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Multi-attribute group decision-making using double hierarchy hesitant fuzzy linguistic preference information

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Abstract

Double hierarchy hesitant fuzzy linguistic term set (DHHFLTS) is one of the successful extensions of the hesitant fuzzy linguistic term set used for describing the uncertain information in a more prominent manner for solving the group decision-making problems. In DHHFLTS, the membership functions are represented in terms of linguistic membership degrees which are a flexible structure for preference elicitation and enrich the ability for rational decision-making with complex linguistic expressions. Driven by the flexibility of DHHFLTS, in this paper, a new decision framework is developed for solving decision-making problems, which provides scientific and rational decisions based on the preference information. For it, initially, a new aggregation operator is proposed for aggregating decision-makers’ preferences. Later, the importance of the attribute weights in the problems is determined by formulating a mathematical model and the COPRAS method is extended to the DHHFLTS context for prioritizing alternatives. The applicability of the presented approach is demonstrated through a numeric example related to green supplier selection. A comparative analysis with existing studies is also administered to test the effectiveness and verify the method.

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Authors and Affiliations

  1. School of Computing, SASTRA University, Thanjavur, Tamil Nadu, 613401, India

    R. Krishankumar, K. S. Ravichandran, V. Shyam & S. V. Sneha

  2. Department of Mathematics, National Institute of Technology, Durgapur, West Bengal, India

    Samarjit Kar

  3. School of Mathematics, Thapar Institute of Engineering and Technology (Deemed University), Patiala, Punjab, 147004, India

    Harish Garg

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  1. R. Krishankumar
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  2. K. S. Ravichandran
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  3. V. Shyam
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Correspondence to Samarjit Kar.

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Krishankumar, R., Ravichandran, K.S., Shyam, V. et al. Multi-attribute group decision-making using double hierarchy hesitant fuzzy linguistic preference information. Neural Comput & Applic 32, 14031–14045 (2020). https://doi.org/10.1007/s00521-020-04802-0

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