基于一类新的直觉模糊熵的多属性决策方法研究

针对现有的直觉模糊熵只考虑了隶属度与非隶属度的偏差,而未考虑直觉模糊集自身包含的犹豫度信息的缺憾,本文提出了一类新的直觉模糊熵,它可以充分表达决策者的犹豫度信息.在此基础上,针对属性权重完全未知和属性权重信息部分已知的直觉模糊多属性决策问题,分别通过熵权法和利用最小化直觉模糊熵建立的最优化模型求解属性权重,给出了直觉模糊多属性决策的折中比值法.最后,通过应用实例说明了所提出方法的有效性和可行性.

A study of multi-attribute decision making based on a new intuitionistic fuzzy entropy measure

With respect to the limitations of most existed intuitionistic fuzzy entropy measures, which only considered the deviation of membership degree and non-membership degree, without including the decision maker's hesitation information, a new intuitionistic fuzzy entropy measure is proposed in the paper. The new intuitionistic fuzzy entropy measure can overcome such above shortcomings, and include both the deviation and hesitation. Based on this, for an intuitionistic fuzzy multi-attribute decision making (MADM) model, with attribute weights completely unknown or partially known, a new decision-making method is proposed. The attribute weights are respectively obtained by entropy weight method and solving the optimum model with minimized entropy. Further, the compromise ratio method is put forward for these intuitionistic fuzzy MADM problems, and two practical examples are presented to demonstrate the effectiveness and feasibility of the proposed method.