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

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 marg...

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Bibliographic Details
Published in:Granular computing (Internet) Vol. 3; no. 4; pp. 275 - 283
Main Authors: Liu, Shukuan, Xu, Zeshui, Gao, Jie
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.12.2018
Subjects:
Artificial Intelligence
Computational Intelligence
Engineering
Original Paper
Supplier selection
Fuzzy compromise programming
Modified S-curve membership function
ISSN:2364-4966, 2364-4974
Online Access:Get full text
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Summary: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.
ISSN:2364-4966
2364-4974
DOI:10.1007/s41066-017-0066-1