuzzy TOPSIS (case study). Expert Syst. Appl. 38, 5166–5177 (2011)CrossRefGoogle Scholar
  18. 18.
    Rahman, M.A., et al.: Selection of the best inland waterway structure: a multicriteria decision analysis approach. Water Resour. Manag. 29, 2733–2749 (2015) CrossRefGoogle Scholar
  19. 19.
    Saaty, T.L.: The Analytic Hierarchy Process: Planning, Priority Setting, Resources Allocation. McGraw, New York (1980)zbMATHGoogle Scholar
  20. 20.
    Ammarapala, V., Chinda, T., Pongsayaporn, P., Ratanachot, W., Punthutaecha, K., Janmonta, K.: Cross-border shipment route selection utilizing analytic hierarchy process (AHP) method, p. 7 (2018)Google Scholar
  21. 21.
    Angelo, P.M., Furuichi, T., Ishii, N.: A fuzzy analytic network process for multi-criteria evaluation of contaminated site remedial countermeasures. J. Environ. Manag. 88, 479–495 (2008)CrossRefGoogle Scholar
  22. 22.
    Bottani, E., Rizzi, A.: A fuzzy multi-attribute framework for supplier selection in an e-procurement environment. Int. J. Logist. Res. Appl. 8(3), 249–266 (2005)CrossRefGoogle Scholar
  23. 23.
    Chan, F.T.S., Kumar, N., Tiwari, M.K., Lau, H.C.W., Choy, K.L.: Global supplier selection: a fuzzy-AHP approach. Int. J. Prod. Res. 46(14), 3825–3857 (2008)CrossRefGoogle Scholar
  24. 24.
    Mikhailov, L.: Fuzzy analytical approach to partnership selection in formation of virtual enterprises. Omega 30(5), 393–401 (2002)CrossRefGoogle Scholar
  25. 25.
    Hwang, C.L., Yoon, K.: Multiple Attribute Decision Making: Methods and Applications. Springer, Heidelberg (1981). Scholar
  26. 26.
    Sun, C.-C.: A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods. Expert Syst. Appl. 37, 7745–7754 (2010)CrossRefGoogle Scholar
  27. 27.
    Mandic, K., Delibasic, B., Knezevic, S., Benkovic, S.: Analysis of the financial parameters of Serbian banks through the application of the fuzzy AHP and TOPSIS methods. Econ. Model. 43, 30–37 (2014)CrossRefGoogle Scholar
  28. 28.
    Patil, S.K., Kant, R.: A fuzzy AHP-TOPSIS framework for ranking the solutions of Knowledge Management adoption in Supply Chain to overcome its barriers. Expert. Syst. Appl. 41, 679–693 (2014)CrossRefGoogle Scholar
  29. 29.
    Taylan, O., Bafail, A.O., Abdulaal, R.M.S., Kabli, M.R.: Construction projects selection and risk assessment by fuzzy AHP and fuzzy TOPSIS methodologies. Appl. Soft Comput. 17, 105–116 (2014)CrossRefGoogle Scholar
  30. 30.
    Zhang, Z., Guo, C.: Deriving priority weights from intuitionistic multiplicative preference relations under group decision-making settings. J. Oper. Res. Soc. 68, 1582–1599 (2018)CrossRefGoogle Scholar
  31. 31.
    Zhang, Z., Kou, X., Yu, W., Guo, C.: On priority weights and consistency for incomplete hesitant fuzzy preference relations. Knowl. Based Syst. 143, 115–126 (2017)CrossRefGoogle Scholar
  32. 32.
    Chang, D.: Applications of the extent analysis method on fuzzy AHP. Eur. J. Oper. Res. 95, 649–655 (1996)CrossRefGoogle Scholar
  33. 33.
    Gumus, A.-T.: Evaluation of hazardous waste transportation firms by using a two step fuzzy-AHP and TOPSIS methodology. Expert. Syst. Appl. 36(2), 4067–4074 (2009)MathSciNetCrossRefGoogle Scholar
  34. 34.
    Jaiswal, R., Ghosh, N.C., Lohani, A., Thomas, T.: Fuzzy AHP based multi crteria decision support for watershed prioritization. Water Resour. Manag. 29, 4205–4227 (2015)CrossRefGoogle Scholar
  35. 35.
    Rodrigue, J.P., Comtois, C., Slack, B.: The Geography of Transport Systems. Routledge, New York (2008)Google Scholar
  36. 36.
    Novák, P., Popesko, B.: Cost variability and cost behaviour in manufacturing enterprises. Econ. Sociol. 7(4), 89–103 (2014)CrossRefGoogle Scholar
  37. 37.
    Andersson, M., Berglund, M., Flodén, J., Persson, C., Waidringer, J.: A method for measuring and valuing transport time variability in logistics and cost benefit analysis. Res. Transp. Econ. 66, 59–69 (2017)CrossRefGoogle Scholar
  38. 38.
    Kengpol, A., Tuammee, S.: The development of a decision support framework for a qualitative risk assessment in multimodal green logistics: an empirical study. Int. J. Prod. Res. 54, 1020–1038 (2016)CrossRefGoogle Scholar
  39. 39.
    Kengpol, A., Tuammee, S., Tuominen, M.: The development of a framework for route selection in multimodal transportation. Int. J. Logist. Manag. 25(3), 581–610 (2014)CrossRefGoogle Scholar
  40. 40.
    Kiba-Janiak, M.: Opportunities and threats for city logistics development from a local authority perspective. J. Econ. Manag. 28(2), 23–39 (2017)CrossRefGoogle Scholar
  41. 41.
    Ibrahimovic, S., Franke, U.: A probabilistic approach to IT risk management in the Basel regulatory framework: a case study. J. Financ. Regul. Compliance 25(2), 176–195 (2017)CrossRefGoogle Scholar
  42. 42.
    Trond, S.N., Fallah, Z.: Risk perceptions, fatalism and driver behaviors in Turkey and Iran. Saf. Sci. 59, 187–192 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Knowledge ScienceJapan Advanced Institute of Science and TechnologyIshikawaJapan
  2. 2.School of Management Technology, Sirindhorn International Institute of TechnologyThammasat UniversityPathumthaniThailand
  3. 3.NECTECNational Science and Technology Development AgencyPathumthaniThailand

Personalised recommendations