A multi-objective reinforcement learning approach for resequencing scheduling problems in automotive manufacturing systems
Gespeichert in:
| Autor*in: |
Leng, Jinling [verfasserIn] Wang, Xingyuan [verfasserIn] Wu, Shiping [verfasserIn] Jin, Chun [verfasserIn] Tang, Meng [verfasserIn] Liu, Rui [verfasserIn] Vogl, Alexander [verfasserIn] Liu, Huiyu [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: International journal of production research - London [u.a.] : Taylor & Francis, 1996, 61(2023), 15, Seite 5156-5175 |
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| Übergeordnetes Werk: |
volume:61 ; year:2023 ; number:15 ; pages:5156-5175 |
| Links: |
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| DOI / URN: |
10.1080/00207543.2022.2098871 |
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| Katalog-ID: |
1866192078 |
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| 982 | |2 26 |1 00 |x DE-206 |b This study investigated a multi-objective resequencing scheduling problem in the automotive manufacturing systems due to operational requirements on the color-batching of the paint shop and sequential requirements on the sequence adherence of an assembly shop. Resequencing cars as color-oriented batches reduced the costs of color changes and operational costs for paint shops. Also, assembly shops required paint shops to complete cars with fewer delays so that high sequence adherence with its demand was assured. Based on real-world applications, we investigated two contradictory objectives-color change costs and sequence tardiness-in a single-machine flowshop scheduling environment. A multi-objective-deep-Q-network algorithm was developed to determine the Pareto frontier. Reward shaping was designed to improve the convergence of the neural network. The 2D-folded-normal distribution was designed to sample the preference, which made the exploration and exploitation of the neural network more comprehensive and improved the training efficiency. Two experiments were conducted and showed that the proposed approach outperformed the meta-heuristic algorithm and the envelope Q-learning algorithm in solving time, performance, the convergence of the neural network, and the diversity of the Pareto frontier. Therefore, the proposed approach can be used in automotive paint shops to improve scheduling efficiency and reduce operational costs. | ||
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10.1080/00207543.2022.2098871 doi (DE-627)1866192078 (DE-599)KXP1866192078 DE-627 ger DE-627 rda eng Leng, Jinling verfasserin (DE-588)1307604617 (DE-627)1868623785 aut A multi-objective reinforcement learning approach for resequencing scheduling problems in automotive manufacturing systems Jinling Leng, Xingyuan Wang, Shiping Wu, Chun Jin, Meng Tang, Rui Liu, Alexander Vogl, Huiyu Liu 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier color-batching (dpeaa)DE-206 multi-objective optimization (dpeaa)DE-206 multi-objective reinforcement learning (dpeaa)DE-206 Scheduling (dpeaa)DE-206 sequence adherence (dpeaa)DE-206 Wang, Xingyuan verfasserin (DE-588)1307604684 (DE-627)1868623823 aut Wu, Shiping verfasserin (DE-588)1307604765 (DE-627)1868623874 aut Jin, Chun verfasserin (DE-588)1307605168 (DE-627)1868624196 aut Tang, Meng verfasserin (DE-588)1307605249 (DE-627)1868624234 aut Liu, Rui verfasserin (DE-588)1307605346 (DE-627)1868624285 aut Vogl, Alexander verfasserin (DE-588)1307605702 (DE-627)1868624587 aut Liu, Huiyu verfasserin (DE-588)1307605494 (DE-627)1868624455 aut Enthalten in International journal of production research London [u.a.] : Taylor & Francis, 1996 61(2023), 15, Seite 5156-5175 Online-Ressource (DE-627)301516731 (DE-600)1485085-0 (DE-576)094115516 1366-588X nnns volume:61 year:2023 number:15 pages:5156-5175 https://www.tandfonline.com/doi/pdf/10.1080/00207543.2022.2098871 Verlag lizenzpflichtig https://doi.org/10.1080/00207543.2022.2098871 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_110 GBV_ILN_120 GBV_ILN_152 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 61 2023 15 5156-5175 26 01 0206 4391995255 x1z 18-10-23 26 00 DE-206 This study investigated a multi-objective resequencing scheduling problem in the automotive manufacturing systems due to operational requirements on the color-batching of the paint shop and sequential requirements on the sequence adherence of an assembly shop. Resequencing cars as color-oriented batches reduced the costs of color changes and operational costs for paint shops. Also, assembly shops required paint shops to complete cars with fewer delays so that high sequence adherence with its demand was assured. Based on real-world applications, we investigated two contradictory objectives-color change costs and sequence tardiness-in a single-machine flowshop scheduling environment. A multi-objective-deep-Q-network algorithm was developed to determine the Pareto frontier. Reward shaping was designed to improve the convergence of the neural network. The 2D-folded-normal distribution was designed to sample the preference, which made the exploration and exploitation of the neural network more comprehensive and improved the training efficiency. Two experiments were conducted and showed that the proposed approach outperformed the meta-heuristic algorithm and the envelope Q-learning algorithm in solving time, performance, the convergence of the neural network, and the diversity of the Pareto frontier. Therefore, the proposed approach can be used in automotive paint shops to improve scheduling efficiency and reduce operational costs. |
| spelling |
10.1080/00207543.2022.2098871 doi (DE-627)1866192078 (DE-599)KXP1866192078 DE-627 ger DE-627 rda eng Leng, Jinling verfasserin (DE-588)1307604617 (DE-627)1868623785 aut A multi-objective reinforcement learning approach for resequencing scheduling problems in automotive manufacturing systems Jinling Leng, Xingyuan Wang, Shiping Wu, Chun Jin, Meng Tang, Rui Liu, Alexander Vogl, Huiyu Liu 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier color-batching (dpeaa)DE-206 multi-objective optimization (dpeaa)DE-206 multi-objective reinforcement learning (dpeaa)DE-206 Scheduling (dpeaa)DE-206 sequence adherence (dpeaa)DE-206 Wang, Xingyuan verfasserin (DE-588)1307604684 (DE-627)1868623823 aut Wu, Shiping verfasserin (DE-588)1307604765 (DE-627)1868623874 aut Jin, Chun verfasserin (DE-588)1307605168 (DE-627)1868624196 aut Tang, Meng verfasserin (DE-588)1307605249 (DE-627)1868624234 aut Liu, Rui verfasserin (DE-588)1307605346 (DE-627)1868624285 aut Vogl, Alexander verfasserin (DE-588)1307605702 (DE-627)1868624587 aut Liu, Huiyu verfasserin (DE-588)1307605494 (DE-627)1868624455 aut Enthalten in International journal of production research London [u.a.] : Taylor & Francis, 1996 61(2023), 15, Seite 5156-5175 Online-Ressource (DE-627)301516731 (DE-600)1485085-0 (DE-576)094115516 1366-588X nnns volume:61 year:2023 number:15 pages:5156-5175 https://www.tandfonline.com/doi/pdf/10.1080/00207543.2022.2098871 Verlag lizenzpflichtig https://doi.org/10.1080/00207543.2022.2098871 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_110 GBV_ILN_120 GBV_ILN_152 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 61 2023 15 5156-5175 26 01 0206 4391995255 x1z 18-10-23 26 00 DE-206 This study investigated a multi-objective resequencing scheduling problem in the automotive manufacturing systems due to operational requirements on the color-batching of the paint shop and sequential requirements on the sequence adherence of an assembly shop. Resequencing cars as color-oriented batches reduced the costs of color changes and operational costs for paint shops. Also, assembly shops required paint shops to complete cars with fewer delays so that high sequence adherence with its demand was assured. Based on real-world applications, we investigated two contradictory objectives-color change costs and sequence tardiness-in a single-machine flowshop scheduling environment. A multi-objective-deep-Q-network algorithm was developed to determine the Pareto frontier. Reward shaping was designed to improve the convergence of the neural network. The 2D-folded-normal distribution was designed to sample the preference, which made the exploration and exploitation of the neural network more comprehensive and improved the training efficiency. Two experiments were conducted and showed that the proposed approach outperformed the meta-heuristic algorithm and the envelope Q-learning algorithm in solving time, performance, the convergence of the neural network, and the diversity of the Pareto frontier. Therefore, the proposed approach can be used in automotive paint shops to improve scheduling efficiency and reduce operational costs. |
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10.1080/00207543.2022.2098871 doi (DE-627)1866192078 (DE-599)KXP1866192078 DE-627 ger DE-627 rda eng Leng, Jinling verfasserin (DE-588)1307604617 (DE-627)1868623785 aut A multi-objective reinforcement learning approach for resequencing scheduling problems in automotive manufacturing systems Jinling Leng, Xingyuan Wang, Shiping Wu, Chun Jin, Meng Tang, Rui Liu, Alexander Vogl, Huiyu Liu 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier color-batching (dpeaa)DE-206 multi-objective optimization (dpeaa)DE-206 multi-objective reinforcement learning (dpeaa)DE-206 Scheduling (dpeaa)DE-206 sequence adherence (dpeaa)DE-206 Wang, Xingyuan verfasserin (DE-588)1307604684 (DE-627)1868623823 aut Wu, Shiping verfasserin (DE-588)1307604765 (DE-627)1868623874 aut Jin, Chun verfasserin (DE-588)1307605168 (DE-627)1868624196 aut Tang, Meng verfasserin (DE-588)1307605249 (DE-627)1868624234 aut Liu, Rui verfasserin (DE-588)1307605346 (DE-627)1868624285 aut Vogl, Alexander verfasserin (DE-588)1307605702 (DE-627)1868624587 aut Liu, Huiyu verfasserin (DE-588)1307605494 (DE-627)1868624455 aut Enthalten in International journal of production research London [u.a.] : Taylor & Francis, 1996 61(2023), 15, Seite 5156-5175 Online-Ressource (DE-627)301516731 (DE-600)1485085-0 (DE-576)094115516 1366-588X nnns volume:61 year:2023 number:15 pages:5156-5175 https://www.tandfonline.com/doi/pdf/10.1080/00207543.2022.2098871 Verlag lizenzpflichtig https://doi.org/10.1080/00207543.2022.2098871 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_110 GBV_ILN_120 GBV_ILN_152 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 61 2023 15 5156-5175 26 01 0206 4391995255 x1z 18-10-23 26 00 DE-206 This study investigated a multi-objective resequencing scheduling problem in the automotive manufacturing systems due to operational requirements on the color-batching of the paint shop and sequential requirements on the sequence adherence of an assembly shop. Resequencing cars as color-oriented batches reduced the costs of color changes and operational costs for paint shops. Also, assembly shops required paint shops to complete cars with fewer delays so that high sequence adherence with its demand was assured. Based on real-world applications, we investigated two contradictory objectives-color change costs and sequence tardiness-in a single-machine flowshop scheduling environment. A multi-objective-deep-Q-network algorithm was developed to determine the Pareto frontier. Reward shaping was designed to improve the convergence of the neural network. The 2D-folded-normal distribution was designed to sample the preference, which made the exploration and exploitation of the neural network more comprehensive and improved the training efficiency. Two experiments were conducted and showed that the proposed approach outperformed the meta-heuristic algorithm and the envelope Q-learning algorithm in solving time, performance, the convergence of the neural network, and the diversity of the Pareto frontier. Therefore, the proposed approach can be used in automotive paint shops to improve scheduling efficiency and reduce operational costs. |
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10.1080/00207543.2022.2098871 doi (DE-627)1866192078 (DE-599)KXP1866192078 DE-627 ger DE-627 rda eng Leng, Jinling verfasserin (DE-588)1307604617 (DE-627)1868623785 aut A multi-objective reinforcement learning approach for resequencing scheduling problems in automotive manufacturing systems Jinling Leng, Xingyuan Wang, Shiping Wu, Chun Jin, Meng Tang, Rui Liu, Alexander Vogl, Huiyu Liu 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier color-batching (dpeaa)DE-206 multi-objective optimization (dpeaa)DE-206 multi-objective reinforcement learning (dpeaa)DE-206 Scheduling (dpeaa)DE-206 sequence adherence (dpeaa)DE-206 Wang, Xingyuan verfasserin (DE-588)1307604684 (DE-627)1868623823 aut Wu, Shiping verfasserin (DE-588)1307604765 (DE-627)1868623874 aut Jin, Chun verfasserin (DE-588)1307605168 (DE-627)1868624196 aut Tang, Meng verfasserin (DE-588)1307605249 (DE-627)1868624234 aut Liu, Rui verfasserin (DE-588)1307605346 (DE-627)1868624285 aut Vogl, Alexander verfasserin (DE-588)1307605702 (DE-627)1868624587 aut Liu, Huiyu verfasserin (DE-588)1307605494 (DE-627)1868624455 aut Enthalten in International journal of production research London [u.a.] : Taylor & Francis, 1996 61(2023), 15, Seite 5156-5175 Online-Ressource (DE-627)301516731 (DE-600)1485085-0 (DE-576)094115516 1366-588X nnns volume:61 year:2023 number:15 pages:5156-5175 https://www.tandfonline.com/doi/pdf/10.1080/00207543.2022.2098871 Verlag lizenzpflichtig https://doi.org/10.1080/00207543.2022.2098871 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_110 GBV_ILN_120 GBV_ILN_152 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 61 2023 15 5156-5175 26 01 0206 4391995255 x1z 18-10-23 26 00 DE-206 This study investigated a multi-objective resequencing scheduling problem in the automotive manufacturing systems due to operational requirements on the color-batching of the paint shop and sequential requirements on the sequence adherence of an assembly shop. Resequencing cars as color-oriented batches reduced the costs of color changes and operational costs for paint shops. Also, assembly shops required paint shops to complete cars with fewer delays so that high sequence adherence with its demand was assured. Based on real-world applications, we investigated two contradictory objectives-color change costs and sequence tardiness-in a single-machine flowshop scheduling environment. A multi-objective-deep-Q-network algorithm was developed to determine the Pareto frontier. Reward shaping was designed to improve the convergence of the neural network. The 2D-folded-normal distribution was designed to sample the preference, which made the exploration and exploitation of the neural network more comprehensive and improved the training efficiency. Two experiments were conducted and showed that the proposed approach outperformed the meta-heuristic algorithm and the envelope Q-learning algorithm in solving time, performance, the convergence of the neural network, and the diversity of the Pareto frontier. Therefore, the proposed approach can be used in automotive paint shops to improve scheduling efficiency and reduce operational costs. |
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10.1080/00207543.2022.2098871 doi (DE-627)1866192078 (DE-599)KXP1866192078 DE-627 ger DE-627 rda eng Leng, Jinling verfasserin (DE-588)1307604617 (DE-627)1868623785 aut A multi-objective reinforcement learning approach for resequencing scheduling problems in automotive manufacturing systems Jinling Leng, Xingyuan Wang, Shiping Wu, Chun Jin, Meng Tang, Rui Liu, Alexander Vogl, Huiyu Liu 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier color-batching (dpeaa)DE-206 multi-objective optimization (dpeaa)DE-206 multi-objective reinforcement learning (dpeaa)DE-206 Scheduling (dpeaa)DE-206 sequence adherence (dpeaa)DE-206 Wang, Xingyuan verfasserin (DE-588)1307604684 (DE-627)1868623823 aut Wu, Shiping verfasserin (DE-588)1307604765 (DE-627)1868623874 aut Jin, Chun verfasserin (DE-588)1307605168 (DE-627)1868624196 aut Tang, Meng verfasserin (DE-588)1307605249 (DE-627)1868624234 aut Liu, Rui verfasserin (DE-588)1307605346 (DE-627)1868624285 aut Vogl, Alexander verfasserin (DE-588)1307605702 (DE-627)1868624587 aut Liu, Huiyu verfasserin (DE-588)1307605494 (DE-627)1868624455 aut Enthalten in International journal of production research London [u.a.] : Taylor & Francis, 1996 61(2023), 15, Seite 5156-5175 Online-Ressource (DE-627)301516731 (DE-600)1485085-0 (DE-576)094115516 1366-588X nnns volume:61 year:2023 number:15 pages:5156-5175 https://www.tandfonline.com/doi/pdf/10.1080/00207543.2022.2098871 Verlag lizenzpflichtig https://doi.org/10.1080/00207543.2022.2098871 Resolving-System lizenzpflichtig GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_110 GBV_ILN_120 GBV_ILN_152 GBV_ILN_224 GBV_ILN_285 GBV_ILN_370 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 61 2023 15 5156-5175 26 01 0206 4391995255 x1z 18-10-23 26 00 DE-206 This study investigated a multi-objective resequencing scheduling problem in the automotive manufacturing systems due to operational requirements on the color-batching of the paint shop and sequential requirements on the sequence adherence of an assembly shop. Resequencing cars as color-oriented batches reduced the costs of color changes and operational costs for paint shops. Also, assembly shops required paint shops to complete cars with fewer delays so that high sequence adherence with its demand was assured. Based on real-world applications, we investigated two contradictory objectives-color change costs and sequence tardiness-in a single-machine flowshop scheduling environment. A multi-objective-deep-Q-network algorithm was developed to determine the Pareto frontier. Reward shaping was designed to improve the convergence of the neural network. The 2D-folded-normal distribution was designed to sample the preference, which made the exploration and exploitation of the neural network more comprehensive and improved the training efficiency. Two experiments were conducted and showed that the proposed approach outperformed the meta-heuristic algorithm and the envelope Q-learning algorithm in solving time, performance, the convergence of the neural network, and the diversity of the Pareto frontier. Therefore, the proposed approach can be used in automotive paint shops to improve scheduling efficiency and reduce operational costs. |
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Resequencing cars as color-oriented batches reduced the costs of color changes and operational costs for paint shops. Also, assembly shops required paint shops to complete cars with fewer delays so that high sequence adherence with its demand was assured. Based on real-world applications, we investigated two contradictory objectives-color change costs and sequence tardiness-in a single-machine flowshop scheduling environment. A multi-objective-deep-Q-network algorithm was developed to determine the Pareto frontier. Reward shaping was designed to improve the convergence of the neural network. The 2D-folded-normal distribution was designed to sample the preference, which made the exploration and exploitation of the neural network more comprehensive and improved the training efficiency. 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26 00 DE-206 This study investigated a multi-objective resequencing scheduling problem in the automotive manufacturing systems due to operational requirements on the color-batching of the paint shop and sequential requirements on the sequence adherence of an assembly shop. Resequencing cars as color-oriented batches reduced the costs of color changes and operational costs for paint shops. Also, assembly shops required paint shops to complete cars with fewer delays so that high sequence adherence with its demand was assured. Based on real-world applications, we investigated two contradictory objectives-color change costs and sequence tardiness-in a single-machine flowshop scheduling environment. A multi-objective-deep-Q-network algorithm was developed to determine the Pareto frontier. Reward shaping was designed to improve the convergence of the neural network. The 2D-folded-normal distribution was designed to sample the preference, which made the exploration and exploitation of the neural network more comprehensive and improved the training efficiency. Two experiments were conducted and showed that the proposed approach outperformed the meta-heuristic algorithm and the envelope Q-learning algorithm in solving time, performance, the convergence of the neural network, and the diversity of the Pareto frontier. Therefore, the proposed approach can be used in automotive paint shops to improve scheduling efficiency and reduce operational costs A multi-objective reinforcement learning approach for resequencing scheduling problems in automotive manufacturing systems Jinling Leng, Xingyuan Wang, Shiping Wu, Chun Jin, Meng Tang, Rui Liu, Alexander Vogl, Huiyu Liu color-batching (dpeaa)DE-206 multi-objective optimization (dpeaa)DE-206 multi-objective reinforcement learning (dpeaa)DE-206 Scheduling (dpeaa)DE-206 sequence adherence (dpeaa)DE-206 |
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| title_short |
A multi-objective reinforcement learning approach for resequencing scheduling problems in automotive manufacturing systems |
| url |
https://www.tandfonline.com/doi/pdf/10.1080/00207543.2022.2098871 https://doi.org/10.1080/00207543.2022.2098871 |
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| author2 |
Wang, Xingyuan Wu, Shiping Jin, Chun Tang, Meng Liu, Rui Vogl, Alexander Liu, Huiyu |
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Wang, Xingyuan Wu, Shiping Jin, Chun Tang, Meng Liu, Rui Vogl, Alexander Liu, Huiyu |
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Leng Jinling Leng, J. Jinling, Leng Leng, Jinling Wang, X. Xingyuan, Wang Wang Xingyuan Wang, Xingyuan Wu, S. Wu Shiping Shiping, Wu Wu, Shiping Chun, Jin Jin Chun Jin, C. Jin, Chun Meng, Tang Tang, M. Tang Meng Tang, Meng Liu Rui Rui, Liu Liu, R. Liu, Rui Vogl, A. Vogl, Alexander Huiyu, Liu Liu Huiyu Liu, H. Liu, Huiyu |
| GND_txt_mv |
Leng Jinling Leng, J. Jinling, Leng Leng, Jinling Wang, X. Xingyuan, Wang Wang Xingyuan Wang, Xingyuan Wu, S. Wu Shiping Shiping, Wu Wu, Shiping Chun, Jin Jin Chun Jin, C. Jin, Chun Meng, Tang Tang, M. Tang Meng Tang, Meng Liu Rui Rui, Liu Liu, R. Liu, Rui Vogl, A. Vogl, Alexander Huiyu, Liu Liu Huiyu Liu, H. Liu, Huiyu |
| GND_txtF_mv |
Leng Jinling Leng, J. Jinling, Leng Leng, Jinling Wang, X. Xingyuan, Wang Wang Xingyuan Wang, Xingyuan Wu, S. Wu Shiping Shiping, Wu Wu, Shiping Chun, Jin Jin Chun Jin, C. Jin, Chun Meng, Tang Tang, M. Tang Meng Tang, Meng Liu Rui Rui, Liu Liu, R. Liu, Rui Vogl, A. Vogl, Alexander Huiyu, Liu Liu Huiyu Liu, H. Liu, Huiyu |
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| doi_str |
10.1080/00207543.2022.2098871 |
| up_date |
2024-07-04T11:52:16.455Z |
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ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">61</subfield><subfield code="j">2023</subfield><subfield code="e">15</subfield><subfield code="h">5156-5175</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">01</subfield><subfield code="x">0206</subfield><subfield code="b">4391995255</subfield><subfield code="y">x1z</subfield><subfield code="z">18-10-23</subfield></datafield><datafield tag="982" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">00</subfield><subfield code="x">DE-206</subfield><subfield code="b">This study investigated a multi-objective resequencing scheduling problem in the automotive manufacturing systems due to operational requirements on the color-batching of the paint shop and sequential requirements on the sequence adherence of an assembly shop. Resequencing cars as color-oriented batches reduced the costs of color changes and operational costs for paint shops. Also, assembly shops required paint shops to complete cars with fewer delays so that high sequence adherence with its demand was assured. Based on real-world applications, we investigated two contradictory objectives-color change costs and sequence tardiness-in a single-machine flowshop scheduling environment. A multi-objective-deep-Q-network algorithm was developed to determine the Pareto frontier. Reward shaping was designed to improve the convergence of the neural network. The 2D-folded-normal distribution was designed to sample the preference, which made the exploration and exploitation of the neural network more comprehensive and improved the training efficiency. Two experiments were conducted and showed that the proposed approach outperformed the meta-heuristic algorithm and the envelope Q-learning algorithm in solving time, performance, the convergence of the neural network, and the diversity of the Pareto frontier. Therefore, the proposed approach can be used in automotive paint shops to improve scheduling efficiency and reduce operational costs.</subfield></datafield></record></collection>
|
| score |
7.401394 |