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A FBWM-PROMETHEE approach for industrial robot selection
In recent years, the selection of a robot for particular industrial purposes is one of the most challenging problems in the manufacturing environment based on automation and smartness for real-time decision-making. At present, several types of industrial robots with various capabilities, features, f...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7218022/ https://www.ncbi.nlm.nih.gov/pubmed/32420473 http://dx.doi.org/10.1016/j.heliyon.2020.e03859 |
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author | Nasrollahi, Mahdi Ramezani, Javaneh Sadraei, Mahmoud |
author_facet | Nasrollahi, Mahdi Ramezani, Javaneh Sadraei, Mahmoud |
author_sort | Nasrollahi, Mahdi |
collection | PubMed |
description | In recent years, the selection of a robot for particular industrial purposes is one of the most challenging problems in the manufacturing environment based on automation and smartness for real-time decision-making. At present, several types of industrial robots with various capabilities, features, facilities, and specifications are available in the market. This makes the decision-making process more and more complicated due to the increase in complexity, advanced technologies, and features that are continually being incorporated into the robots by several manufacturers. The decision-maker needs to identify and select the best-suited robot to attain the desired output with precise application ability, and minimum cost. This paper tries to solve the robot selection problem using Fuzzy Best-Worst Method and PROMETHEE as the two most appropriate multi-criteria decision-making (MCDM) methods for weighting criteria and ranking of decision alternatives, respectively. |
format | Online Article Text |
id | pubmed-7218022 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-72180222020-05-15 A FBWM-PROMETHEE approach for industrial robot selection Nasrollahi, Mahdi Ramezani, Javaneh Sadraei, Mahmoud Heliyon Article In recent years, the selection of a robot for particular industrial purposes is one of the most challenging problems in the manufacturing environment based on automation and smartness for real-time decision-making. At present, several types of industrial robots with various capabilities, features, facilities, and specifications are available in the market. This makes the decision-making process more and more complicated due to the increase in complexity, advanced technologies, and features that are continually being incorporated into the robots by several manufacturers. The decision-maker needs to identify and select the best-suited robot to attain the desired output with precise application ability, and minimum cost. This paper tries to solve the robot selection problem using Fuzzy Best-Worst Method and PROMETHEE as the two most appropriate multi-criteria decision-making (MCDM) methods for weighting criteria and ranking of decision alternatives, respectively. Elsevier 2020-05-08 /pmc/articles/PMC7218022/ /pubmed/32420473 http://dx.doi.org/10.1016/j.heliyon.2020.e03859 Text en © 2020 The Author(s) http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Nasrollahi, Mahdi Ramezani, Javaneh Sadraei, Mahmoud A FBWM-PROMETHEE approach for industrial robot selection |
title | A FBWM-PROMETHEE approach for industrial robot selection |
title_full | A FBWM-PROMETHEE approach for industrial robot selection |
title_fullStr | A FBWM-PROMETHEE approach for industrial robot selection |
title_full_unstemmed | A FBWM-PROMETHEE approach for industrial robot selection |
title_short | A FBWM-PROMETHEE approach for industrial robot selection |
title_sort | fbwm-promethee approach for industrial robot selection |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7218022/ https://www.ncbi.nlm.nih.gov/pubmed/32420473 http://dx.doi.org/10.1016/j.heliyon.2020.e03859 |
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