MAGDM-oriented dual hesitant fuzzy multigranulation probabilistic models based on MULTIMOORA
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ORIGINAL ARTICLE
MAGDM‑oriented dual hesitant fuzzy multigranulation probabilistic models based on MULTIMOORA Chao Zhang1,2 · Deyu Li1,2 · Jiye Liang1 · Baoli Wang3 Received: 9 May 2020 / Accepted: 2 November 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract 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 MAGDMoriented 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. Keywords Granular computing · MAGDM · Multigranulation probabilistic models · Dual hesitant fuzzy information · MULTIMOORA
1 Introduction MAGDM consists of several decision matrices provided by a panel of decision makers, and each of them involves a set of finite alternatives that are depicted by finite attributes * Deyu Li [email protected] Chao Zhang [email protected] Jiye Liang [email protected] Baoli Wang [email protected] 1
Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan 030006, Shanxi, China
2
School of Computer and Information Technology, Shanxi University, Taiyuan 030006, Shanxi, China
3
School of Mathematics and Information Technology, Yuncheng University, Yuncheng 044000, Shanxi, China
[40]. Until now, plenty of traditional approaches have been advised to handle MAGDM problems, and it is recognized that GrC-based methods act as quite effective representatives among them [9, 25, 38, 46, 48]. Compared with classic MAGDM methods, GrC-based methods excel in simulating human thinking processes and intelligent behaviors by using approximate reasonings rather than precise reasonings. In addition, GrC-based methods are able to divide a complicated problem into several fundamental components, then corresponding processing strategies for each component are employed to ef
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