dc.contributor.author | Zakeri, Shervin | |
dc.contributor.author | Chatterjee, Prasenjit | |
dc.contributor.author | Cheikhrouhou, Naoufel | |
dc.contributor.author | Konstantas, Dimitri | |
dc.contributor.author | Yang, Yingjie | |
dc.date.accessioned | 2023-09-18T16:40:55Z | |
dc.date.available | 2023-09-18T16:40:55Z | |
dc.date.issued | 2023 | |
dc.identifier.issn | 0957-4174 | |
dc.identifier.other | (SCOPUS_ID)85159629909 | |
dc.identifier.uri | https://etalpykla.vilniustech.lt/handle/123456789/115933 | |
dc.description.abstract | 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. | eng |
dc.format | PDF | |
dc.format.extent | p. 1-17 | |
dc.format.medium | tekstas / txt | |
dc.language.iso | eng | |
dc.relation.isreferencedby | Science Citation Index Expanded (Web of Science) | |
dc.relation.isreferencedby | Scopus | |
dc.relation.isreferencedby | ScienceDirect | |
dc.source.uri | https://www.sciencedirect.com/science/article/pii/S095741742300965X | |
dc.title | MUTRISS: A new method for material selection problems using MUltiple-TRIangles scenarios | |
dc.type | Straipsnis Web of Science DB / Article in Web of Science DB | |
dcterms.accessRights | This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). | |
dcterms.license | Creative Commons – Attribution – 4.0 International | |
dcterms.references | 40 | |
dc.type.pubtype | S1 - Straipsnis Web of Science DB / Web of Science DB article | |
dc.contributor.institution | University of Geneva University of Applied Sciences Western Switzerland | |
dc.contributor.institution | Vilniaus Gedimino technikos universitetas | |
dc.contributor.institution | University of Applied Sciences Western Switzerland | |
dc.contributor.institution | University of Geneva | |
dc.contributor.institution | De Montfort University | |
dc.contributor.faculty | Statybos fakultetas / Faculty of Civil Engineering | |
dc.contributor.department | Tvariosios statybos institutas / Institute of Sustainable Construction | |
dc.subject.researchfield | T 007 - Informatikos inžinerija / Informatics engineering | |
dc.subject.researchfield | T 004 - Aplinkos inžinerija / Environmental engineering | |
dc.subject.researchfield | T 002 - Statybos inžinerija / Construction and engineering | |
dc.subject.vgtuprioritizedfields | IK0303 - Dirbtinio intelekto ir sprendimų priėmimo sistemos / Artificial intelligence and decision support systems | |
dc.subject.ltspecializations | L104 - Nauji gamybos procesai, medžiagos ir technologijos / New production processes, materials and technologies | |
dc.subject.en | material selection | |
dc.subject.en | analytic geometry | |
dc.subject.en | mutriss | |
dc.subject.en | relative closeness ratio | |
dc.subject.en | robustness analysis | |
dc.subject.en | compromise ranking coefficient | |
dc.subject.en | similarity degree | |
dcterms.sourcetitle | Expert systems with applications | |
dc.description.volume | vol. 228 | |
dc.publisher.name | Elsevier Ltd | |
dc.publisher.city | Oxford | |
dc.identifier.doi | 2-s2.0-85159629909 | |
dc.identifier.doi | S095741742300965X | |
dc.identifier.doi | 85159629909 | |
dc.identifier.doi | 1 | |
dc.identifier.doi | 1-s2.0-S095741742300965X | |
dc.identifier.doi | S0957-4174(23)00965-X | |
dc.identifier.doi | 147758432 | |
dc.identifier.doi | 001009348800001 | |
dc.identifier.doi | 10.1016/j.eswa.2023.120463 | |
dc.identifier.elaba | 167563834 | |