基于直觉模糊信息的多属性排序问题的软集决策方法

针对属性信息具有模糊性的一类多属性方案排序问题,提出一种基于直觉模糊软集的决策方法。直觉模糊软集结合软集和直觉模糊集的优势,对对象近似描述时没有任何限制,并且能够更客观地表达事物的模糊性本质。首先定义直觉模糊软集的综合精确度、综合犹豫度和综合得分值,以及直觉模糊软集水平集的选择值和接受水平。在此基础上,提出关于多属性方案排序的选择值准则、精确度准则、犹豫度准则以及得分值准则。然后,提出一种组合使用各排序准则的多属性方案排序方法。最后,通过数值算例及讨论证实该方法的可行性和有效性。这种方法充分考虑决策者的直觉信息及主观偏好,能够有效完成多属性方案的优选和排序决策。

Multi-attribute decision-making based on subjective and objective integrated eigenvector method

An integrated approach is proposed to investigate the fuzzy multi-attribute decision-making （MADM） problems, where subjective preferences are expressed by a pairwise comparison matrix on the relative weights of attributes and objective information is expressed by a decision matrix. An eigenvector method integrated the subjective fuzzy preference matrix and objective information is proposed. Two linear programming models based on subjective and objective information are introduced to assess the relative importance weights of attributes in an MADM problem. The simple additive weighting method is utilized to aggregate the decision information, and then all the alternatives are ranked. Finally, a numerical example is given to show the feasibility and effectiveness of the method. The result shows that it is easier than other methods of integrating subjective and objective information.