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...
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doaj-c692ea28712d48b39045c5084ee63eb82020-11-25T03:00:34ZengElsevierHeliyon2405-84402020-05-0165e03859A FBWM-PROMETHEE approach for industrial robot selectionMahdi Nasrollahi0Javaneh Ramezani1Mahmoud Sadraei2Imam Khomeini International University (IKIU), Faculty of Social Sciences, Qazvin, Iran; Corresponding author.NOVA University of Lisbon, Faculty of Sciences and Technology and UNINOVA-CTS, Campus da Caparica, 2829-516, Monte Caparica, PortugalBusiness Consultant, Sydney, AustraliaIn 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.http://www.sciencedirect.com/science/article/pii/S2405844020307040Industrial engineeringMultidisciplinary design optimizationManufacturing engineeringTechnology managementOperations managementIndustry management |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Mahdi Nasrollahi Javaneh Ramezani Mahmoud Sadraei |
spellingShingle |
Mahdi Nasrollahi Javaneh Ramezani Mahmoud Sadraei A FBWM-PROMETHEE approach for industrial robot selection Heliyon Industrial engineering Multidisciplinary design optimization Manufacturing engineering Technology management Operations management Industry management |
author_facet |
Mahdi Nasrollahi Javaneh Ramezani Mahmoud Sadraei |
author_sort |
Mahdi Nasrollahi |
title |
A FBWM-PROMETHEE approach for industrial robot selection |
title_short |
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_sort |
fbwm-promethee approach for industrial robot selection |
publisher |
Elsevier |
series |
Heliyon |
issn |
2405-8440 |
publishDate |
2020-05-01 |
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. |
topic |
Industrial engineering Multidisciplinary design optimization Manufacturing engineering Technology management Operations management Industry management |
url |
http://www.sciencedirect.com/science/article/pii/S2405844020307040 |
work_keys_str_mv |
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1724697379354968064 |