Scalability in manufacturing systems: a hybridized GA approach
Abstract As one of the key characteristics in manufacturing systems, scalability plays an increasingly important role that is driven by the rapid change of market demand. It provides the ability to rapidly reconfigure production capacity in a cost-effective manner under different situations. Our ind...
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Gespeichert in:
| Autor*in: |
Shao, Huan [verfasserIn] |
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Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC 2017 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of intelligent manufacturing - Springer US, 1990, 30(2017), 4 vom: 04. Sept., Seite 1859-1879 |
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| Übergeordnetes Werk: |
volume:30 ; year:2017 ; number:4 ; day:04 ; month:09 ; pages:1859-1879 |
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10.1007/s10845-017-1352-0 |
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| 520 | |a Abstract As one of the key characteristics in manufacturing systems, scalability plays an increasingly important role that is driven by the rapid change of market demand. It provides the ability to rapidly reconfigure production capacity in a cost-effective manner under different situations. Our industrial partners face scalability problems involving multi-unit and multi-product manufacturing systems. In this paper, a hybridized genetic algorithm (GA) approach is presented to solve these kinds of problems. A mathematical model is defined by considering technological and capacity as well as industrial constraints. Starting from the original process plan and configuration of the manufacturing system, a set of practical principles are built to reduce the time associated with finding a feasible solution. An improved GA is proposed to search in the global solution space; the method is hybridized with a heuristic approach to locally improve the solution between generations. A balancing objective function is defined and used to rank the solutions. Experiments are set to determine the most adequate parameters of the algorithm. An industrial case study demonstrates the validity of the proposed approach. | ||
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10.1007/s10845-017-1352-0 doi (DE-627)OLC2066778745 (DE-He213)s10845-017-1352-0-p DE-627 ger DE-627 rakwb eng 620 004 VZ Shao, Huan verfasserin aut Scalability in manufacturing systems: a hybridized GA approach 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2017 Abstract As one of the key characteristics in manufacturing systems, scalability plays an increasingly important role that is driven by the rapid change of market demand. It provides the ability to rapidly reconfigure production capacity in a cost-effective manner under different situations. Our industrial partners face scalability problems involving multi-unit and multi-product manufacturing systems. In this paper, a hybridized genetic algorithm (GA) approach is presented to solve these kinds of problems. A mathematical model is defined by considering technological and capacity as well as industrial constraints. Starting from the original process plan and configuration of the manufacturing system, a set of practical principles are built to reduce the time associated with finding a feasible solution. An improved GA is proposed to search in the global solution space; the method is hybridized with a heuristic approach to locally improve the solution between generations. A balancing objective function is defined and used to rank the solutions. Experiments are set to determine the most adequate parameters of the algorithm. An industrial case study demonstrates the validity of the proposed approach. Manufacturing system Reconfiguration Scalability Genetic algorithm Industrial case study Li, Aiping aut Xu, Liyun aut Moroni, Giovanni (orcid)0000-0002-7621-3382 aut Enthalten in Journal of intelligent manufacturing Springer US, 1990 30(2017), 4 vom: 04. Sept., Seite 1859-1879 (DE-627)130892815 (DE-600)1041378-9 (DE-576)026321106 0956-5515 nnns volume:30 year:2017 number:4 day:04 month:09 pages:1859-1879 https://doi.org/10.1007/s10845-017-1352-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 AR 30 2017 4 04 09 1859-1879 |
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10.1007/s10845-017-1352-0 doi (DE-627)OLC2066778745 (DE-He213)s10845-017-1352-0-p DE-627 ger DE-627 rakwb eng 620 004 VZ Shao, Huan verfasserin aut Scalability in manufacturing systems: a hybridized GA approach 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2017 Abstract As one of the key characteristics in manufacturing systems, scalability plays an increasingly important role that is driven by the rapid change of market demand. It provides the ability to rapidly reconfigure production capacity in a cost-effective manner under different situations. Our industrial partners face scalability problems involving multi-unit and multi-product manufacturing systems. In this paper, a hybridized genetic algorithm (GA) approach is presented to solve these kinds of problems. A mathematical model is defined by considering technological and capacity as well as industrial constraints. Starting from the original process plan and configuration of the manufacturing system, a set of practical principles are built to reduce the time associated with finding a feasible solution. An improved GA is proposed to search in the global solution space; the method is hybridized with a heuristic approach to locally improve the solution between generations. A balancing objective function is defined and used to rank the solutions. Experiments are set to determine the most adequate parameters of the algorithm. An industrial case study demonstrates the validity of the proposed approach. Manufacturing system Reconfiguration Scalability Genetic algorithm Industrial case study Li, Aiping aut Xu, Liyun aut Moroni, Giovanni (orcid)0000-0002-7621-3382 aut Enthalten in Journal of intelligent manufacturing Springer US, 1990 30(2017), 4 vom: 04. Sept., Seite 1859-1879 (DE-627)130892815 (DE-600)1041378-9 (DE-576)026321106 0956-5515 nnns volume:30 year:2017 number:4 day:04 month:09 pages:1859-1879 https://doi.org/10.1007/s10845-017-1352-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 AR 30 2017 4 04 09 1859-1879 |
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10.1007/s10845-017-1352-0 doi (DE-627)OLC2066778745 (DE-He213)s10845-017-1352-0-p DE-627 ger DE-627 rakwb eng 620 004 VZ Shao, Huan verfasserin aut Scalability in manufacturing systems: a hybridized GA approach 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2017 Abstract As one of the key characteristics in manufacturing systems, scalability plays an increasingly important role that is driven by the rapid change of market demand. It provides the ability to rapidly reconfigure production capacity in a cost-effective manner under different situations. Our industrial partners face scalability problems involving multi-unit and multi-product manufacturing systems. In this paper, a hybridized genetic algorithm (GA) approach is presented to solve these kinds of problems. A mathematical model is defined by considering technological and capacity as well as industrial constraints. Starting from the original process plan and configuration of the manufacturing system, a set of practical principles are built to reduce the time associated with finding a feasible solution. An improved GA is proposed to search in the global solution space; the method is hybridized with a heuristic approach to locally improve the solution between generations. A balancing objective function is defined and used to rank the solutions. Experiments are set to determine the most adequate parameters of the algorithm. An industrial case study demonstrates the validity of the proposed approach. Manufacturing system Reconfiguration Scalability Genetic algorithm Industrial case study Li, Aiping aut Xu, Liyun aut Moroni, Giovanni (orcid)0000-0002-7621-3382 aut Enthalten in Journal of intelligent manufacturing Springer US, 1990 30(2017), 4 vom: 04. Sept., Seite 1859-1879 (DE-627)130892815 (DE-600)1041378-9 (DE-576)026321106 0956-5515 nnns volume:30 year:2017 number:4 day:04 month:09 pages:1859-1879 https://doi.org/10.1007/s10845-017-1352-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_70 AR 30 2017 4 04 09 1859-1879 |
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Abstract As one of the key characteristics in manufacturing systems, scalability plays an increasingly important role that is driven by the rapid change of market demand. It provides the ability to rapidly reconfigure production capacity in a cost-effective manner under different situations. Our industrial partners face scalability problems involving multi-unit and multi-product manufacturing systems. In this paper, a hybridized genetic algorithm (GA) approach is presented to solve these kinds of problems. A mathematical model is defined by considering technological and capacity as well as industrial constraints. Starting from the original process plan and configuration of the manufacturing system, a set of practical principles are built to reduce the time associated with finding a feasible solution. An improved GA is proposed to search in the global solution space; the method is hybridized with a heuristic approach to locally improve the solution between generations. A balancing objective function is defined and used to rank the solutions. Experiments are set to determine the most adequate parameters of the algorithm. An industrial case study demonstrates the validity of the proposed approach. © Springer Science+Business Media, LLC 2017 |
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Abstract As one of the key characteristics in manufacturing systems, scalability plays an increasingly important role that is driven by the rapid change of market demand. It provides the ability to rapidly reconfigure production capacity in a cost-effective manner under different situations. Our industrial partners face scalability problems involving multi-unit and multi-product manufacturing systems. In this paper, a hybridized genetic algorithm (GA) approach is presented to solve these kinds of problems. A mathematical model is defined by considering technological and capacity as well as industrial constraints. Starting from the original process plan and configuration of the manufacturing system, a set of practical principles are built to reduce the time associated with finding a feasible solution. An improved GA is proposed to search in the global solution space; the method is hybridized with a heuristic approach to locally improve the solution between generations. A balancing objective function is defined and used to rank the solutions. Experiments are set to determine the most adequate parameters of the algorithm. An industrial case study demonstrates the validity of the proposed approach. © Springer Science+Business Media, LLC 2017 |
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Abstract As one of the key characteristics in manufacturing systems, scalability plays an increasingly important role that is driven by the rapid change of market demand. It provides the ability to rapidly reconfigure production capacity in a cost-effective manner under different situations. Our industrial partners face scalability problems involving multi-unit and multi-product manufacturing systems. In this paper, a hybridized genetic algorithm (GA) approach is presented to solve these kinds of problems. A mathematical model is defined by considering technological and capacity as well as industrial constraints. Starting from the original process plan and configuration of the manufacturing system, a set of practical principles are built to reduce the time associated with finding a feasible solution. An improved GA is proposed to search in the global solution space; the method is hybridized with a heuristic approach to locally improve the solution between generations. A balancing objective function is defined and used to rank the solutions. Experiments are set to determine the most adequate parameters of the algorithm. An industrial case study demonstrates the validity of the proposed approach. © Springer Science+Business Media, LLC 2017 |
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Scalability in manufacturing systems: a hybridized GA approach |
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Li, Aiping Xu, Liyun Moroni, Giovanni |
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