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

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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 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.

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Acknowledgements

The authors are grateful to the editors and three reviewers for their valuable comments that helped to improve the quality of this paper. The work was supported by the National Natural Science Foundation of China (Nos. 61806116, 62072294, 61672331, 61972238, 61876103, 61703363), the Key R&D program of Shanxi Province (International Cooperation, 201903D421041), the Natural Science Foundation of Shanxi (Nos. 201801D221175, 201901D211462, 201901D211176 and 201901D211414), Training Program for Young Scientific Researchers of Higher Education Institutions in Shanxi, Research Project Supported by Shanxi Scholarship Council of China, Cultivate Scientific Research Excellence Programs of Higher Education Institutions in Shanxi (CSREP) (2019SK036), Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi (STIP) (Nos. 201802014, 2019L0864, 2019L0066, 2019L0500), Industry-University-Research Collaboration Program Between Shanxi University and Xiaodian District, and the 1331 Engineering Project of Shanxi Province, China.

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Authors and Affiliations

  1. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan, 030006, Shanxi, China

    Chao Zhang, Deyu Li & Jiye Liang

  2. School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, Shanxi, China

    Chao Zhang & Deyu Li

  3. School of Mathematics and Information Technology, Yuncheng University, Yuncheng, 044000, Shanxi, China

    Baoli Wang

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  1. Chao Zhang
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  2. Deyu Li
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Correspondence to Deyu Li.

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Zhang, C., Li, D., Liang, J. et al. MAGDM-oriented dual hesitant fuzzy multigranulation probabilistic models based on MULTIMOORA. Int. J. Mach. Learn. & Cyber. 12, 1219–1241 (2021). https://doi.org/10.1007/s13042-020-01230-3

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