Simulation and Genetic Algorithms to Improve the Performance of an Automated Manufacturing Line
Simulation in manufacturing is often applied in situations where conducting experiments on a real system is very difficult often because of cost or the time to carry out the experiment is too long. Optimization is the organized search for such designs and operating modes to find the best available s...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Széchenyi István University
2022-08-01
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| Series: | Acta Technica Jaurinensis |
| Subjects: | |
| Online Access: | https://acta.sze.hu/index.php/acta/article/view/668 |
| _version_ | 1828417837728792576 |
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| author | Patrick Ruane Patrick Walsh John Cosgrove |
| author_facet | Patrick Ruane Patrick Walsh John Cosgrove |
| author_sort | Patrick Ruane |
| collection | DOAJ |
| description | Simulation in manufacturing is often applied in situations where conducting experiments on a real system is very difficult often because of cost or the time to carry out the experiment is too long. Optimization is the organized search for such designs and operating modes to find the best available solution from a set of feasible solutions. It determines the set of actions or elements that must be implemented to achieve an optimized manufacturing line. As a result of being able to concurrently simulate and optimize equipment processes, the understanding of how the actual production system will perform under varying conditions is achieved. The author has adopted an open-source simulation tool (JaamSim) to develop a digital model of an automated tray loader manufacturing system in the Johnson & Johnson Vision Care (JJVC) manufacturing facility. This paper demonstrates how a digital model developed using JaamSim was integrated with an author developed genetic algorithm optimization system and how both tools can be used for the optimization and development of an automated manufacturing line in the medical devices industry. |
| first_indexed | 2024-12-10T14:26:49Z |
| format | Article |
| id | doaj.art-1ea0e29dbc8142208f55593312f9293c |
| institution | Directory Open Access Journal |
| issn | 2064-5228 |
| language | English |
| last_indexed | 2024-12-10T14:26:49Z |
| publishDate | 2022-08-01 |
| publisher | Széchenyi István University |
| record_format | Article |
| series | Acta Technica Jaurinensis |
| spelling | doaj.art-1ea0e29dbc8142208f55593312f9293c2022-12-22T01:45:03ZengSzéchenyi István UniversityActa Technica Jaurinensis2064-52282022-08-0115317418710.14513/actatechjaur.00668596Simulation and Genetic Algorithms to Improve the Performance of an Automated Manufacturing LinePatrick Ruane0Patrick Walsh1John Cosgrove2Johnson & Johnson Vision Care, Rivers, V94 N732 Limerick, Ireland // Technological University of the Shannon, Moylish, V94 EC5T, Limerick, IrelandTechnological University of the Shannon, Moylish, V94 EC5T, Limerick, IrelandTechnological University of the Shannon, Moylish, V94 EC5T, Limerick, IrelandSimulation in manufacturing is often applied in situations where conducting experiments on a real system is very difficult often because of cost or the time to carry out the experiment is too long. Optimization is the organized search for such designs and operating modes to find the best available solution from a set of feasible solutions. It determines the set of actions or elements that must be implemented to achieve an optimized manufacturing line. As a result of being able to concurrently simulate and optimize equipment processes, the understanding of how the actual production system will perform under varying conditions is achieved. The author has adopted an open-source simulation tool (JaamSim) to develop a digital model of an automated tray loader manufacturing system in the Johnson & Johnson Vision Care (JJVC) manufacturing facility. This paper demonstrates how a digital model developed using JaamSim was integrated with an author developed genetic algorithm optimization system and how both tools can be used for the optimization and development of an automated manufacturing line in the medical devices industry.https://acta.sze.hu/index.php/acta/article/view/668digital modeldigitalizationgenetic algorithmjaamsimoptimizationsimulation |
| spellingShingle | Patrick Ruane Patrick Walsh John Cosgrove Simulation and Genetic Algorithms to Improve the Performance of an Automated Manufacturing Line Acta Technica Jaurinensis digital model digitalization genetic algorithm jaamsim optimization simulation |
| title | Simulation and Genetic Algorithms to Improve the Performance of an Automated Manufacturing Line |
| title_full | Simulation and Genetic Algorithms to Improve the Performance of an Automated Manufacturing Line |
| title_fullStr | Simulation and Genetic Algorithms to Improve the Performance of an Automated Manufacturing Line |
| title_full_unstemmed | Simulation and Genetic Algorithms to Improve the Performance of an Automated Manufacturing Line |
| title_short | Simulation and Genetic Algorithms to Improve the Performance of an Automated Manufacturing Line |
| title_sort | simulation and genetic algorithms to improve the performance of an automated manufacturing line |
| topic | digital model digitalization genetic algorithm jaamsim optimization simulation |
| url | https://acta.sze.hu/index.php/acta/article/view/668 |
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