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Third party logistics service selection using fuzzy multiple attribute decision - making system

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This study models the selection of third party logistics service provider (3PL) process considering comprehensive criteria and fuzzy nature of such problems. Criteria are identified and selected with respect to various aspects of logistics management, and existing vagueness in their behaviours and their relational preferences. Multiple attribute decision-making (MADM) approach and fuzzy methodology are applied. Based on a wide review of previous research, a fuzzy MADAM (FMADM) procedure is developed. Accordingly, especial algorithm in applying FMADM to 3PL selection problems is defined. A numerical example supports the developed procedure. Then, a real-world case study is explained and its 3PL selection problem is discussed. Results show reliability and efficiency of the model.
Rocznik
Strony
101--110
Opis fizyczny
Bibliogr. 27 poz.
Twórcy
  • Kingston Business School, Kingston University London, UK
Bibliografia
  • [1] Baas, S.M.; Kwakernaak, H., 1977. “Rating and ranking of multiple aspect alternative using fuzzy sets”, Automatica 13, pp. 47-58.
  • [2] Browne, M., Allen, J., Woodburn, A., 2007. Developments in Western European logistics strategies. In Water, D. (Ed.) global Logistics: New Directions in Supply Chain Management, Kogan Page, London.
  • [3] Chan, H.K., Wang, W.Y.C., Luong, L.H.S., Chan, F.T.S., 2009. Fexibility and adaptability in supply chains: a lesson learnt from a practitioner. Supply Chain Management: An International Journal, 14(6), 407-410.
  • [4] Chen , S.J.; Hwang , C.L.; HWANG, F.P., 1992. “Fuzzy Multiple Attribute Decision Making: Methods and Application”, Berlin: Springer.
  • [5] Christopher, M., 2000. Supply chain migration from lean and functional to agile and customised. Supply Chain Management: An International Journal, 5(4), 206-213.
  • [6] Coyle, J.J., Bardi, E.J., Langley, C.J., 2003. The Management of Business Logistics—A Supply Chain Perspective. South- Western Publishing, Mason.
  • [7] Dubois, D.; Prade, H., 1982. “The use of fuzzy numbers in decision analysis”, Fuzzy information and decision processes, North-Holland, pp. 309 321.
  • [8] Dubois, D.; Prade, H., 1983. “Ranking of fuzzy numbers in the setting of possibility theory”, Information Sciences, 30, pp. 183-224.
  • [9] Dubois, D.; Prade, H.; Testemale, C., 1988. “Weighted fuzzy pattern matching”, Fuzzy sets and systems, 26, pp. 225-242.
  • [10] Evangelista, P., Sweeney, E., 2006. Technology usage in the supply chain: The case of small 3PLs. The International Journal of Logistics Management, 17(1), 55–74.
  • [11] House, R.G., Stank, T.P., 2001. Insights from a logistics partnership. Supply Chain Management: An International Journal, 6 (1), 16–20.
  • [12] Hwang, C.L.; Yoon, K., 1981. “Multiple attribute decision making methods and applications, A stateof-the-art survey”, Springer, New York.
  • [13] Liou, T. S., Jiun,M. and Wang, J., 1992, Fuzzy weighted average: an improved algorithm. Fuzzy Sets and Systems, 49, 307-315.
  • [14] Marasco, A., 2008. Third-party logistics: A literature review, International Journal of Production Economics, 113(1), 127-147.
  • [15] Modarres, M., Saadinejad, M., 2001. “Ranking fuzzy numbers by preference ratio”, Fuzzy Sets and Systems 118, pp. 429-436.
  • [16] Nakamura, K., “Preference relations on a set of fuzzy utilities as a basis for decision making”, Fuzzy Sets and Systems, Vol. 20, pp. 147-162.
  • [17] Saura, I.G., Francés, D.S., Contrí, G.B., Blasco, M.F., 2008. Logistics service quality: a new way to loyalty, Industrial Management & Data Systems,108(5), 650-668.
  • [18] Spencer, M.S., Rogers, D.S., Daugherty, P.J., 1994. JIT Systems and External Logistics Suppliers. International Journal of Operations & Production Management. 14(6), 60-74.
  • [19] Trentin, A., 2011. Third-party logistics providers offering form postponement services: value propositions and organisational approaches. International Journal of Production Research, 49(6), 1685-1712.
  • [20] Vaidyanathan, G., 2005. A framework for evaluating third-party logistics. Communications of the ACM, 48(1), 89–94.
  • [21] Varila, M., Seppänen, M., Suomala, P., 2007. Detailed cost modelling: a case study in warehouse logistics. International Journal of Physical Distribution & Logistics Management, 37(3), 184- 200.
  • [22] Voss, H., 2003. International logistics outsourcing—Competitive advantages for 3PLproviders through intercultural compe- tence. In: Juga, J. (Ed.), Proceedings of the 15th Annual Conference for Nordic Researchers in Logistics: Striving for Leading Edge Logistics, 12–13 June, Faculty of Economics and Business Administration, University of Oulu, Oulu, Finland, 447–463.
  • [23] Wang, T.; Shaw, C.; Chen, Y., 2000. “Machine selection in flexible manufacturing cell: a fuzzy attribute decision making approach”, Int. J. Prod. Res., Vol. 38, No. 9, pp. 2079-2097.
  • [24] Yager, R.R., 1981, “A procedure for ordering fuzzy subsets of the unit interval” Information Science, 24, pp. 143-161.
  • [25] Yuan, Y., 1991. “Criteria for evaluating fuzzy ranking methods”, Fuzzy Sets and Systems, 44, pp. 139-157.
  • [26] Zhu, B., Wang, Z., Yang, h., Mo, R., and Zhao, Y., 2008. Applying fuzzy multiple attributes decision making for product configuration. Journal of Intelligent Manufacturing, 19(5), 591-598.
  • [27] Zimmermann, H.J., 1996. “Fuzzy Sets Theory and its Applications”, third edition, Boston, Kluwer Academic
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPW6-0021-0012
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