一种新的模糊 KEMIRA 方法在创新园区选址分析和选择中的应用

IF 4.6 3区 管理学 Q1 BUSINESS
Mehdi Soltanifar;Madjid Tavana;Francisco J. Santos-Arteaga;Vincent Charles
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引用次数: 0

摘要

本研究针对多属性决策(MADM),同时捕捉和处理复杂问题中固有的不确定性,提出了一种名为模糊凯门尼中值指标排序法(KEMIRA)的新方法。我们探索通过优先投票来增强 MADM 模型,并将其改写为带有权重限制的线性规划(LP)问题。我们的模糊 KEMIRA 模型利用 LP 来确定每个特征的最佳优先级和权重,并以判别强度函数为指导。为了说明我们方法的有效性,我们利用了文献中一个著名的数字示例。我们还介绍了一个案例研究,该案例描述了受专家对各种属性的主观判断制约的创新园区选址问题。通过与犹豫模糊 KEMIRA 和随机 KEMIRA 的比较分析,我们证明了我们提出的模糊 KEMIRA 方法具有更高的灵活性,并减轻了计算负担。通过强调这些属性,我们强调了我们方法的通用性,它适用于广泛的 MADM 问题,远远超出了特定实例的范围。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A New Fuzzy KEMIRA Method With an Application to Innovation Park Location Analysis and Selection
This study introduces a novel approach named the fuzzy Kemeny median indicator ranks accordance (KEMIRA) method tailored for multiattribute decision making (MADM) while capturing and processing the uncertainties inherent in complex problems. We explore preferential voting to enhance MADM models, rewriting it as a linear programming (LP) problem with weight restrictions. Our fuzzy KEMIRA model leverages LP to ascertain optimal priorities and weights for each feature, guided by discrimination intensity functions. To illustrate the effectiveness of our approach, we utilize a well-known numerical example from the literature. We also present a case study describing the location selection of an innovation park constrained by experts’ subjective judgments across various attributes. Through comparative analyses with hesitant fuzzy KEMIRA and stochastic KEMIRA, we demonstrate our proposed fuzzy KEMIRA method's higher flexibility and reduced computational burden. By emphasizing these attributes, we underscore the versatility of our method, which applies to a broad spectrum of MADM problems that go well beyond specific instances.
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来源期刊
IEEE Transactions on Engineering Management
IEEE Transactions on Engineering Management 管理科学-工程:工业
CiteScore
10.30
自引率
19.00%
发文量
604
审稿时长
5.3 months
期刊介绍: Management of technical functions such as research, development, and engineering in industry, government, university, and other settings. Emphasis is on studies carried on within an organization to help in decision making or policy formation for RD&E.
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