Grey risky multi-attribute decision-making method based on regret theory and EDAS
Grey Systems: Theory and Application
ISSN: 2043-9377
Article publication date: 17 September 2018
Issue publication date: 28 January 2019
Abstract
Purpose
The purpose of this paper is to advance a new grey risky multi-attribute decision-making (RMADM) method from the perspective of regret aversion, which is based on the general grey numbers (GGNs) taking the form of kernel and degree of greyness.
Design/methodology/approach
First, the normalised grey decision-making matrix is obtained on the basis of kernel and greyness degree of GGNs. Then the regret theory is integrated into the decision-making process by constructing the grey perceived utility function based on GGNs. Finally, the method of evaluation based on distance from average solution (EDAS) is applied to handle with the ranking problem because of its efficiency, stability as well as simplicity.
Findings
GGNs have more powerful capacity in expressing uncertainty than interval grey numbers, so the method can solve a larger number of RMADM problems in uncertain and imprecise environments. Meanwhile, the method fully considers the psychological behaviour of the decision makers, which is more applicable to the real world. It is the supplement and perfection of the existing RMADM methods.
Originality/value
The RMADM problem, the grey regret-rejoice function and the EDAS method are all introduced for the first time with GGNs in the form of kernel and degree of greyness. At the same time, the EDAS method is also the first time to be used in combination with the grey RMADM method based on the regret theory.
Keywords
Acknowledgements
This work was supported by the National Natural Science Foundation of China (Nos 11101283 and 71671091), and the National Social Science Foundation of China (No. 12AZD102).
Citation
Qian, L., Liu, S. and Fang, Z. (2019), "Grey risky multi-attribute decision-making method based on regret theory and EDAS", Grey Systems: Theory and Application, Vol. 9 No. 1, pp. 101-113. https://doi.org/10.1108/GS-05-2018-0025
Publisher
:Emerald Publishing Limited
Copyright © 2018, Emerald Publishing Limited