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Chinese Journal of Management Science ›› 2019, Vol. 27 ›› Issue (12): 152-163.doi: 10.16381/j.cnki.issn1003-207x.2019.12.015

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Linguistic Preference Decision-Making Model Based on Consistency Local Adjustment Algorithm and DEA

JIN Fei-fei1,2,3, NI Zhi-wei2,3, CHEN Hua-you4, ZHU Xu-hui2,3, WU Wen-ying2,3   

  1. 1. School of Business, Anhui University, Hefei 230601, China;
    2. School of Management, Hefei University of Technology, Hefei 230009, China;
    3. Key Laboratory of Process Optimization and Intelligent Decision-Making Ministry of Education, Hefei 230009, China;
    4. School of Mathematical Sciences, Anhui University, Hefei 230601, China
  • Received:2018-03-29 Revised:2018-07-04 Online:2019-12-20 Published:2019-12-30

Abstract: The linguistic preference relations (LPRs) are useful tool to address situations in which the decision makers (DMs) are more comfortable providing their evaluation information linguistically rather than numerical values. Consistency improvement process and deriving the reliable priority weight vector for alternatives are two significant and challenging issues in decision-making with LPRs. In this paper, a linguistic preference decision-making model is investigated with consistency local adjustment algorithm and data envelopment analysis (DEA). Firstly, the concept of multiplicative consistent LPRs is reviewed. Then, a construction approach of multiplicative consistent LPRs is proposed, and a convergent consistency-improving algorithm for LPRs is designed to transform the unacceptable multiplicative consistent LPRs into the acceptable ones, in which the local adjustment strategy is presented to preserve the DM's original decision-making information as much as possible and use the DM's original information sufficiently. Subsequently, a novel linguistic DEA model is developed to explore the relationship between the relative efficiency scores and the priority weight vector of multiplicative consistent LPRs. Furthermore, the linguistic preference decision-making model is investigated to generate the reliable ranking of the alternatives. Finally, a numerical example of selecting the desirable supplier is provided, and the comparison with existing approaches is performed to validate the rationality and effectiveness of the proposed linguistic preference decision-making model.

Key words: linguistic preference decision-making model, linguistic preference relation, multiplicative consistency, local adjustment algorithm, data envelopment analysis

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