Development of a genetic algorithm for multi-objective assembly line balancing using multiple assignment approach
Abstract In this study, a new genetic algorithm is developed for solving multi-objective single-model assembly line balancing problems. The proposed genetic algorithm is called multiple-assignment genetic algorithm (MA-GA) which presents a new approach of tasks assignment. In addition to the forward...
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| Autor*in: |
Al-Hawari, Tarek [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag London 2014 |
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| Übergeordnetes Werk: |
Enthalten in: The international journal of advanced manufacturing technology - Springer London, 1985, 77(2014), 5-8 vom: 11. Nov., Seite 1419-1432 |
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| Übergeordnetes Werk: |
volume:77 ; year:2014 ; number:5-8 ; day:11 ; month:11 ; pages:1419-1432 |
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| DOI / URN: |
10.1007/s00170-014-6545-5 |
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OLC2026068739 |
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| 520 | |a Abstract In this study, a new genetic algorithm is developed for solving multi-objective single-model assembly line balancing problems. The proposed genetic algorithm is called multiple-assignment genetic algorithm (MA-GA) which presents a new approach of tasks assignment. In addition to the forward assignment approach, backward and bidirectional assignment approaches are used with genetic algorithms to develop alternative assignment procedures that can result in improving the potential of finding better solutions. This approach has not been used before along with GAs in solving assembly line balancing (ALB) problems. The objectives considered here are minimization of number of workstations, maximization of assembly line efficiency, and minimization of workload variation between workstations. The performance of the proposed algorithm MA-GA was compared with GAs that only use the forward assignment approach called Forward-GAs. The proposed MA-GA has shown improved results in assembly line efficiency and workload variation between workstations in many test problems taken from the literature while achieving the optimal number of workstations in all test problems. | ||
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10.1007/s00170-014-6545-5 doi (DE-627)OLC2026068739 (DE-He213)s00170-014-6545-5-p DE-627 ger DE-627 rakwb eng 670 VZ Al-Hawari, Tarek verfasserin aut Development of a genetic algorithm for multi-objective assembly line balancing using multiple assignment approach 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2014 Abstract In this study, a new genetic algorithm is developed for solving multi-objective single-model assembly line balancing problems. The proposed genetic algorithm is called multiple-assignment genetic algorithm (MA-GA) which presents a new approach of tasks assignment. In addition to the forward assignment approach, backward and bidirectional assignment approaches are used with genetic algorithms to develop alternative assignment procedures that can result in improving the potential of finding better solutions. This approach has not been used before along with GAs in solving assembly line balancing (ALB) problems. The objectives considered here are minimization of number of workstations, maximization of assembly line efficiency, and minimization of workload variation between workstations. The performance of the proposed algorithm MA-GA was compared with GAs that only use the forward assignment approach called Forward-GAs. The proposed MA-GA has shown improved results in assembly line efficiency and workload variation between workstations in many test problems taken from the literature while achieving the optimal number of workstations in all test problems. Assembly line balancing Multi-objective optimization Genetic algorithm Ali, Marwan aut Al-Araidah, Omar aut Mumani, Ahmad aut Enthalten in The international journal of advanced manufacturing technology Springer London, 1985 77(2014), 5-8 vom: 11. Nov., Seite 1419-1432 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:77 year:2014 number:5-8 day:11 month:11 pages:1419-1432 https://doi.org/10.1007/s00170-014-6545-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_20 GBV_ILN_70 GBV_ILN_150 GBV_ILN_2018 GBV_ILN_2333 AR 77 2014 5-8 11 11 1419-1432 |
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10.1007/s00170-014-6545-5 doi (DE-627)OLC2026068739 (DE-He213)s00170-014-6545-5-p DE-627 ger DE-627 rakwb eng 670 VZ Al-Hawari, Tarek verfasserin aut Development of a genetic algorithm for multi-objective assembly line balancing using multiple assignment approach 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2014 Abstract In this study, a new genetic algorithm is developed for solving multi-objective single-model assembly line balancing problems. The proposed genetic algorithm is called multiple-assignment genetic algorithm (MA-GA) which presents a new approach of tasks assignment. In addition to the forward assignment approach, backward and bidirectional assignment approaches are used with genetic algorithms to develop alternative assignment procedures that can result in improving the potential of finding better solutions. This approach has not been used before along with GAs in solving assembly line balancing (ALB) problems. The objectives considered here are minimization of number of workstations, maximization of assembly line efficiency, and minimization of workload variation between workstations. The performance of the proposed algorithm MA-GA was compared with GAs that only use the forward assignment approach called Forward-GAs. The proposed MA-GA has shown improved results in assembly line efficiency and workload variation between workstations in many test problems taken from the literature while achieving the optimal number of workstations in all test problems. Assembly line balancing Multi-objective optimization Genetic algorithm Ali, Marwan aut Al-Araidah, Omar aut Mumani, Ahmad aut Enthalten in The international journal of advanced manufacturing technology Springer London, 1985 77(2014), 5-8 vom: 11. Nov., Seite 1419-1432 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:77 year:2014 number:5-8 day:11 month:11 pages:1419-1432 https://doi.org/10.1007/s00170-014-6545-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_20 GBV_ILN_70 GBV_ILN_150 GBV_ILN_2018 GBV_ILN_2333 AR 77 2014 5-8 11 11 1419-1432 |
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10.1007/s00170-014-6545-5 doi (DE-627)OLC2026068739 (DE-He213)s00170-014-6545-5-p DE-627 ger DE-627 rakwb eng 670 VZ Al-Hawari, Tarek verfasserin aut Development of a genetic algorithm for multi-objective assembly line balancing using multiple assignment approach 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2014 Abstract In this study, a new genetic algorithm is developed for solving multi-objective single-model assembly line balancing problems. The proposed genetic algorithm is called multiple-assignment genetic algorithm (MA-GA) which presents a new approach of tasks assignment. In addition to the forward assignment approach, backward and bidirectional assignment approaches are used with genetic algorithms to develop alternative assignment procedures that can result in improving the potential of finding better solutions. This approach has not been used before along with GAs in solving assembly line balancing (ALB) problems. The objectives considered here are minimization of number of workstations, maximization of assembly line efficiency, and minimization of workload variation between workstations. The performance of the proposed algorithm MA-GA was compared with GAs that only use the forward assignment approach called Forward-GAs. The proposed MA-GA has shown improved results in assembly line efficiency and workload variation between workstations in many test problems taken from the literature while achieving the optimal number of workstations in all test problems. Assembly line balancing Multi-objective optimization Genetic algorithm Ali, Marwan aut Al-Araidah, Omar aut Mumani, Ahmad aut Enthalten in The international journal of advanced manufacturing technology Springer London, 1985 77(2014), 5-8 vom: 11. Nov., Seite 1419-1432 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:77 year:2014 number:5-8 day:11 month:11 pages:1419-1432 https://doi.org/10.1007/s00170-014-6545-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_20 GBV_ILN_70 GBV_ILN_150 GBV_ILN_2018 GBV_ILN_2333 AR 77 2014 5-8 11 11 1419-1432 |
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10.1007/s00170-014-6545-5 doi (DE-627)OLC2026068739 (DE-He213)s00170-014-6545-5-p DE-627 ger DE-627 rakwb eng 670 VZ Al-Hawari, Tarek verfasserin aut Development of a genetic algorithm for multi-objective assembly line balancing using multiple assignment approach 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2014 Abstract In this study, a new genetic algorithm is developed for solving multi-objective single-model assembly line balancing problems. The proposed genetic algorithm is called multiple-assignment genetic algorithm (MA-GA) which presents a new approach of tasks assignment. In addition to the forward assignment approach, backward and bidirectional assignment approaches are used with genetic algorithms to develop alternative assignment procedures that can result in improving the potential of finding better solutions. This approach has not been used before along with GAs in solving assembly line balancing (ALB) problems. The objectives considered here are minimization of number of workstations, maximization of assembly line efficiency, and minimization of workload variation between workstations. The performance of the proposed algorithm MA-GA was compared with GAs that only use the forward assignment approach called Forward-GAs. The proposed MA-GA has shown improved results in assembly line efficiency and workload variation between workstations in many test problems taken from the literature while achieving the optimal number of workstations in all test problems. Assembly line balancing Multi-objective optimization Genetic algorithm Ali, Marwan aut Al-Araidah, Omar aut Mumani, Ahmad aut Enthalten in The international journal of advanced manufacturing technology Springer London, 1985 77(2014), 5-8 vom: 11. Nov., Seite 1419-1432 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:77 year:2014 number:5-8 day:11 month:11 pages:1419-1432 https://doi.org/10.1007/s00170-014-6545-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_20 GBV_ILN_70 GBV_ILN_150 GBV_ILN_2018 GBV_ILN_2333 AR 77 2014 5-8 11 11 1419-1432 |
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10.1007/s00170-014-6545-5 doi (DE-627)OLC2026068739 (DE-He213)s00170-014-6545-5-p DE-627 ger DE-627 rakwb eng 670 VZ Al-Hawari, Tarek verfasserin aut Development of a genetic algorithm for multi-objective assembly line balancing using multiple assignment approach 2014 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London 2014 Abstract In this study, a new genetic algorithm is developed for solving multi-objective single-model assembly line balancing problems. The proposed genetic algorithm is called multiple-assignment genetic algorithm (MA-GA) which presents a new approach of tasks assignment. In addition to the forward assignment approach, backward and bidirectional assignment approaches are used with genetic algorithms to develop alternative assignment procedures that can result in improving the potential of finding better solutions. This approach has not been used before along with GAs in solving assembly line balancing (ALB) problems. The objectives considered here are minimization of number of workstations, maximization of assembly line efficiency, and minimization of workload variation between workstations. The performance of the proposed algorithm MA-GA was compared with GAs that only use the forward assignment approach called Forward-GAs. The proposed MA-GA has shown improved results in assembly line efficiency and workload variation between workstations in many test problems taken from the literature while achieving the optimal number of workstations in all test problems. Assembly line balancing Multi-objective optimization Genetic algorithm Ali, Marwan aut Al-Araidah, Omar aut Mumani, Ahmad aut Enthalten in The international journal of advanced manufacturing technology Springer London, 1985 77(2014), 5-8 vom: 11. Nov., Seite 1419-1432 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:77 year:2014 number:5-8 day:11 month:11 pages:1419-1432 https://doi.org/10.1007/s00170-014-6545-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_20 GBV_ILN_70 GBV_ILN_150 GBV_ILN_2018 GBV_ILN_2333 AR 77 2014 5-8 11 11 1419-1432 |
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Development of a genetic algorithm for multi-objective assembly line balancing using multiple assignment approach |
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Abstract In this study, a new genetic algorithm is developed for solving multi-objective single-model assembly line balancing problems. The proposed genetic algorithm is called multiple-assignment genetic algorithm (MA-GA) which presents a new approach of tasks assignment. In addition to the forward assignment approach, backward and bidirectional assignment approaches are used with genetic algorithms to develop alternative assignment procedures that can result in improving the potential of finding better solutions. This approach has not been used before along with GAs in solving assembly line balancing (ALB) problems. The objectives considered here are minimization of number of workstations, maximization of assembly line efficiency, and minimization of workload variation between workstations. The performance of the proposed algorithm MA-GA was compared with GAs that only use the forward assignment approach called Forward-GAs. The proposed MA-GA has shown improved results in assembly line efficiency and workload variation between workstations in many test problems taken from the literature while achieving the optimal number of workstations in all test problems. © Springer-Verlag London 2014 |
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Abstract In this study, a new genetic algorithm is developed for solving multi-objective single-model assembly line balancing problems. The proposed genetic algorithm is called multiple-assignment genetic algorithm (MA-GA) which presents a new approach of tasks assignment. In addition to the forward assignment approach, backward and bidirectional assignment approaches are used with genetic algorithms to develop alternative assignment procedures that can result in improving the potential of finding better solutions. This approach has not been used before along with GAs in solving assembly line balancing (ALB) problems. The objectives considered here are minimization of number of workstations, maximization of assembly line efficiency, and minimization of workload variation between workstations. The performance of the proposed algorithm MA-GA was compared with GAs that only use the forward assignment approach called Forward-GAs. The proposed MA-GA has shown improved results in assembly line efficiency and workload variation between workstations in many test problems taken from the literature while achieving the optimal number of workstations in all test problems. © Springer-Verlag London 2014 |
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Abstract In this study, a new genetic algorithm is developed for solving multi-objective single-model assembly line balancing problems. The proposed genetic algorithm is called multiple-assignment genetic algorithm (MA-GA) which presents a new approach of tasks assignment. In addition to the forward assignment approach, backward and bidirectional assignment approaches are used with genetic algorithms to develop alternative assignment procedures that can result in improving the potential of finding better solutions. This approach has not been used before along with GAs in solving assembly line balancing (ALB) problems. The objectives considered here are minimization of number of workstations, maximization of assembly line efficiency, and minimization of workload variation between workstations. The performance of the proposed algorithm MA-GA was compared with GAs that only use the forward assignment approach called Forward-GAs. The proposed MA-GA has shown improved results in assembly line efficiency and workload variation between workstations in many test problems taken from the literature while achieving the optimal number of workstations in all test problems. © Springer-Verlag London 2014 |
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Development of a genetic algorithm for multi-objective assembly line balancing using multiple assignment approach |
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Ali, Marwan Al-Araidah, Omar Mumani, Ahmad |
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