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<p>In view of the fact that multi-attribute preferences and time factors are less applied in person-position matching scenarios, a person-position matching decision method considering multi-attribute preferences and time factors in probabilistic hesitant fuzzy environment is proposed. First, the problem of probabilistic hesitant fuzzy person-position matching is described. According to probabilistic hesitant fuzzy evaluation matrices given by position managers and job seekers, pros and cons grade matrices are obtained by calculating improved scores and standardized scores. Second, according to probabilistic hesitant fuzzy preference relations given by position managers and job seekers, weights of attributes are calculated; based on this, pros and cons probabilities of position managers and job seekers are obtained. According to pros and cons grade matrices and pros and cons probabilities, relative satisfactions of attributes are calculated by introducing the pros and cons relation. On this basis, the linear weighting method is used to obtain initial satisfaction matrices of bilateral subjects. According to time matrices and time satisfaction preferences of position managers and job seekers, time satisfaction matrices are calculated. According to initial satisfaction matrices and time satisfaction matrices, multiple person-position matching models are established. By solving these models, optimal person-position matching schemes are obtained. Finally, the feasibility and effectiveness of the proposed method are verified by a case analysis of person-position matching. Main contributions of this paper are as follows: (1) A new probabilistic hesitant fuzzy improved score is introduced. (2) An attribute weight calculation method based on probabilistic hesitant fuzzy preference relations is proposed. (3) An initial satisfaction calculation method considering pros and cons grades and probabilities is proposed. (4) A time satisfaction calculation method with exponential decay characteristics is proposed. (5) Multiple person-position matching models considering initial satisfactions and time satisfactions are built.</p>

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Person-Position Matching Decision Considering Multi-attribute Preferences and Time Factors in Probabilistic Hesitant Fuzzy Environment

  • 期刊论文

  • 发表日期：

    2025年2月21日

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  • Qi Yue,
  • Liezhang Liu,
  • Yuan Tao,
  • He Huang

摘要

In view of the fact that multi-attribute preferences and time factors are less applied in person-position matching scenarios, a person-position matching decision method considering multi-attribute preferences and time factors in probabilistic hesitant fuzzy environment is proposed. First, the problem of probabilistic hesitant fuzzy person-position matching is described. According to probabilistic hesitant fuzzy evaluation matrices given by position managers and job seekers, pros and cons grade matrices are obtained by calculating improved scores and standardized scores. Second, according to probabilistic hesitant fuzzy preference relations given by position managers and job seekers, weights of attributes are calculated; based on this, pros and cons probabilities of position managers and job seekers are obtained. According to pros and cons grade matrices and pros and cons probabilities, relative satisfactions of attributes are calculated by introducing the pros and cons relation. On this basis, the linear weighting method is used to obtain initial satisfaction matrices of bilateral subjects. According to time matrices and time satisfaction preferences of position managers and job seekers, time satisfaction matrices are calculated. According to initial satisfaction matrices and time satisfaction matrices, multiple person-position matching models are established. By solving these models, optimal person-position matching schemes are obtained. Finally, the feasibility and effectiveness of the proposed method are verified by a case analysis of person-position matching. Main contributions of this paper are as follows: (1) A new probabilistic hesitant fuzzy improved score is introduced. (2) An attribute weight calculation method based on probabilistic hesitant fuzzy preference relations is proposed. (3) An initial satisfaction calculation method considering pros and cons grades and probabilities is proposed. (4) A time satisfaction calculation method with exponential decay characteristics is proposed. (5) Multiple person-position matching models considering initial satisfactions and time satisfactions are built.