The supplier selection application based on two methods: VIKOR algorithm with entropy method and Fuzzy TOPSIS with vague sets method

Abstract

In recent years, supply chain management (SCM) has become a hot issue of theoretical and empirical research. In supply chain management, the range of manufacturer’s decision expanded from the single enterprise itself to the entire supply chain. As a fundamental decision-making for manufacturers, the quality of supplier performance not only affects the downstream business, but also determines the success of the whole supply chain. Therefore, choosing suitable suppliers in the supply chain becomes a key strategic step, it directly impacts the benefits for manufacturers. However, it is difficult for manufacturers particularly in the supplier selection, to have comprehensive and accurate understanding of the supplier. The supplier selection is a complex multi-criteria problem including both quantitative and qualitative factors which may be in conflict and may also be uncertain. In this paper two scientific and simple calculation methods for manufacture’s decision-makers to choose the most ideal supplier have been provided, this paper deals with the supplier selection problems based on VIKOR algorithm (Vlse Kriterijumska Optimizacija Kompromisno Resenje) which is a compromise multiple criteria decision making approach with entropy method which gives the weights to indicators, and fuzzy multiple attribute decision-making method with vague sets incorporating TOPSIS. The VIKOR algorithm deals with the conflicts between indicators based on certain way to sort the scheme and choose the best scheme. And the Fuzzy TOPSIS with vague sets method helps to deal with inaccurate, incomplete and imperfect information to some extent. A numerical example is proposed to illustrate the effectiveness of the two algorithms. However, in the future it should consider the selection of indicators and data filtration and the right of assignment and so on, to make the results of evaluations more objective and accurate.

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