一种基于IMF-SWARA和F-CODAS的咨询公司选择组合模糊MCDM方法

IF 0.5 Q4 ECONOMICS EGE ACADEMIC REVIEW Pub Date : 2023-09-12 DOI:10.21121/eab.1214630
Nilsen KUNDAKCI, Kevser ARMAN
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引用次数: 0

摘要

在当今充满挑战的行业条件下,优秀并不足以取得成功,试图做到最好的公司需要不同领域的咨询。咨询公司为企业提供咨询服务,他们需要确定最适合他们的咨询服务。模糊多准则决策方法适用于解决咨询公司选择问题。本研究采用一种基于IMF-SWARA(改进模糊逐步权重评价比率分析法)和F-CODAS(模糊组合距离评价法)的新型组合模糊MCDM方法来处理在土耳其伊斯坦布尔经营的一家纺织公司的咨询公司选择问题。采用IMF-SWARA方法计算各指标的重要性权重。调查结果表明,最重要的三个标准分别是:经验、参考和可靠性。然后使用F-CODAS方法对咨询公司进行排名,并将最佳咨询公司推荐给纺织公司的人力资源部。本研究对现有文献的贡献是多方面的。提出了一种新的组合模糊MCDM方法来解决咨询公司选择问题,并提出了一种新的基于tfn的模糊CODAS方法。此外,人力资源经理可以使用本研究的结果来评估咨询公司。
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A Novel Combined Fuzzy MCDM Approach Based on IMF-SWARA and F-CODAS for Consulting Firm Selection
In today's challenging industry conditions, where being good is not enough to be successful, companies trying to be the best, need consultancy in different fields. Consulting firms provides consultancy services to businesses, and they need to determine the most appropriate one for them. Fuzzy MCDM (Multi Criteria Decision Making) methods are appropriate to solve consulting firm selection problem. In this study, consulting firm selection problem of a textile company operating in Istanbul, Turkey is handled by using a novel combined fuzzy MCDM method based on IMF-SWARA (Improved Fuzzy Stepwise Weight Assessment Ratio Analysis) and F-CODAS (Fuzzy COmbinative Distance-based Assessment) methods. The importance weights of the criteria are calculated with IMF-SWARA method. Findings indicate that the top three important criteria are respectively, experience, references, and reliability. Then, F-CODAS method is used to rank the consulting firms and the best one is presented to the Human Resources department of the textile company. This study contributes to the existing literature in various aspects. It suggests a novel combined fuzzy MCDM method to solve consulting firm selection and a new Fuzzy CODAS based on TFNs is proposed. Moreover, HR managers can use the findings of this study to evaluate consulting firms.
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来源期刊
EGE ACADEMIC REVIEW
EGE ACADEMIC REVIEW ECONOMICS-
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发文量
32
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