Simulation optimization of operator allocation problem with learning effects and server breakdown under uncertainty
This paper investigates the operator allocation problem with learning effects and server breakdown in cellular manufacturing systems (CMSs) using fuzzy computer simulation and response surface methodology (RSM). The primary contribution of this study is incorporating combined server breakdowns and l...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2018-01-01
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| Series: | Production and Manufacturing Research: An Open Access Journal |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/21693277.2018.1531080 |
| _version_ | 1818484028777431040 |
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| author | Delaram Heydarian Fariborz Jolai |
| author_facet | Delaram Heydarian Fariborz Jolai |
| author_sort | Delaram Heydarian |
| collection | DOAJ |
| description | This paper investigates the operator allocation problem with learning effects and server breakdown in cellular manufacturing systems (CMSs) using fuzzy computer simulation and response surface methodology (RSM). The primary contribution of this study is incorporating combined server breakdowns and learning effects in CMS under uncertainty. Machine breakdowns of all the machines as well as the probability related to each entity should be delivered in good order are considered. Also, previous studies did not consider fuzzy simulation and RSM to deal with environmental and data uncertainty in operator allocation problems. The superiority of the presented model, in comparison with the traditional one, is shown according to the number of required iterations. The proposed simulation model is run in uncertain state to obtain the total processing time. RSM algorithm identifies a fitted function in terms of the value of allocated capital to each server and total processing time. This is a practical approach for decision-makers of all Cellular Manufacturing Systems. |
| first_indexed | 2024-12-10T15:50:03Z |
| format | Article |
| id | doaj.art-a6a880e6a9dc4250963cd60ed97a5c74 |
| institution | Directory Open Access Journal |
| issn | 2169-3277 |
| language | English |
| last_indexed | 2024-12-10T15:50:03Z |
| publishDate | 2018-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Production and Manufacturing Research: An Open Access Journal |
| spelling | doaj.art-a6a880e6a9dc4250963cd60ed97a5c742022-12-22T01:42:50ZengTaylor & Francis GroupProduction and Manufacturing Research: An Open Access Journal2169-32772018-01-016139641510.1080/21693277.2018.15310801531080Simulation optimization of operator allocation problem with learning effects and server breakdown under uncertaintyDelaram Heydarian0Fariborz Jolai1College of Engineering, University of TehranCollege of Engineering, University of TehranThis paper investigates the operator allocation problem with learning effects and server breakdown in cellular manufacturing systems (CMSs) using fuzzy computer simulation and response surface methodology (RSM). The primary contribution of this study is incorporating combined server breakdowns and learning effects in CMS under uncertainty. Machine breakdowns of all the machines as well as the probability related to each entity should be delivered in good order are considered. Also, previous studies did not consider fuzzy simulation and RSM to deal with environmental and data uncertainty in operator allocation problems. The superiority of the presented model, in comparison with the traditional one, is shown according to the number of required iterations. The proposed simulation model is run in uncertain state to obtain the total processing time. RSM algorithm identifies a fitted function in terms of the value of allocated capital to each server and total processing time. This is a practical approach for decision-makers of all Cellular Manufacturing Systems.http://dx.doi.org/10.1080/21693277.2018.1531080Operator allocationCellular Manufacturing System (CMS)learning effectserver breakdownFuzzy SimulationResponse Surface Methodology (RSM) |
| spellingShingle | Delaram Heydarian Fariborz Jolai Simulation optimization of operator allocation problem with learning effects and server breakdown under uncertainty Production and Manufacturing Research: An Open Access Journal Operator allocation Cellular Manufacturing System (CMS) learning effect server breakdown Fuzzy Simulation Response Surface Methodology (RSM) |
| title | Simulation optimization of operator allocation problem with learning effects and server breakdown under uncertainty |
| title_full | Simulation optimization of operator allocation problem with learning effects and server breakdown under uncertainty |
| title_fullStr | Simulation optimization of operator allocation problem with learning effects and server breakdown under uncertainty |
| title_full_unstemmed | Simulation optimization of operator allocation problem with learning effects and server breakdown under uncertainty |
| title_short | Simulation optimization of operator allocation problem with learning effects and server breakdown under uncertainty |
| title_sort | simulation optimization of operator allocation problem with learning effects and server breakdown under uncertainty |
| topic | Operator allocation Cellular Manufacturing System (CMS) learning effect server breakdown Fuzzy Simulation Response Surface Methodology (RSM) |
| url | http://dx.doi.org/10.1080/21693277.2018.1531080 |
| work_keys_str_mv | AT delaramheydarian simulationoptimizationofoperatorallocationproblemwithlearningeffectsandserverbreakdownunderuncertainty AT fariborzjolai simulationoptimizationofoperatorallocationproblemwithlearningeffectsandserverbreakdownunderuncertainty |