考虑边际效用的二元语言术语集新算法及其应用

New algorithm of 2-tuple language term sets considering marginal utility and its application

二元语言术语集是表达不确定环境下决策信息的重要工具,然而,已有的二元语言术语集的运算法则往往不能保证计算结果仍在语言范围之内.本文通过定义新的转换函数并结合Einstein运算,提出了二元语言术语集的新算法.新算法有效解决了以往运算法则中计算结果超出语言范围的问题,新算法同时也考虑了决策者的决策评判心理边际效用递减现象.在此基础上提出了二元语言术语集的多属性信息聚合算子和相应的多属性决策方法,并通过供应商选择案例验证了该算法的有效性。

2-tuple language term sets are important tools for expressing decision information in uncertain environments.However,the existing algorithms for 2-tuple language term sets often cannot ensure that the calculation results are still within the language range.This paper proposes a new algorithm by defining a new transformation function and combining Einstein operations.The new algorithm effectively solves the problem that the calculation result exceeds the linguistic range in the previous algorithm.The new algorithm also considers the phenomenon of decision-making maker’s decision-making psychological marginal utility decreases.On this basis,the multi-attribute information aggregation operators and the corresponding multi-attribute decision-making method of binary language term set are proposed,and the effectiveness of the algorithm is verified by the supplier selection case.