Hierarchical Punishment-Driven Consensus Model for Probabilistic Linguistic Large-Group Decision Making with Application to Global Supplier Selection

Sumin Yu (), Zhijiao Du () and Xuanhua Xu ()
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Sumin Yu: Shenzhen University
Zhijiao Du: Sun Yat-Sen University
Xuanhua Xu: Central South University

Group Decision and Negotiation, 2021, vol. 30, issue 6, No 7, 1343-1372

Abstract: Abstract Large-group decision making (LGDM) 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 paper, we develop a hierarchical punishment-driven consensus model for LGDM 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-group decision making (PL-LGDM); Hierarchical punishment-driven consensus model (HPDCM); Global supplier selection; Hierarchical clustering; Hard adjustment; Soft adjustment (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10726-020-09681-3

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