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A personalized individual semantics model for computing with linguistic intuitionistic fuzzy information and application in MCDM

  • Mathematical Methods in Data Science
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Abstract

This paper develops a personalized individual semantics (PISs) model for computing with linguistic intuitionistic fuzzy information and applies to evaluate different brands of mobile phones. First, considering that a linguistic term means different things to different decision-makers, a consistency-driven optimization model for checking the additive consistent linguistic intuitionistic fuzzy preference relations (LIFPRs) is constructed by considering the PISs model. Besides, several optimization models are built to determine the PISs of linguistic terms with LIFPRs and obtain the acceptable additive consistent LIFPRs. Second, a new definition of Hamming distance for measuring linguistic intuitionistic fuzzy numbers (LIFNs) is developed by considering the PISs model, and several desirable properties are discussed. Then, the method of deriving the weight vectors of criteria is calculated based on the proposed distance measure. Subsequently, a framework of group decision-making (GDM) process with LIFPRs is offered, and the application of the proposed method is illustrated by using a multi-criteria decision-making (MCDM) problem about evaluating different brands of mobile phones. Finally, the comparative analysis is conducted to show the feasibility of proposed method.

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Acknowledgements

The authors thank the anonymous reviewers and the editor for their insightful and constructive comments and suggestions that have led to an improved version of this paper. This work was supported by the National Natural Science Foundation of China (Nos.72061026), the Natural Science Foundation of Guangxi (Nos. 2020GXNSFAA297239), the Science and Technology Plan of Guangxi (gui ke AD20238006) and Young Teachers’ Basic Scientific Research Ability in Universities of Guangxi (No. 2022KY0387 and 2021KY1563).

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Authors and Affiliations

  1. School of Logistics Management and Engineering, Nanning Normal University, Nanning, 530001, People’s Republic of China

    Jian Li & Qiongxia Chen

  2. Guangxi Agricultural Vocational and Technical University, Nanning, 530007, People’s Republic of China

    Hongxia Tang & Li-li Niu

  3. Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision, Nanning Normal University, Nanning, 530001, People’s Republic of China

    Jian Li

  4. Guangxi Medical University Affiliated Tumor Hospital, Nanning, 530021, People’s Republic of China

    Feilong Li

  5. School of Mathematics and Information Science, Guangxi University, Nanning, 530004, People’s Republic of China

    Zhong-xing Wang

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  1. Jian Li
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  3. Li-li Niu
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  4. Qiongxia Chen
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Correspondence to Hongxia Tang.

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Authors, Jian Li, Hongxia Tang, Li–li Niu, Qiongxia Chen, Feilong Li and Zhong-xing Wang, declare that they have no conflict of interest.

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Li, J., Tang, H., Niu, Ll. et al. A personalized individual semantics model for computing with linguistic intuitionistic fuzzy information and application in MCDM. Soft Comput 27, 4501–4519 (2023). https://doi.org/10.1007/s00500-022-07698-1

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