A group decision-making method that links changes in experts’ preferences and weights: Application to site selection of waste-to-energy plants

Author:

Zhao Shuping12,Wang Dong12,Lei Ting12,Wang Yifan12

Affiliation:

1. School of Management, Hefei University of Technology, Hefei, Anhui, China

2. Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei, China

Abstract

The selection of a waste-to-energy (WTE) plant site is the core issue that determines whether the WTE project can effectively treat municipal solid waste, reduce environmental pollution, and promote the development of a circular economy, and is often determined through group decision-making. The complexity of this group decision problem makes the opinions of decision makers often with uncertainty. The single-valued neutrosophic set (SVNS) can reduce the loss of information that contains uncertainty by quantitatively describing the information through three functions. In addition, existing studies on group decision-making for WTE plant siting suffer from the problem that decision maker weights do not change in concert with those decision makers’ decision information. Therefore, this study proposes a group decision-making method based on SVNSs. First, a group consensus strategy is proposed to improve group consensus by removing the evaluation value of the corresponding solution for decision makers who do not reach consensus and are unwilling to modify their preferences. Second, a decision maker weight determination and adjustment method is proposed to readjust the decision maker weights from the solution level according to their respective consensus degree when the decision makers’ preference information changes. This method enables the decision makers’ preferences and weights to be changed jointly. An illustrative example and a comparative analysis of WTE plant siting decisions demonstrate the feasibility and superiority of the method. The experimental results show that the method is effective in helping decision makers to select the optimal WTE plant site more accurately.

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

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