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Hesitant fuzzy multi-attribute decision making based on TOPSIS with incomplete weight information

机译：权重信息不完全的基于TOPSIS的犹豫模糊多属性决策

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摘要

Hesitant fuzzy set (HFS), which allows the membership degree of an element to a set represented by several possible values, is considered as a powerful tool to express uncertain information in the process of multi-attribute decision making (MADM) problems. In this paper, we develop a novel approach based on TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and the maximizing deviation method for solving MADM problems, in which the evaluation information provided by the decision maker is expressed in hesitant fuzzy elements and the information about attribute weights is incomplete. There are two key issues being addressed in this approach. The first one is to establish an optimization model based on the maximizing deviation method, which can be used to determine the attribute weights. According to the idea of the TOPSIS of Hwang and Yoon [1], the second one is to calculate the relative closeness coefficient of each alternative to the hesitant positive-ideal solution, based on which the considered alternatives are ranked and then the most desirable one is selected. An energy policy selection problem is used to illustrate the detailed implementation process of the proposed approach, and demonstrate its validity and applicability. Finally, the extended results in interval-valued hesitant fuzzy situations are also pointed out.
机译：犹豫模糊集（HFS）允许将元素的隶属程度提高到由多个可能值表示的集合，被认为是表达多属性决策（MADM）问题过程中不确定信息的强大工具。在本文中，我们开发了一种基于TOPSIS（类似于理想解决方案的订单偏好技术）和用于解决MADM问题的最大偏差方法的新颖方法，其中决策者提供的评估信息以犹豫的模糊元素和有关属性权重的信息不完整。此方法解决了两个关键问题。第一个是基于最大偏差方法建立优化模型，该模型可用于确定属性权重。根据Hwang和Yoon [1]的TOPSIS的思想，第二种方法是计算每个替代方案与犹豫的正理想解的相对接近系数，在此基础上对考虑的替代方案进行排名，然后选择最可取的替代方案被选中。能源政策选择问题用于说明该方法的详细实施过程，并证明其有效性和适用性。最后，指出了区间值犹豫模糊情况下的扩展结果。

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