Sustainable supplier selection and order allocation: An integrated delphi method, fuzzy TOPSIS, and multi-objective programming model

The purpose of the current study is to select suppliers and determine their order allocation in a way that the performance of the sustainability of the supply process gets optimized on the whole. In this research, after reviewing the literature and investigating the supply chain of the case study (I...

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Veröffentlicht in:Scientia Iranica. Transaction E, Industrial engineering Jg. 26; H. 4; S. 2524 - 2540
Hauptverfasser: Rabieh, M, Rafsanjani, A Fadaei, Babaei, L, Esmaeili, M
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Tehran Sharif University of Technology 01.08.2019
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Zusammenfassung:The purpose of the current study is to select suppliers and determine their order allocation in a way that the performance of the sustainability of the supply process gets optimized on the whole. In this research, after reviewing the literature and investigating the supply chain of the case study (Iran Khodro's supply chain) through Delphi method, a set of evaluation criteria related to the performance of the suppliers in economic, social, and environmental terms was identified. In the next stage, by using the identified criteria, the multi-objective mathematical integer programming was presented to solve the problems of supplier selection and order allocation. The suggested mathematical programming in this research is designed to be multi-product, single-period, and multiple sourcing. Fuzzy TOPSIS method is applied to calculate the qualitative parameters that are used in the suggested mathematical programming. Ultimately, the mathematical model suggested in the research is solved by two methods, i.e., epsilon constraint method and weighted sum method. Moreover, the Total Value of Sustainable Purchasing (TVSP) is calculated for both cases. The comparison of these two methods indicates that, in this research, the results of the weighted sum method are more efficient than those of the epsilon constraint method.
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DOI:10.24200/sci.2018.5254.1176