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Solving Multi-attribute Decision-Making Problems Using Probabilistic Interval-Valued Intuitionistic Hesitant Fuzzy Set and Particle Swarm Optimization

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Numerical Optimization in Engineering and Sciences

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 979))

Abstract

This paper proposes a multi-attribute decision-making (MADM) approach using probabilistic interval-valued intuitionistic hesitant fuzzy set (P-IVIHFS) and particle swarm optimization (PSO) approach. Here, the assessing values of the alternatives regarding the attributes are given by probabilistic interval-valued intuitionistic hesitant fuzzy values (P-IVIHFVs) and weights of the attributes are given by interval-valued intuitionistic fuzzy values (IVIFVs). Firstly, the proposed method uses a P-IVIHFS-based score and an accuracy function to convert the assessment matrix into transformed assessment matrix. Then, PSO is used to determine the optimal weights of the attributes using the transformed assessment matrix. We use interval-valued intuitionistic hesitant fuzzy weighted geometric (IVIHFWG) operator to aggregate the values of each alternative. Next, the ranking of the alternatives is determined using the transformed values which is computed from the aggregated values of each alternative. Finally, the proposed study is validated using a numerical example.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Warangal, Warangal, 506004, India

    Kajal Kumbhar & Sujit Das

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  1. Kajal Kumbhar
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  2. Sujit Das
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Correspondence to Sujit Das .

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Editors and Affiliations

  1. National Institute of Technology Warangal, Warangal, Telangana, India

    Debashis Dutta

  2. Indian Institute of Technology Kharagpur, Kharagpur, India

    Biswajit Mahanty

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Kumbhar, K., Das, S. (2020). Solving Multi-attribute Decision-Making Problems Using Probabilistic Interval-Valued Intuitionistic Hesitant Fuzzy Set and Particle Swarm Optimization. In: Dutta, D., Mahanty, B. (eds) Numerical Optimization in Engineering and Sciences. Advances in Intelligent Systems and Computing, vol 979. Springer, Singapore. https://doi.org/10.1007/978-981-15-3215-3_14

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  • DOI: https://doi.org/10.1007/978-981-15-3215-3_14

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-3214-6

  • Online ISBN: 978-981-15-3215-3

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