期刊
JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
卷 73, 期 4, 页码 855-868出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/01605682.2020.1869917
关键词
Group decision making; generalised fuzzy number; Weight of period; Opinion interaction; Supplier selection
资金
- National Natural Science Foundation of China [71601032, 71771037, 71725001, 71971042]
- Sichuan Planning Project of Social Science [SC20C006]
- Fundamental Research Funds for the Central Universities [ZYGX2019J133]
This paper proposes a group decision making approach considering multi-period fuzzy information and opinion interaction for supplier selection. By taking into account both time factors and opinion interaction between decision makers, this approach can better meet the diverse evaluation requirements of decision makers and select suitable suppliers.
Since the performances of suppliers usually fluctuate over time and are affected by environmental changes, enterprises need to evaluate suppliers in the past several periods, rather than a single period. As the environment changes, decision makers will exchange their views and influence each other. In addition, multiple decision makers are not always knowledgeable and sometimes express their preferences about suppliers using fuzzy numbers. To meet the different evaluation requirements of decision makers and analyze the influence of time factors and opinion interaction between decision makers, this paper develops a group decision making approach considering multi-period fuzzy information and opinion interaction of decision makers for supplier selection. In this approach, decision makers provide their preferences in multiple periods using generalised fuzzy numbers and the weights of different periods are determined by a mathematical programming method. The effects of opinion interaction are considered by assigning various weights to different decision makers. Fuzzy Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method is used to rank potential suppliers. The results of a supplier selection example show the proposed approach can select suitable suppliers by considering multi-period fuzzy information and opinion interaction.
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