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一种基于理想解相似性排序技术的新扩展,用于带有区间数的群体决策中具有客观标准权重的情况。

A Novel Extension of the Technique for Order Preference by Similarity to Ideal Solution Method with Objective Criteria Weights for Group Decision Making with Interval Numbers.

作者信息

Kacprzak Dariusz

机构信息

Department of Mathematics, Faculty of Computer Science, Bialystok University of Technology, Wiejska 45A, 15-351 Bialystok, Poland.

出版信息

Entropy (Basel). 2021 Nov 3;23(11):1460. doi: 10.3390/e23111460.

DOI:10.3390/e23111460
PMID:34828158
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8620818/
Abstract

This paper presents an extension of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method with objective criteria weights for Group Decision Making (GDM) with Interval Numbers (INs). The proposed method is an alternative to popular and often used methods that aggregate the decision matrices provided by the decision makers (DMs) into a single group matrix, which is the basis for determining objective criteria weights and ranking the alternatives. It does not use an aggregation operator, but a transformation of the decision matrices into criteria matrices, in the case of determining objective criteria weights, and into alternative matrices, in the case of the ranking of alternatives. This ensures that all the decision makers' evaluations are taken into account instead of their certain average. The numerical example shows the ease of use of the proposed method, which can be implemented into common data analysis software such as Excel.

摘要

本文提出了一种理想解相似排序法(TOPSIS)的扩展方法,该方法具有客观标准权重,用于处理具有区间数(INs)的群体决策(GDM)。所提出的方法是一种替代常用方法的选择,常用方法是将决策者(DMs)提供的决策矩阵汇总为单个群体矩阵,这是确定客观标准权重和对备选方案进行排序的基础。该方法不使用聚合算子,而是在确定客观标准权重时将决策矩阵转换为标准矩阵,在对备选方案进行排序时转换为备选方案矩阵。这确保了考虑到所有决策者的评估,而不是他们的某种平均值。数值示例展示了所提方法的易用性,该方法可在诸如Excel等常见数据分析软件中实现。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4043/8620818/7500efa40bee/entropy-23-01460-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4043/8620818/00c5afa7577a/entropy-23-01460-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4043/8620818/da2a673de639/entropy-23-01460-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4043/8620818/d023476bb31d/entropy-23-01460-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4043/8620818/1a8c6aacefff/entropy-23-01460-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4043/8620818/7500efa40bee/entropy-23-01460-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4043/8620818/00c5afa7577a/entropy-23-01460-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4043/8620818/da2a673de639/entropy-23-01460-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4043/8620818/d023476bb31d/entropy-23-01460-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4043/8620818/1a8c6aacefff/entropy-23-01460-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4043/8620818/7500efa40bee/entropy-23-01460-g005.jpg

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