MAGDM-oriented dual hesitant fuzzy multigranulation probabilistic models based on MULTIMOORA

• Published in: International journal of machine learning and cybernetics Vol. 12; no. 5; pp. 1219 - 1241
• Main Authors: Zhang, Chao, Li, Deyu, Liang, Jiye, Wang, Baoli
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2021; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Case studies; Complex Systems; Computational Intelligence; Control; Data analysis; Data integration; Decision making; Engineering; Fault tolerance; Fuzzy sets; Information management; Information systems; Mechatronics; Multiple objective analysis; Original Article; Pattern Recognition; Probabilistic models; Robotics; Semantics; Soft computing; Software; Subjectivity; Systems Biology; Granular computing; MULTIMOORA; Multigranulation probabilistic models; Dual hesitant fuzzy information; MAGDM
• ISSN: 1868-8071; 1868-808X
• DOI: 10.1007/s13042-020-01230-3

In real world, multi-attribute group decision making (MAGDM) is a complicated cognitive process that involves expression, fusion and analysis of multi-source uncertain information. Among diverse soft computing tools for addressing MAGDM, the ones from granular computing (GrC) frameworks perform excellently via efficient strategies for multi-source uncertain information. However, they usually lack convincing semantic interpretations for MAGDM due to extreme information fusion rules and instabilities of information analysis mechanisms. This work adopts a typical GrC framework named multigranulation probabilistic models to enrich semantic interpretations for GrC-based MAGDM approaches, and constructs MAGDM-oriented multigranulation probabilistic models with dual hesitant fuzzy (DHF) information in light of the MULTIMOORA (Multi-Objective Optimization by Ratio Analysis plus the full MULTIplicative form) method. After reviewing several basic knowledge, we first put forward four types of DHF multigranulation probabilistic models. Then, according to the MULTIMOORA method, a DHF MAGDM algorithm is designed via the proposed theoretical models in the context of person-job (P-J) fit. Finally, an illustrative case study for P-J fit is investigated, and corresponding validity tests and comparative analysis are conducted as well to demonstrate the rationality of the presented models.