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A parametric distance-based outranking method for probabilistic linguistic multi-criteria decision-making problems

  • Soft computing in decision making and in modeling in economics
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Abstract

Probabilistic linguistic term set (PLTS) is a useful tool to flexibly express uncertainty in experts’ preferences and has attracted much attention. This paper aims to develop an outranking method with high generalization performance to solve the multi-criteria decision-making (MCDM) problems under a probabilistic linguistic environment. To simplify the computation, a novel standardization process based on the greatest common divisor is first put forward to preprocess PLTSs to have the same probabilities. On this basis, a parametric distance measure between PLTSs is proposed. Next, a new concept called the outranking degree of PLTSs is introduced to compare PLTSs and four kinds of binary relations for PLTSs are then defined by using this concept. Moreover, an outranking framework similar to ELECTRE I method is formulated to rank alternatives for MCDM problems with probabilistic linguistic information. Finally, a case study is provided to confirm the validity and effectiveness of the developed method, and the advantages of this study are further tested by some robustness and comparative analysis.

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Data Availability Statement

The data and material used to support the findings in this research can be provided by the corresponding author upon request.

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Acknowledgements

We would like to thank the editors and anonymous reviewers for their helpful comments and suggestions.

Funding

The funding was provided the project for Natural Science Foundation of China (No. 71901074), Science and Technology Program of Guangzhou, China (No. 202102020600) and the University Innovation Team Project of Guangdong Province in 2021 (2021WCXTD013), the Guangdong Basic and Applied Basic Research Foundation (Grant No. 2022A1515110315).

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Authors and Affiliations

  1. School of Business, Guangdong University of Foreign Studies, Guangzhou, 510006, China

    Pei Wang

  2. School of Management, Jiangsu University, Zhenjiang, 212016, China

    Zhen Shen

  3. School of Management, Guangdong University of Technology, Guangzhou, 510520, China

    Shuai Huang

Authors
  1. Pei Wang
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  2. Zhen Shen
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  3. Shuai Huang
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Contributions

Pei Wang contributed to methodology, writing, and funding acquisition. Zhen Shen performed visualization and data analysis. Shuai Huang contributed to conceptualization, software, and funding acquisition

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Correspondence to Shuai Huang.

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Wang, P., Shen, Z. & Huang, S. A parametric distance-based outranking method for probabilistic linguistic multi-criteria decision-making problems. Soft Comput 27, 8137–8152 (2023). https://doi.org/10.1007/s00500-023-08121-z

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