淡江大學機構典藏:Item 987654321/126867
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    題名: Resolving Rank Reversal in TOPSIS: A Comprehensive Analysis of Distance Metrics and Normalization Methods
    作者: Shyur, Huan-Jyh;Shih, Hsu-Shih
    關鍵詞: ranking reversal;TOPSIS;normalization;distance metric;extreme alternative
    日期: 2024.12
    上傳時間: 2025-03-20 09:28:04 (UTC+8)
    出版者: Vilnius University Institute of Data Science and Digital Technologies
    摘要: This paper examines ranking reversal (RR) in Multiple Criteria Decision Making (MCDM) using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Through a mathematical analysis of min-max and max normalization techniques and distance metrics (Euclidean, Manhattan, and Chebyshev), the study explores their impact on RR, particularly when new, high-performing alternatives are introduced. This research provides insight into the causes of RR, offering a framework that clarifies when and why RR occurs. The findings help decision-makers select appropriate techniques, promoting more consistent and reliable outcomes in real-world MCDM applications.
    關聯: Informatica 35( 4)， p. 837-858
    DOI: 10.15388/24-INFOR576
    顯示於類別:[資訊管理學系暨研究所] 期刊論文

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