Resolving Rank Reversal in TOPSIS: A Comprehensive Analysis of Distance Metrics and Normalization Methods | |
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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 | 是 |
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 ) |
SDGS | 減少不平等,和平正義與有力的制度 |