Dual probabilistic linguistic consensus reaching method for group decision-making

Abhijit Saha*, Tapan Senapati*, Muhammad Akram, Cengiz Kahraman, Radko Mesiar, Leena Arya

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

The interactive group decision-making method with dual probabilistic linguistic term sets (DPLTSs) is talked about in this paper. The benefit of choosing DPLTSs is that they take into account both random and stochastic uncertainties at the same time. The DPLTSs are superior to hesitant fuzzy sets (HFSs), probabilistic HFSs, and probabilistic dual HFSs with uncertainty. Here, we combine the merits of DPLTSs, the Archimedean copula aggregator, and the generalized Maclaurin symmetric mean (GMSM) operator and develop a consensus-based group decision-making methodology. In this methodology, the concepts of consistency and similarity between the experts are used to calculate their weights of subjective and objective types, respectively. We propose an optimization model based on cross-entropy measure and dispersion measure to generate the criteria weights. For aggregating preferences, we propose DPL weighted GMSM aggregation operator utilizing the Archimedean Copula operations between DPL elements. A case study exhibits the proposed method's applicability in group decision-making. The sensitivity analysis of criteria weights establishes the stability of the developed approach. To validate the robustness of the suggested method, a comparison study has been performed, which ensures the effectiveness of the proposed methodology.

Original languageEnglish
Article number35
JournalGranular Computing
Volume9
Issue number2
DOIs
Publication statusPublished - Jun 2024

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

Keywords

  • Cross-entropy measure
  • DPL weighted GMSM aggregation operator
  • Dual probabilistic linguistic term set
  • Group decision-making
  • Supply chain selection

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