多准则供应商选择的多元AHP树方法

IF 1.3 Q3 SOCIAL SCIENCES, MATHEMATICAL METHODS
Computational Management Science Pub Date : 2021-01-01 Epub Date: 2021-04-17 DOI:10.1007/s10287-021-00397-6
Toly Chen
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引用次数: 0

摘要

决策者通常对标准的优先次序持有各种观点,这使得决策过程变得复杂。为了克服这一问题,本研究提出了一种多元化 AHP 树方法。在所提出的多元化 AHP 树方法中,决策者的判断矩阵被分解成多个子判断矩阵,这些子判断矩阵比原始判断矩阵更加一致,代表了关于标准相对优先级的不同观点。因此,建立并优化了一个非线性编程模型,并为此设计了一种遗传算法。为了评估所提出的多元化 AHP 树方法的有效性,我们将其应用于一个供应商选择问题。实验结果表明,应用多元化 AHP 树方法可以从单一的判断矩阵中选择多个多元化供应商。此外,使用多样化 AHP 树方法选出的所有供应商都比较理想。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A diversified AHP-tree approach for multiple-criteria supplier selection.

A decision maker usually holds various viewpoints regarding the priorities of criteria, which complicates the decision making process. To overcome this concern, in this study, a diversified AHP-tree approach was proposed. In the proposed diversified AHP-tree approach, the judgement matrix of a decision maker is decomposed into several subjudgement matrices, which are more consistent than the original judgement matrix and represent diverse viewpoints on the relative priorities of criteria. Thus, a nonlinear programming model was established and optimized, for which a genetic algorithm is designed. To assess the effectiveness of the proposed diversified AHP-tree approach, it was applied to a supplier selection problem. The experimental results showed that the application of the diversified AHP-tree approach enabled the selection of multiple diversified suppliers from a single judgement matrix. Furthermore, all suppliers selected using the diversified AHP-tree approach were somewhat ideal.

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来源期刊
Computational Management Science
Computational Management Science SOCIAL SCIENCES, MATHEMATICAL METHODS-
CiteScore
1.90
自引率
11.10%
发文量
13
期刊介绍: Computational Management Science (CMS) is an international journal focusing on all computational aspects of management science. These include theoretical and empirical analysis of computational models; computational statistics; analysis and applications of constrained, unconstrained, robust, stochastic and combinatorial optimisation algorithms; dynamic models, such as dynamic programming and decision trees; new search tools and algorithms for global optimisation, modelling, learning and forecasting; models and tools of knowledge acquisition. The emphasis on computational paradigms is an intended feature of CMS, distinguishing it from more classical operations research journals. Officially cited as: Comput Manag Sci
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