多准则决策分析
蒙特卡罗方法
加权
计算机科学
灵敏度(控制系统)
适应性
理论(学习稳定性)
背景(考古学)
可靠性(半导体)
数学优化
运筹学
可靠性工程
机器学习
数学
工程类
统计
物理
放射科
生物
古生物学
功率(物理)
医学
量子力学
电子工程
生态学
作者
Huadong Cui,Songwei Dong,Jiayi Hu,Mengqi Chen,Bodong Hou,Jingshun Zhang,Botong Zhang,Jitong Xian,Faan Chen
标识
DOI:10.1016/j.ins.2023.119439
摘要
Employing an appropriate method to achieve a reliable decision remains a challenge for decision-makers (DMs) in the multiple-criteria decision-making (MCDM) process owing to its inherent model-related complexity, sensitivity, and uncertainty. In this context, this study proposes an innovative hybrid MCDM model that integrates criteria importance through intercriteria correlation (CRITIC), multi-attributive border approximation area comparison (MABAC), and k-means with Monte Carlo simulation (i.e., CRITIC–MABAC–Kmeans with Monte Carlo simulation), aiming to address MCDM problems with substantial stability and reliability. Specifically, MABAC attests to the stability of this method, as it is less affected by normalization and weighting schemes. In addition, the challenge of conflicting k-means clustering outcomes, owing to diverse initial centroid selections, is mitigated by a Monte Carlo simulation, which identifies the most probable type of result and compensates for small-sample size bias. The model performance is tested using a case study of observing transport safety accomplishments in the ASEAN region. Enhanced multiple comparisons of the experimental results verify the quality, efficiency, and adaptability of the proposed model, indicating its feasibility for DMs, policymakers, and practitioners as a practical tool for handling real-life MCDM activities in various domains under compounded sensitivity and uncertainty.
科研通智能强力驱动
Strongly Powered by AbleSci AI