多准则决策分析
排名(信息检索)
亲密度
稳健性(进化)
选择(遗传算法)
计算机科学
材料选择
妥协
数学优化
运筹学
数据挖掘
数学
机器学习
材料科学
数学分析
社会学
复合材料
化学
基因
生物化学
社会科学
作者
Shervin Zakeri,Prasenjit Chatterjee,Naoufel Cheikhrouhou,Dimitri Konstantas,Yingjie Yang
标识
DOI:10.1016/j.eswa.2023.120463
摘要
This paper proposes a new Multiple-criteria decision-making (MCDM) method called MUltiple-TRIangles ScenarioS (MUTRISS) with two scenarios respecting different levels of access to complete information for material selection problems. MUTRISS calculates the areas occupied by alternatives in n-dimensional space, employing analytic geometry and converting each alternative into n-edges forms. The paper applies MUTRISS to three material selection case studies, with Ti-6Al-4V, Material 4, and AISI 4140 Steel- UNS G41400 emerging as the best materials for the three examples with the highest overall scores of 0.036, 4.540 and 0.427 respectively. The results are compared with various MCDM methods through four statistical measures, including relative closeness ratio, robustness analysis, compromise ranking coefficient, and similarity degree. The measures focus on different aspects of MCDM methods in solving problems and their results. The paper concludes that MUTRISS offers a more robust and reliable approach for material selection problems compared to other MCDM methods, with the first scenario of MUTRISS being more reliable than the second scenario. The paper also emphasizes the importance of validating results in material selection problems due to the potential irreversible consequences of selecting the wrong material.
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