The optimization ordering model for intuitionistic fuzzy preference relations with utility functions
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摘要
Intuitionistic fuzzy sets describe information from the three aspects of membership degree, non-membership degree and hesitation degree, which has more practical significance when uncertainty pervades qualitative decision problems. In this paper, we investigate the problem of ranking intuitionistic fuzzy preference relations (IFPRs) based on various non-linear utility functions. First, we transform IFPRs into their isomorphic interval-value fuzzy preference relations (IVFPRs), and utilise non-linear utility functions, such as parabolic, S-shaped, and hyperbolic absolute risk aversion, to fit the true value of a decision-maker’s judgement. Ultimately, the optimization ordering models for the membership and non-membership of IVFPRs based on utility function are constructed, with objective function aiming at minimizing the distance deviation between the multiplicative consistency ideal judgment and the actual judgment, represented by utility function, subject to the decision-maker’s utility constraints. The proposed models ensure that more factual and optimal ranking of alternative is acquired, avoiding information distortion caused by the operations of intervals. Second, by introducing a non-Archimedean infinitesimal, we establish the optimization ordering model for IFPRs with the priority of utility or deviation, which realises the goal of prioritising solutions under multi-objective programming. Subsequently, we verify that a close connection exists between the ranking for membership and non-membership degree IVFPRs. Comparison analyses with existing approaches are summarized to demonstrate that the proposed models have advantage in dealing with group decision making problems with IFPRs.
论文关键词:Intuitionistic fuzzy preference relation,Utility function,Ranking,Multiplicative consistency,Non-archimedean infinitesimal
论文评审过程:Received 25 February 2018, Revised 18 April 2018, Accepted 4 July 2018, Available online 20 July 2018, Version of Record 5 December 2018.
论文官网地址:https://doi.org/10.1016/j.knosys.2018.07.012