层次分析法
成对比较
多准则决策分析
排名(信息检索)
计算机科学
加权
一致性(知识库)
过程(计算)
运筹学
管理科学
等级制度
决策问题
人工智能
数学
工程类
算法
经济
放射科
操作系统
医学
市场经济
作者
Madjid Tavana,Mehdi Soltanifar,Francisco J. Santos‐Arteaga
标识
DOI:10.1007/s10479-021-04432-2
摘要
The Analytical Hierarchy Process (AHP) is a reliable, rigorous, and robust method for eliciting and quantifying subjective judgments in multi-criteria decision-making (MCDM). Despite the many benefits, the complications of the pairwise comparison process and the limitations of consistency in AHP are challenges that have been the subject of extensive research. AHP revolutionized how we resolve complex decision problems and has evolved substantially over three decades. We recap this evolution by introducing five new hybrid methods that combine AHP with popular weighting methods in MCDM. The proposed methods are described and evaluated systematically by implementing a widely used example in the AHP literature. We show that (i) the hybrid methods proposed in this study require fewer expert judgments than AHP but deliver the same ranking, (ii) a higher degree of involvement in the hybrid voting AHP methods leads to higher acceptability of the results when experts are also the decision-makers, and (iii) experts are more motivated and attentive in methods requiring fewer pairwise comparisons and less interaction, resulting in a more efficient process and higher acceptability.
科研通智能强力驱动
Strongly Powered by AbleSci AI