Hesitant Fuzzy Multiattribute Matching Decision Making Based on Regret Theory with Uncertain Weights

Hesitant Fuzzy Multiattribute Matching Decision Making Based on Regret Theory with Uncertain Weights 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Fuzzy Systems Springer Journals

Hesitant Fuzzy Multiattribute Matching Decision Making Based on Regret Theory with Uncertain Weights

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2016 by Taiwan Fuzzy Systems Association and Springer-Verlag Berlin Heidelberg
Subject
Engineering; Computational Intelligence; Artificial Intelligence (incl. Robotics); Operations Research, Management Science
ISSN
1562-2479
eISSN
2199-3211
D.O.I.
10.1007/s40815-016-0213-x
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

International Journal of Fuzzy SystemsSpringer Journals

Published: Jul 5, 2016

References

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