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A fuzzy compromise programming model based on the modified S-curve membership functions for supplier selection

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Abstract

Among various decision techniques, multi-objective programming model is a proper approach to solve the supplier selection problem. Notice that the multi-objective programming problem often consists of conflicting goals that cannot be achieved simultaneously, we present an approach to obtain the marginal evaluation of each objective and to aggregate these marginal evaluations into the global evaluation. Moreover, due to the limited historical data of prospective suppliers and most companies are reluctant to share proprietary information. Therefore, the decision makers often rely on vague information in their supplier selections. S-curve membership function is more flexible and robust than linear membership function when describing the vagueness. Thus, the compromise satisfactory level of multiple goals in the supplier selection problem can be represented by the modified S-curve membership function. Based on the global evaluation obtained, we can formulate a fuzzy compromise programming model based on the modified S-curve membership function. Lastly, a numerical example is given to demonstrate the efficiency of our method by comparing it with the weighted max–min method and the weighted additive method.

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Acknowledgements

The work was supported in part by the China National Natural Science Foundation (nos. 71771155, 71571123).

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Authors and Affiliations

  1. Antai School of Economics and Management, Shanghai Jiaotong University, Shanghai, 200052, China

    Shukuan Liu

  2. Business School, Sichuan University, Chengdu, 610064, Sichuan, China

    Zeshui Xu

  3. Institute for Disaster Management and Reconstruction, Sichuan University, Chengdu, 610207, China

    Jie Gao

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  1. Shukuan Liu
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  2. Zeshui Xu
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Correspondence to Zeshui Xu.

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Liu, S., Xu, Z. & Gao, J. A fuzzy compromise programming model based on the modified S-curve membership functions for supplier selection. Granul. Comput. 3, 275–283 (2018). https://doi.org/10.1007/s41066-017-0066-1

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