基于优势、劣势和欧几里得归一化相似性度量的折衷排名:ESIASP方法

Q3 Decision Sciences
Moufida Hidouri
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引用次数: 0

摘要

徐晓展于2001年提出了多属性方法SIR.TOPSIS和SIR-SAW。SIR.TOPSIS方法有两个不同的变体,我们在这里称之为SR.TOPSIS和IR.TOPSIS。值得注意的是,每个变体只关注一种类型的索引,而不是两种类型的索引,这可能导致备选方案的最终排名存在问题。此外,SIR.TOPSIS变体排序指标基于TOPSIS接近系数,这在不受每个备选方案与正理想解和负理想解分离的相对重要性的影响的意义上是不灵活的。同样,SIR.SAW方法忽略了选择的优势流和劣势流的相对重要性。因此,引入一种新的排名方法来克服Xu的SIR方法中的缺陷是值得的。对所获得的排序结果进行比较,表明所建议的方法是Xu的SIR方法的一种相关且可实现的替代方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Compromise ranking based on superiority, inferiority and Euclidean normalised similarity metrics: the ESIASP method
Xiaozhan Xu introduced in the year 2001 the multi-attribute methods SIR.TOPSIS and SIR-SAW. The SIR.TOPSIS method has two different variants, which we refer to here as SR.TOPSIS and IR.TOPSIS. Noteworthy, each variant gives attention only to one type of indexes rather than both types, which may result in questionable final ranking of alternatives. Additionally, the SIR.TOPSIS variants ranking indexes have been based on the TOPSIS closeness coefficient, which is inflexible in the sense of not being affected by the relative importance of separations of each alternative from positive ideal solution and negative ideal solution. Likewise, the SIR.SAW method ignores the relative significances of superiority flows and inferiority flows of alternatives. It is therefore worthwhile to introduce a new ranking method to overcome the flaws seen in Xu's SIR methods. Comparison of the ranking results obtained shows that the suggested method is a relevant and implementable alternative to Xu's SIR methods.
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来源期刊
International Journal of Information and Decision Sciences
International Journal of Information and Decision Sciences Business, Management and Accounting-Management of Technology and Innovation
CiteScore
1.90
自引率
0.00%
发文量
13
期刊介绍: In today''s fast-paced business environment, even with an abundance of information, decision-making can be complex and slow. As floods of data emerge, effective information processing is sought as a panacea. With the ever-present spectre of uncertainty, sound decisions are key. As a consequence of the various conflicts/dilemmas, employment of efficient data management leading to better decision-making is the goal. Organisations must employ effective information management/decision-making processes at each critical stage of their functions. IJIDS addresses the issues involved in this. Topics covered include: -AHP or DEA -Behavioural sciences, psychology, sociology -Business intelligence, economics -Computing and decision sciences, data-driven decision making -Decision making in social setting, under uncertainty, with multimedia -Decision support systems/software -Decision theory, decision trees, MCDM -Ethical decision making, group decision making, software -Fuzzy information processing, game theory, grid analysis, informatics, IT -Intelligent agent technologies, neural networks, OLAP -Knowledge discovery in databases, web search, scenario/system analysis -Mathematics of decision sciences, decision making methods/styles -Perspectives of decision making, robust decisions, morphological analysis -Political/public decision making -Risk management, statistics
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