Abstract: Aiming at the complex emergency response solutions evaluation problems where assessments are generally of high uncertainty and relative importance among evaluative attributes often only can be recognized as prioritization relations among them, we investigate effective multiple attribute decision making(MADM)approaches in this paper. Firstly, to help decision makers elicit their uncertain opinions more effectively and comprehensively, we define a dual hesitant fuzzy unbalanced linguistic set, and develop its operational rules, entropy and distance measures. Then, we develop a dual hesitant fuzzy unbalanced linguistic prioritized weighted aggregation(DHFUBLPWA)operator. Next, we construct an MADM approach based on DHFUBLPWA operator and the entropy measure to effectively accommodate situations with attributes' prioritization relations and another MADM approach based on classic VIKOR framework to address the situations with unknown weighting vector for evaluative attributes. Finally, application studies have verified the effectiveness of our proposed methods.

Key words: emergency response solutions, dual hesitant fuzzy unbalanced linguistic set, prioritized aggregation operator, VIKOR

摘要： 在应用多属性决策理论求解应急响应预案评估问题时,问题结构的复杂性往往使决策者评价信息存在高度不确定性且属性相对重要性仅能以优先级关系来表征。为此,本文首先提出了双边犹豫模糊非均衡语言集这种新型信息形式以使决策者能够灵活有效的表征复杂评价信息,并定义了运算法则、熵和距离测度;其次,基于熵测度开发了双边犹豫模糊非均衡语言优先加权集成算子,并构建了能够考虑属性优先关系的多属性决策方法;进一步针对属性相对重要性不能由定性分析获得的情况,设计了客观权重确定方法,并构建了另一种更具实际灵活性的VIKOR决策方法;最后,实例研究表明了方法的有效性与优势。

关键词: 应急响应预案, 双边犹豫模糊非均衡语言集, 优先加权集成算子, VIKOR