Hesitant Fuzzy Multiattribute Matching Decision Making Based on Regret Theory with Uncertain Weights
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An approach based on regret theory with hesitant fuzzy analysis is presented in a context of multiattribute matching decision making where the relative weights are uncertain. There are two steps being addressed in this approach. First, we put forward a maximizing differential model to determine the relative weights of hesitant fuzzy attributes, and calculate collective utilities of each attribute according to regret theory. The matching satisfaction degrees (MSDs) are then acquired by aggregating the collective utilities with relative weights. Secondly, an optimal matching model is programmed to generate the matching results based on the MSDs. This model belongs to a sort of multiobjective assignment problem and can be solved using the min–max method. A case study of matching outsourcing contractors and providers in Fuzhou National Hi-tech Zone is conducted to demonstrate the proposed approach and its potential applications.
KeywordsMatching decision making Hesitant fuzzy set Regret theory Maximizing differential method Matching satisfaction degree Optimization model
The authors would like to express their sincere thanks to anonymous referees and editors for their insightful and constructive comments, which have helped to improve the paper. This work was partly supported by the National Natural Science Foundation of China (NSFC) under Grant 71371053, and the Social Science Foundation of Fujian Province under FJ2015C111.
- 17.Placek, M., Rajkumar, B.: Storage exchange: a global trading platform for storage services. In: Euro-Par Parallel Processing, pp. 425–436 (2006)Google Scholar
- 18.Engel, Y., Wellman, M.P., Lochner, K.M.: Bid expressiveness and clearing algorithms in multiattribute double auctions. In: Proceedings of the 7th ACM Conference on Electronic Commerce, pp. 110–119 (2006)Google Scholar
- 36.Fan, Z.P., Yue, Q.: Strict two-sided matching method based on complete preference ordinal information. J. Manag. Sci. 17(1), 21–34 (2014)Google Scholar
- 41.Rodriguez, R.M., Bedregal, B., Bustince, H., Dong, Y.C., et al.: A position and perspective analysis of hesitant fuzzy sets on information fusion in decision making. Towards high quality progress. Inf. Sci. 29, 89–97 (2015)Google Scholar