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Topsis Method For Supplier Selection Problem

Authors: Omid Jadidi; Fatemeh Firouzi; Enzo Bagliery;

Topsis Method For Supplier Selection Problem

Abstract

{"references": ["A, Amid, S.H. Ghodsypour, C. OBrien, A weighted additive fuzzy\nmulti-objective model for the supplier selection problem under price\nbreaks in a supply Chain, Int. J. of Prod. Econ.. 121 (2009) 323-332.", "G. Barbarosoglu and T. Yazgac, \"An application of the analytic\nhierarchy process to the supplier selection problem,\" Production and\nInventory Management Journal 1st quarter (1997), pp. 14-21.", "R. Narasimhan, \"An analytic approach to supplier selection,\" Journal of\nPurchasing and Supply Management 1 (1983), pp. 27-32.", "J. Sarkis, S. Talluri, \"A model for strategic supplier selection,\"\nProceedings of Ninth Int Conf on IPSERA, 2000, pp. 652-661.", "K.N. Thompson, \"Vendor profile analysis,\" Journal of Purchasing and\nMaterials Management 26 (1990) (1), pp. 11-18.", "E. Timmerman, \"An approach to vendor performance evaluation,\"\nJournal of Purchasing and Supply Management 1 (1986), pp. 27-32.", "R.M. Monezka and S.J. Trecha, \"Cost-based supplier performance\nevaluation,\" Journal of Purchasing and Materials Management 24\n(1998) (2), pp. 2-7.", "D.L. Smytka and M.W. Clemens, \"Total cost supplier selection model: A\ncase study,\" International Journal of Purchasing and Materials\nManagement 29 (1993) (1), pp. 42-49.", "F.P. Buffa and W.M. Jackson, \"A goal programming model for purchase\nplanning,\" Journal of Purchasing and Materials Management 19 (1983)\n(3), pp. 27-34.\n[10] S.S. Chaudhry, F.G. Forst and J.L. Zydiak, Vendor selection with price\nbreaks, European Journal of Operational Research 70 (1993), pp. 52-\n66.\n[11] G.D. Li, D. Yamaguchi, M. Nagai, \"A grey based decision making\napproach to the supplier selection problem,\" Mathematical and\nComputer Modelling 36 (2007), pp. 573-581.\n[12] J.L, Deng, \"The introduction of grey system\", The Journal of Grey\nSystem. 1 (1989), pp. 1-24.\n[13] LA, Zadeh, \"Fuzzy sets\", Information and Control. 8 (1965), pp 338-\n353.\n[14] R.E, Bellman, and L.A, Zadeh, \"Decision making in a fuzzy\nenvironment\", Management Science. 17 (1970), pp. 141-164.\n[15] J.J, Zhang, D.S, Wu, and D.L, Olson, \"The method of grey related\nanalysis to multiple attribute decision making problems with interval\nnumbers\", Mathematical and Computer Modelling. 42 (2005), pp. 991-\n998.\n[16] C.L. Hwang and K. P.Yoon, Multiple attribute decision making methods\nand applications, Springer, New York (1981).\n[17] A, Shaniana, and O, Savadogo, \"TOPSIS multiple-criteria decision\nsupport analysis for material selection of metallic bipolar plates for\npolymer electrolyte fuel cell\", Journal of Power Sources. 159 (2006), pp.\n1095-1104.\n[18] Jadidi O., Tang S. H., Firouzi F., Rosnah M. Y., \"An optimal grey based\napproach based on TOPSIS concepts for supplier selection problem\",\nInternational Journal of Management Science and Engineering\nManagement. Vol. 4 (2009) No. 2, pp. 104-117."]}

Supplier selection, in real situation, is affected by several qualitative and quantitative factors and is one of the most important activities of purchasing department. Since at the time of evaluating suppliers against the criteria or factors, decision makers (DMS) do not have precise, exact and complete information, supplier selection becomes more difficult. In this case, Grey theory helps us to deal with this problem of uncertainty. Here, we apply Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to evaluate and select the best supplier by using interval fuzzy numbers. Through this article, we compare TOPSIS with some other approaches and afterward demonstrate that the concept of TOPSIS is very important for ranking and selecting right supplier.

Keywords

fuzzy number, MADM, TOPSIS, Supplier selection

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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impulse
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