基于相对熵和二元熵的多粒度直觉语言TODIM决策方法

针对评估信息为多粒度直觉语言集的决策问题，提出一种基于相对熵和二元熵的TODIM方法。该方法首先定义了直觉语言数的相对熵和二元熵，以度量决策信息的差异和不确定性；其次，构建了基于相对熵和二元熵的专家赋权模型，并建立了主观权重完全已知、部分已知和完全未知场景下的属性赋权模型；最后，为集结多粒度群体决策信息，提出了多粒度直觉语言加权算术平均(MIL-WAA)算子。算例分析表明，该方法能够较好地度量决策信息的不确定性和差异性，并考虑了决策者的有限理性行为，具有一定的合理性和有效性。

Multi-granular intuitionistic linguistic TODIM method based on relative entropy and binary entropy

To solve the decision problem in which the evaluations are multi-granular intuitionistic linguistic sets, this paper proposed a TODIM method based on relative entropy and binary entropy. Firstly, this method defined relative entropy and binary entropy of the intuitionistic linguistic sets to measure the difference and uncertainty of decision information. Secondly, it built an expert weighting model on the basis of relative entropy and binary entropy, and proposed attribute weighting models under the scenarios where the subjective weight was completely known, partially known and completely unknown. Finally, in order to gather the information of multi-granularity group decision information, this paper developed a multi-granular intuitio-nistic linguistic weighted arithmetic average(MIL-WAA) operator. The example analysis shows that this method can better measure the uncertainty and difference of decision information, and it considers the limited rationality behavior of decision-makers, so it is reasonable and effective.