An integrated probabilistic linguistic projection method for MCGDM based on ELECTRE III and the weighted convex median voting rule. Issue 6 (27th June 2020)

• Record Type: Journal Article
• Authors: Chen, Zi‐yu; Wang, Xiao‐kang; Peng, Juan‐juan; Zhang, Hong‐yu; Wang, Jian‐qiang
• Other Names: Gonzalez‐Pardo Antonio guestEditor.; Tallón‐Ballesteros Antonio J. guestEditor.; Yin Hujun guestEditor.; Cortez Paulo guestEditor.; Bifet Albert guestEditor.
• Is Part Of: Expert systems. Volume 37:Issue 6(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-06-27
• Subjects: Bayesian best–worst method -- multi‐criteria group decision‐making -- probabilistic linguistic term sets -- projection -- the weighted convex median voting rule; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12593
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: In the multi‐criteria group decision‐making (MCGDM) problems with great uncertainty, making full use of participants' evaluation information could help improve the accuracy and reliability of decision results. Probabilistic linguistic term set (PLTS) is an effective tool to represent qualitative data and can fully express the hesitation and preference of decision makers. Therefore, this paper aims to propose an MCGDM method based on PLTSs. In the proposed method, the projection of PLTSs is explored to measure the distance and angle differences between two objects, and Bayesian best–worst method (Bayesian BWM) is used to determine the aggregated final weights of criteria. Besides, the elimination and choice translating reality III (ELECTRE III) method combined with distillation algorithm deals with the projection of PLTSs to obtain the alternatives' ranking of each decision maker. Then, the weighted convex median voting rule is developed to integrate the rankings results regarding all decision makers, which can solve the conflict of ranking results among experts and ensure that the comprehensive ranking results are reasonable and practical. Finally, a case study of health‐care waste management is designed and comparative analyses are implemented to show the effectiveness and advantages of the proposed method.