Clustering analysis and punishment‐driven consensus‐reaching process for probabilistic linguistic large‐group decision‐making with application to car‐sharing platform selection

作者：Sumin Yu, Zhijiao Du, Xueyang Zhang · 发表于：International Transactions in Operational Research · 年份：2021 · DOI：10.1111/itor.13049 · 被引用次数：27 · 研究领域：Multi-Criteria Decision Making、Transportation Planning and Optimization、Economic and Environmental Valuation

Abstract Probabilistic linguistic large‐group decision‐making (LGDM) is becoming a hot topic in the field of decision science. In general, clustering and consensus‐reaching are the two important processes to deal with LGDM problems. Traditional clustering methods are mainly based on opinion similarity measures and cannot directly control the number of decision makers (DMs) that can be contained in a cluster. Situations of noncooperative behavior toward consensus may be encountered during the consensus‐reaching process. This paper first proposes a distance‐to‐center clustering algorithm to classify the large group. The most important characteristic of the algorithm is that it is based on similarity measures of probabilistic linguistic information, and specifies the upper and lower limits on the number of DMs in the cluster. Then, a novel weight‐determining method is developed that considers the size, external consensus level, and inner consensus level of a cluster. A punishment‐driven consensus model is designed to manage opinion differences and noncooperative behaviors. Finally, the proposed clustering algorithm and consensus model are applied to a case study of car‐sharing platform selection. The results and comparative analysis reveal the potential application and effectiveness of this study.