Hierarchical Punishment-Driven Consensus Model for Probabilistic Linguistic LSGDM

Su-Min Yu () and Zhi-Jiao Du ()
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Su-Min Yu: Shenzhen University
Zhi-Jiao Du: Sun Yat-sen University

Chapter Chapter 5 in Large-Scale Group Decision-Making, 2022, pp 71-99 from Springer

Abstract: Abstract Large-scale group decision making (LSGDM) has attracted extensive attention and has been used to model complex decision problems. It is necessary to implement a consensus-reaching process (CRP) due to the need to obtain a decision that is acceptable to the majority. The theory of probabilistic linguistic term sets (PLTSs) is very useful in addressing uncertain information in the decision-making process. In this chapter, we develop a hierarchical punishment-driven consensus model for LSGDM problems in the context of probabilistic linguistic information. The model has three stages. In the first stage, we define probabilistic linguistic large-group decision making. To improve the performance of PLTSs in the CRP, we redefine the rules governing their normalization and operations. In the second stage, the original large group is divided into several small subgroups by hierarchical clustering. In the third stage, we propose three levels of consensus measures and two adjustment strategies to refine the scope of measure and adjustment to the matrix element level. Then, a hierarchical punishment-driven consensus model is established that can provide guidance for adjustment and soften the human supervision of the CRP. Finally, a case study on global supplier selection illustrates the utility and applicability of the model, and a comparison with other linguistic models reveals its advantages.

Keywords: Probabilistic linguistic large-scale group decision making (PL-LSGDM); Hierarchical punishment-driven consensus model (HPDCM); Global supplier selection; Hard adjustment; Soft adjustment (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-16-7889-9_5

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DOI: 10.1007/978-981-16-7889-9_5

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