Alternative Ranking-Based Clustering and Reliability Index-Based Consensus Reaching Process for Hesitant Fuzzy Large Scale Group Decision Making

The paper addresses the growing importance of Large Scale Group Decision Making (LSGDM) problems, focusing on hesitant fuzzy LSGDM. It introduces a Reliability Index-based Consensus Reaching Process (RI-CRP) to enhance efficiency. The proposed method assesses the ordinal consistency of decision makers' (DMs) information, measures deviation, and assigns a reliability index to DMs' opinions. An unreliable DMs management method is presented to filter out unreliable information. Additionally, an Alternative Ranking-based Clustering (ARC) method with hesitant fuzzy reciprocal preference relations is proposed to improve the efficiency of RI-CRP. The numerical example demonstrates the feasibility and effectiveness of the ARC method and RI-CRP for hesitant fuzzy LSGDM problems.Este artículo aborda la creciente importancia de los problemas de Toma de Decisiones en Grupo a Gran Escala (LSGDM), centrándose en el LSGDM difuso vacilante. Introduce un Proceso de Consenso Basado en Índices de Fiabilidad (RI-CRP) para mejorar la eficiencia. El método propuesto evalúa la consistencia ordinal de la información de los decisores, mide la desviación y asigna un índice de fiabilidad a las opiniones de los decisores. Se presenta un método de gestión de los decisores poco fiables para filtrar la información poco fiable. Además, se propone un método de agrupamiento alternativo basado en la clasificación (ARC) con relaciones de preferencia recíproca difusas vacilantes para mejorar la eficacia de RI-CRP. El ejemplo numérico demuestra la viabilidad y eficacia del método ARC y del RI-CRP para problemas LSGDM difusos vacilantes.Instituto Interuniversitario de Investigación en Data Science and Computational Intelligence (DaSCI

Green supplier selection based on CODAS method in probabilistic uncertain linguistic environment

Probabilistic uncertain linguistic sets (PULTSs) have widely been used in MADM or MAGDM. The CODAS method, which is a novel MADM or MAGDM tool, aims to acquire the optimal choice which have the largest Euclidean & Hamming distances from the NIS. This paper designs the probabilistic uncertain linguistic CODAS (PUL-CODAS) method with sine entropy weight. Finally, a numerical example for green supplier selection is given and the obtained results are compared with some existing models. First published online 05 February 202

Consistency-driven methodology to manage incomplete linguistic preference relation: A perspective based on personalized individual semantics

The file attached to this record is the author's final peer reviewed version.In linguistic decision making problems there may be cased when decision makers will not be able to provide complete linguistic preference relations. However, when estimating unknown linguistic preference values in incomplete preference relations, the existing research approaches ignore the fact that words mean different things for different people, i.e. decision makers have personalized individual semantics (PISs) regarding words. To manage incomplete linguistic preference relations with PISs, in this paper we propose a consistency-driven methodology both to estimate the incomplete linguistic preference values and to obtain the personalized numerical meanings of linguistic values of the different decision makers. The proposed incomplete linguistic preference estimation method combines the characteristic of the personalized representation of decision makers and guarantees the optimum consistency of incomplete linguistic preference relations in the implementation process. Numerical examples and a comparative analysis are included to justify the feasibility of the PISs based incomplete linguistic preference estimation method

Group decision-making models for venture capitalists: the PROMETHEE with hesitant fuzzy linguistic information

Venture capitalists (VCs) have long been preoccupied by the issue of selecting a promising start-up firm, whereas, ranking the available start-up firms is an effective way to solve this issue. In this paper, the PROMETHEE is chosen to be the fundamental ranking method. Also, the hesitant fuzzy linguistic term set is a suitable tool to simulate VCs’ evaluation information. Additionally, as the deepening of social division of labor and specialization of individuals, group decision making is famous for improving decision-making quality. Moreover, in the decision-making process, VCs exhibit behavioral characteristics which is depicted well by prospect theory that VCs are risk averse for gains and risk seeking for losses and rely on the transformed probability to make their decisions rather than unidimensional probability. Thus, a group prospect PROMETHEE with hesitant fuzzy linguistic information is constructed for VCs to make a better decision. Then, the proposed method is applied to rank start-up firms and the comparative analyses are made as well. It confirms that the group prospect PROMETHEE is better in describing the common behavioral characteristics of VCs and in enhancing the quality of evaluation