MUTRISS: A new method for material selection problems using MUltiple-TRIangles scenarios


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.


open access article


Material selection, Analytic geometry, MUTRISS, Relative closeness ratio, Robustness analysis, Compromise ranking coefficient, Similarity degree


Zakeri, S., Chatterjee, P., Cheikhrouhou, N., Konstantas D. and Yang, Y. (2023) MUTRISS: A new method for material selection problems using MUltiple-TRIangles scenarios. Expert Systems with Applications. 228, 120463


Research Institute

Institute of Artificial Intelligence (IAI)