Comparing aggregation methods in large-scale group AHP: Time for the shift to distance-based aggregation
作者：
Highlights：
• Euclidean Distance Based and Aitchison Distance Based Aggregation Methods presented.
• Efficiency examined by 96.000 simulation cases and by a real-world case study.
• In small dimensional cases both new methods overperform the conventional techniques.
• In high dimensional (7 to 9) cases Euclidean distance aggregation keeps its primacy.
• Both proposed methods have low computational time and high applicability.
摘要
•Euclidean Distance Based and Aitchison Distance Based Aggregation Methods presented.•Efficiency examined by 96.000 simulation cases and by a real-world case study.•In small dimensional cases both new methods overperform the conventional techniques.•In high dimensional (7 to 9) cases Euclidean distance aggregation keeps its primacy.•Both proposed methods have low computational time and high applicability.
论文关键词：Group AHP,Priority vector,Aggregation,Consensus creation,Rank correlation,Compatibility
论文评审过程：Received 5 December 2020, Revised 15 November 2021, Accepted 8 February 2022, Available online 15 February 2022, Version of Record 18 February 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.116667
