Knowledge-Driven Multi-Objective Optimization for Reconfigurable Manufacturing Systems
Current market requirements force manufacturing companies to face production changes more often than ever before. Reconfigurable manufacturing systems (RMS) are considered a key enabler in today’s manufacturing industry to cope with such dynamic and volatile markets. The literature confirms that the...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Mathematical and Computational Applications |
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| Online Access: | https://www.mdpi.com/2297-8747/27/6/106 |
| _version_ | 1797456462177370112 |
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| author | Henrik Smedberg Carlos Alberto Barrera-Diaz Amir Nourmohammadi Sunith Bandaru Amos H. C. Ng |
| author_facet | Henrik Smedberg Carlos Alberto Barrera-Diaz Amir Nourmohammadi Sunith Bandaru Amos H. C. Ng |
| author_sort | Henrik Smedberg |
| collection | DOAJ |
| description | Current market requirements force manufacturing companies to face production changes more often than ever before. Reconfigurable manufacturing systems (RMS) are considered a key enabler in today’s manufacturing industry to cope with such dynamic and volatile markets. The literature confirms that the use of simulation-based multi-objective optimization offers a promising approach that leads to improvements in RMS. However, due to the dynamic behavior of real-world RMS, applying conventional optimization approaches can be very time-consuming, specifically when there is no general knowledge about the quality of solutions. Meanwhile, Pareto-optimal solutions may share some common design principles that can be discovered with data mining and machine learning methods and exploited by the optimization. In this study, the authors investigate a novel knowledge-driven optimization (KDO) approach to speed up the convergence in RMS applications. This approach generates generalized knowledge from previous scenarios, which is then applied to improve the efficiency of the optimization of new scenarios. This study applied the proposed approach to a multi-part flow line RMS that considers scalable capacities while addressing the tasks assignment to workstations and the buffer allocation problems. The results demonstrate how a KDO approach leads to convergence rate improvements in a real-world RMS case. |
| first_indexed | 2024-03-09T16:08:10Z |
| format | Article |
| id | doaj.art-786b3f0c557e43b2bd3adb1611ba7387 |
| institution | Directory Open Access Journal |
| issn | 1300-686X 2297-8747 |
| language | English |
| last_indexed | 2024-03-09T16:08:10Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematical and Computational Applications |
| spelling | doaj.art-786b3f0c557e43b2bd3adb1611ba73872023-11-24T16:30:58ZengMDPI AGMathematical and Computational Applications1300-686X2297-87472022-12-0127610610.3390/mca27060106Knowledge-Driven Multi-Objective Optimization for Reconfigurable Manufacturing SystemsHenrik Smedberg0Carlos Alberto Barrera-Diaz1Amir Nourmohammadi2Sunith Bandaru3Amos H. C. Ng4Division of Intelligent Production Systems, School of Engineering Science, University of Skövde, P.O. Box 408, 54128 Skövde, SwedenDivision of Intelligent Production Systems, School of Engineering Science, University of Skövde, P.O. Box 408, 54128 Skövde, SwedenDivision of Intelligent Production Systems, School of Engineering Science, University of Skövde, P.O. Box 408, 54128 Skövde, SwedenDivision of Intelligent Production Systems, School of Engineering Science, University of Skövde, P.O. Box 408, 54128 Skövde, SwedenDivision of Intelligent Production Systems, School of Engineering Science, University of Skövde, P.O. Box 408, 54128 Skövde, SwedenCurrent market requirements force manufacturing companies to face production changes more often than ever before. Reconfigurable manufacturing systems (RMS) are considered a key enabler in today’s manufacturing industry to cope with such dynamic and volatile markets. The literature confirms that the use of simulation-based multi-objective optimization offers a promising approach that leads to improvements in RMS. However, due to the dynamic behavior of real-world RMS, applying conventional optimization approaches can be very time-consuming, specifically when there is no general knowledge about the quality of solutions. Meanwhile, Pareto-optimal solutions may share some common design principles that can be discovered with data mining and machine learning methods and exploited by the optimization. In this study, the authors investigate a novel knowledge-driven optimization (KDO) approach to speed up the convergence in RMS applications. This approach generates generalized knowledge from previous scenarios, which is then applied to improve the efficiency of the optimization of new scenarios. This study applied the proposed approach to a multi-part flow line RMS that considers scalable capacities while addressing the tasks assignment to workstations and the buffer allocation problems. The results demonstrate how a KDO approach leads to convergence rate improvements in a real-world RMS case.https://www.mdpi.com/2297-8747/27/6/106multi-objective optimizationknowledge discoveryreconfigurable manufacturing systemsimulation |
| spellingShingle | Henrik Smedberg Carlos Alberto Barrera-Diaz Amir Nourmohammadi Sunith Bandaru Amos H. C. Ng Knowledge-Driven Multi-Objective Optimization for Reconfigurable Manufacturing Systems Mathematical and Computational Applications multi-objective optimization knowledge discovery reconfigurable manufacturing system simulation |
| title | Knowledge-Driven Multi-Objective Optimization for Reconfigurable Manufacturing Systems |
| title_full | Knowledge-Driven Multi-Objective Optimization for Reconfigurable Manufacturing Systems |
| title_fullStr | Knowledge-Driven Multi-Objective Optimization for Reconfigurable Manufacturing Systems |
| title_full_unstemmed | Knowledge-Driven Multi-Objective Optimization for Reconfigurable Manufacturing Systems |
| title_short | Knowledge-Driven Multi-Objective Optimization for Reconfigurable Manufacturing Systems |
| title_sort | knowledge driven multi objective optimization for reconfigurable manufacturing systems |
| topic | multi-objective optimization knowledge discovery reconfigurable manufacturing system simulation |
| url | https://www.mdpi.com/2297-8747/27/6/106 |
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