Resolving Rank Reversal in TOPSIS: A Comprehensive Analysis of Distance Metrics and Normalization Methods
學年 113
學期 1
出版(發表)日期 2024-12-01
作品名稱 Resolving Rank Reversal in TOPSIS: A Comprehensive Analysis of Distance Metrics and Normalization Methods
作品名稱(其他語言)
著者 Huan-Jyh Shyur; Hsu-Shih Shih
單位
出版者
著錄名稱、卷期、頁數 Informatica 35( 4), p. 837-858
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.
關鍵字 ranking reversal; TOPSIS; normalization; distance metric; extreme alternative
語言 en
ISSN
期刊性質 國外
收錄於 SCI EI Scopus
產學合作
通訊作者 Huan-Jyh Shyur
審稿制度
國別 LTU
公開徵稿
出版型式 ,電子版,紙本
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/126867 )

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