Addressing multi-attribute large-scale group decision making with overlapping subgroups: A bidirectional adjustment consensus reaching method for heterogeneous decision makers

Yanxin Xu, Yanbing Ju*, Peiwu Dong, Tian Ju, Petra Maresova, Enrique Herrera-Viedma

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Due to the rapid development of internet technology, multi-attribute large-scale group decision making (MALSGDM) becomes a research hotspot of multi-attribute group decision making. An interesting phenomenon is that decision makers (DMs) may assume multiple roles across various interest groups, thereby naturally forming overlapping subgroups. Aiming at the MALSGDM problem with overlapping subgroups, this paper proposes an overlapping subgroup-based consensus reaching method considering heterogeneous DMs. Firstly, a novel overlapping clustering method is proposed to decompose the large-scale group into several overlapping subgroups characterized by low evaluation conflict and stable trust relationship, thereby identify ordinary and key DMs. Secondly, a two-criteria consensus reaching judgment method is devised as the termination condition for the consensus reaching process. Thirdly, a novel bidirectional adjustment method considering heterogeneous DMs is constructed, which focuses on key DMs belonging to multiple subgroups simultaneously. The adjusted evaluation values are obtained through linear uncertain minimum adjustment consensus models under two different cases. Finally, a numerical example is provided to demonstrate the feasibility of the proposed method, and the comparative and sensitivity analyses are carried out to verify the superiority of the proposed method.

Original languageEnglish
Article number110260
JournalComputers and Industrial Engineering
Volume193
DOIs
Publication statusPublished - Jul 2024

Keywords

  • Bidirectional adjustment method
  • Consensus reaching
  • Multi-attribute large-scale group decision making
  • Overlapping subgroups
  • Social network analysis

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