Interpreting Weights in Multiple Criteria Decision Making

Q3 Engineering
D. Podkopaev
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引用次数: 0

Abstract

Many decision making problems of business and management are formulated in terms of Multiple Attribute Decision Making (MADM): given a set of alternatives evaluated with multiple criteria, find the alternative which according to the Decision Maker (DM), has the most preferred combination of criteria values (attributes), or rank alternatives from the most preferred one to the least preferred one. The MADM methods incorporate mechanisms of building preference models based on information obtained from the DM. In a wide variety of such methods, the DM is supposed to provide information in terms of weights of criteria, usually understood as criteria’s priorities. These weights serve as parameters of the method- specific preference models. The DM can define weights directly, or by using special weight elicitation techniques such as AHP, MAVT and others. Our concerns are that when using weight-based methods, the DM cannot ensure the correctness of the preference model. First, different weight-based methods use different kinds of preference models, which prioritize criteria based on weights in different manners. Second, interpretation of weights in some MADM methods is far from intuitive. Thus, a situation may occur when an inexperienced DM thinks of weights differently than they actually work in the method, and expresses the preference information incorrectly. In this paper we demonstrate the differences between how weights are interpreted in several methods: simple additive weighting, TOPSIS, VIKOR and PROMETHEE. We do it by comparing rankings produced with methods based on randomly generated data. We demonstrate that differences of interpreting weights significantly contribute to differences in produced rankings. A solution to this problem could be twofold: first, increasing awareness of differences between method-specific weight-based prioritizing mechanisms, and second, providing interpretations of weights for popular methods in the language understandable by the DMs.
解释多准则决策中的权重
商业和管理中的许多决策问题都是用多属性决策(MADM)来表述的:给定一组用多个标准评估的备选方案,根据决策者(DM)找到具有最优选的标准值(属性)组合的备选方案,或将备选方案从最优选的到最不优选的进行排序。MADM方法结合了基于从决策决策中获得的信息建立偏好模型的机制。在各种这样的方法中,决策决策应该根据标准的权重提供信息,通常被理解为标准的优先级。这些权重作为方法特定偏好模型的参数。DM可以直接定义权重,也可以使用特殊的权重提取技术,如AHP、MAVT等。我们担心的是,当使用基于权重的方法时,DM不能保证偏好模型的正确性。首先,不同的基于权重的方法使用不同类型的偏好模型,这些模型以不同的方式根据权重对标准进行优先级排序。其次,在一些MADM方法中,权重的解释远非直观。因此,当没有经验的DM认为权重与方法中的实际工作不同时,可能会出现这样的情况,并且错误地表达了偏好信息。在本文中,我们展示了在几种方法中如何解释权重之间的差异:简单加性加权,TOPSIS, VIKOR和PROMETHEE。我们通过比较基于随机生成数据的方法产生的排名来做到这一点。我们证明,解释权重的差异显著有助于产生排名的差异。这个问题的解决方案可以是双重的:首先,提高对特定于方法的基于权重的优先级机制之间差异的认识,其次,用dm可以理解的语言为流行方法提供权重解释。
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来源期刊
International Journal of Information and Management Sciences
International Journal of Information and Management Sciences Engineering-Industrial and Manufacturing Engineering
CiteScore
0.90
自引率
0.00%
发文量
0
期刊介绍: - Information Management - Management Sciences - Operation Research - Decision Theory - System Theory - Statistics - Business Administration - Finance - Numerical computations - Statistical simulations - Decision support system - Expert system - Knowledge-based systems - Artificial intelligence
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