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A two-sided matching method considering the lowest value of acceptability with regret theory for probabilistic linguistic term sets

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Abstract

In order to solve matching problems for probabilistic linguistic information, a novel two-sided matching decision method for the probabilistic linguistic term sets (PLTSs) based on the regret theory considering the lowest value of acceptability is proposed. First, we propose a new utility function to transform the PLTSs to utility values, which can be conveniently applied to two-sided matching models. Then, to reflect the bounded rationality of expert and make the decision result close to real decision process, we put forward a novel regret-based model to obtain regret-rejoice by setting the lowest value of acceptability based on the utility function. Furthermore, we presented a new type of two-sided matching method considering constraint condition based on the lowest value of acceptability. Finally, we apply our method to a real case and make comparisons with two traditional two-sided methods.

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Acknowledgement

This research was funded by National Natural Science Foundation of China, grant number 71971190; Humanities and Social Science Fund of Ministry of Education of China, grant number 19YJA630039.

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Authors and Affiliations

  1. College of Economics and Management, Jiangsu University of Science and Technology, Zhenjiang, 212003, PR China

    Peng Li & Nannan Wang

  2. College of Mathematical Sciences, Yangzhou University, Yangzhou, 225002, PR China

    Cuiping Wei

  3. College of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, PR China

    Cuiping Wei

  4. School of Economics and Management, Shihezi University, Shihezi, 832000, PR China

    Na Zhang

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  1. Peng Li
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  2. Nannan Wang
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  3. Cuiping Wei
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  4. Na Zhang
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Correspondence to Na Zhang.

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Li, P., Wang, N., Wei, C. et al. A two-sided matching method considering the lowest value of acceptability with regret theory for probabilistic linguistic term sets. Int. J. Mach. Learn. & Cyber. 12, 917–930 (2021). https://doi.org/10.1007/s13042-020-01211-6

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  • DOI: https://doi.org/10.1007/s13042-020-01211-6

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