Improved meta-heuristics with Q-learning for solving distributed assembly permutation flowshop scheduling problems

This study addresses a distributed assembly permutation flowshop scheduling problem, which is of great significance in practical manufacturing systems. We aim to sequence products and jobs, and assign jobs to the appropriate factory to minimize the total flowtime of products. First, a mathematical m...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Yu, Hui [verfasserIn] Gao, Kai-Zhou [verfasserIn] Ma, Zhen-Fang [verfasserIn] Pan, Yu-Xia [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Distributed assembly permutation flowshop scheduling Meta-heuristic Total flowtime Q-learning

Übergeordnetes Werk:	Enthalten in: Swarm and evolutionary computation - Amsterdam [u.a.] : Elsevier, 2011, 80
Übergeordnetes Werk:	volume:80

DOI / URN:	10.1016/j.swevo.2023.101335

Katalog-ID:	ELV010147691

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520			\|a This study addresses a distributed assembly permutation flowshop scheduling problem, which is of great significance in practical manufacturing systems. We aim to sequence products and jobs, and assign jobs to the appropriate factory to minimize the total flowtime of products. First, a mathematical model is developed to describe the concerned problems. Then, four meta-heuristics, e.g., artificial bee colony, particle swarm optimization, genetic algorithm, and Jaya algorithm, and their variants are proposed. Three initialization strategies are developed to generate high-quality initial solutions. Four local search operators are designed to improve the performance of the algorithms. Q-learning is embedded to select the premium local search strategy during iterations. Based on 81 large-scale benchmark instances, comprehensive numerical experiments are carried out to evaluate the effectiveness of the proposed algorithms. The experimental results show that the proposed Jaya with Q-learning-based local search has strong competitiveness, and it updates optimal solutions for 51 out of 81 benchmark instances.
650		4	\|a Distributed assembly permutation flowshop scheduling
650		4	\|a Meta-heuristic
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700	1		\|a Pan, Yu-Xia \|e verfasserin \|4 aut
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allfields	10.1016/j.swevo.2023.101335 doi (DE-627)ELV010147691 (ELSEVIER)S2210-6502(23)00108-6 DE-627 ger DE-627 rda eng 004 VZ Yu, Hui verfasserin aut Improved meta-heuristics with Q-learning for solving distributed assembly permutation flowshop scheduling problems 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study addresses a distributed assembly permutation flowshop scheduling problem, which is of great significance in practical manufacturing systems. We aim to sequence products and jobs, and assign jobs to the appropriate factory to minimize the total flowtime of products. First, a mathematical model is developed to describe the concerned problems. Then, four meta-heuristics, e.g., artificial bee colony, particle swarm optimization, genetic algorithm, and Jaya algorithm, and their variants are proposed. Three initialization strategies are developed to generate high-quality initial solutions. Four local search operators are designed to improve the performance of the algorithms. Q-learning is embedded to select the premium local search strategy during iterations. Based on 81 large-scale benchmark instances, comprehensive numerical experiments are carried out to evaluate the effectiveness of the proposed algorithms. The experimental results show that the proposed Jaya with Q-learning-based local search has strong competitiveness, and it updates optimal solutions for 51 out of 81 benchmark instances. Distributed assembly permutation flowshop scheduling Meta-heuristic Total flowtime Q-learning Gao, Kai-Zhou verfasserin (orcid)0000-0002-9252-6928 aut Ma, Zhen-Fang verfasserin aut Pan, Yu-Xia verfasserin aut Enthalten in Swarm and evolutionary computation Amsterdam [u.a.] : Elsevier, 2011 80 Online-Ressource (DE-627)661267121 (DE-600)2611387-9 (DE-576)346017084 nnns volume:80 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 80
spelling	10.1016/j.swevo.2023.101335 doi (DE-627)ELV010147691 (ELSEVIER)S2210-6502(23)00108-6 DE-627 ger DE-627 rda eng 004 VZ Yu, Hui verfasserin aut Improved meta-heuristics with Q-learning for solving distributed assembly permutation flowshop scheduling problems 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study addresses a distributed assembly permutation flowshop scheduling problem, which is of great significance in practical manufacturing systems. We aim to sequence products and jobs, and assign jobs to the appropriate factory to minimize the total flowtime of products. First, a mathematical model is developed to describe the concerned problems. Then, four meta-heuristics, e.g., artificial bee colony, particle swarm optimization, genetic algorithm, and Jaya algorithm, and their variants are proposed. Three initialization strategies are developed to generate high-quality initial solutions. Four local search operators are designed to improve the performance of the algorithms. Q-learning is embedded to select the premium local search strategy during iterations. Based on 81 large-scale benchmark instances, comprehensive numerical experiments are carried out to evaluate the effectiveness of the proposed algorithms. The experimental results show that the proposed Jaya with Q-learning-based local search has strong competitiveness, and it updates optimal solutions for 51 out of 81 benchmark instances. Distributed assembly permutation flowshop scheduling Meta-heuristic Total flowtime Q-learning Gao, Kai-Zhou verfasserin (orcid)0000-0002-9252-6928 aut Ma, Zhen-Fang verfasserin aut Pan, Yu-Xia verfasserin aut Enthalten in Swarm and evolutionary computation Amsterdam [u.a.] : Elsevier, 2011 80 Online-Ressource (DE-627)661267121 (DE-600)2611387-9 (DE-576)346017084 nnns volume:80 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 80
allfields_unstemmed	10.1016/j.swevo.2023.101335 doi (DE-627)ELV010147691 (ELSEVIER)S2210-6502(23)00108-6 DE-627 ger DE-627 rda eng 004 VZ Yu, Hui verfasserin aut Improved meta-heuristics with Q-learning for solving distributed assembly permutation flowshop scheduling problems 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study addresses a distributed assembly permutation flowshop scheduling problem, which is of great significance in practical manufacturing systems. We aim to sequence products and jobs, and assign jobs to the appropriate factory to minimize the total flowtime of products. First, a mathematical model is developed to describe the concerned problems. Then, four meta-heuristics, e.g., artificial bee colony, particle swarm optimization, genetic algorithm, and Jaya algorithm, and their variants are proposed. Three initialization strategies are developed to generate high-quality initial solutions. Four local search operators are designed to improve the performance of the algorithms. Q-learning is embedded to select the premium local search strategy during iterations. Based on 81 large-scale benchmark instances, comprehensive numerical experiments are carried out to evaluate the effectiveness of the proposed algorithms. The experimental results show that the proposed Jaya with Q-learning-based local search has strong competitiveness, and it updates optimal solutions for 51 out of 81 benchmark instances. Distributed assembly permutation flowshop scheduling Meta-heuristic Total flowtime Q-learning Gao, Kai-Zhou verfasserin (orcid)0000-0002-9252-6928 aut Ma, Zhen-Fang verfasserin aut Pan, Yu-Xia verfasserin aut Enthalten in Swarm and evolutionary computation Amsterdam [u.a.] : Elsevier, 2011 80 Online-Ressource (DE-627)661267121 (DE-600)2611387-9 (DE-576)346017084 nnns volume:80 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 80
allfieldsGer	10.1016/j.swevo.2023.101335 doi (DE-627)ELV010147691 (ELSEVIER)S2210-6502(23)00108-6 DE-627 ger DE-627 rda eng 004 VZ Yu, Hui verfasserin aut Improved meta-heuristics with Q-learning for solving distributed assembly permutation flowshop scheduling problems 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study addresses a distributed assembly permutation flowshop scheduling problem, which is of great significance in practical manufacturing systems. We aim to sequence products and jobs, and assign jobs to the appropriate factory to minimize the total flowtime of products. First, a mathematical model is developed to describe the concerned problems. Then, four meta-heuristics, e.g., artificial bee colony, particle swarm optimization, genetic algorithm, and Jaya algorithm, and their variants are proposed. Three initialization strategies are developed to generate high-quality initial solutions. Four local search operators are designed to improve the performance of the algorithms. Q-learning is embedded to select the premium local search strategy during iterations. Based on 81 large-scale benchmark instances, comprehensive numerical experiments are carried out to evaluate the effectiveness of the proposed algorithms. The experimental results show that the proposed Jaya with Q-learning-based local search has strong competitiveness, and it updates optimal solutions for 51 out of 81 benchmark instances. Distributed assembly permutation flowshop scheduling Meta-heuristic Total flowtime Q-learning Gao, Kai-Zhou verfasserin (orcid)0000-0002-9252-6928 aut Ma, Zhen-Fang verfasserin aut Pan, Yu-Xia verfasserin aut Enthalten in Swarm and evolutionary computation Amsterdam [u.a.] : Elsevier, 2011 80 Online-Ressource (DE-627)661267121 (DE-600)2611387-9 (DE-576)346017084 nnns volume:80 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 80
allfieldsSound	10.1016/j.swevo.2023.101335 doi (DE-627)ELV010147691 (ELSEVIER)S2210-6502(23)00108-6 DE-627 ger DE-627 rda eng 004 VZ Yu, Hui verfasserin aut Improved meta-heuristics with Q-learning for solving distributed assembly permutation flowshop scheduling problems 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study addresses a distributed assembly permutation flowshop scheduling problem, which is of great significance in practical manufacturing systems. We aim to sequence products and jobs, and assign jobs to the appropriate factory to minimize the total flowtime of products. First, a mathematical model is developed to describe the concerned problems. Then, four meta-heuristics, e.g., artificial bee colony, particle swarm optimization, genetic algorithm, and Jaya algorithm, and their variants are proposed. Three initialization strategies are developed to generate high-quality initial solutions. Four local search operators are designed to improve the performance of the algorithms. Q-learning is embedded to select the premium local search strategy during iterations. Based on 81 large-scale benchmark instances, comprehensive numerical experiments are carried out to evaluate the effectiveness of the proposed algorithms. The experimental results show that the proposed Jaya with Q-learning-based local search has strong competitiveness, and it updates optimal solutions for 51 out of 81 benchmark instances. Distributed assembly permutation flowshop scheduling Meta-heuristic Total flowtime Q-learning Gao, Kai-Zhou verfasserin (orcid)0000-0002-9252-6928 aut Ma, Zhen-Fang verfasserin aut Pan, Yu-Xia verfasserin aut Enthalten in Swarm and evolutionary computation Amsterdam [u.a.] : Elsevier, 2011 80 Online-Ressource (DE-627)661267121 (DE-600)2611387-9 (DE-576)346017084 nnns volume:80 GBV_USEFLAG_U SYSFLAG_U GBV_ELV 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_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 80
language	English
source	Enthalten in Swarm and evolutionary computation 80 volume:80
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topic_facet	Distributed assembly permutation flowshop scheduling Meta-heuristic Total flowtime Q-learning
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authorswithroles_txt_mv	Yu, Hui @@aut@@ Gao, Kai-Zhou @@aut@@ Ma, Zhen-Fang @@aut@@ Pan, Yu-Xia @@aut@@
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author	Yu, Hui
spellingShingle	Yu, Hui ddc 004 misc Distributed assembly permutation flowshop scheduling misc Meta-heuristic misc Total flowtime misc Q-learning Improved meta-heuristics with Q-learning for solving distributed assembly permutation flowshop scheduling problems
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topic_title	004 VZ Improved meta-heuristics with Q-learning for solving distributed assembly permutation flowshop scheduling problems Distributed assembly permutation flowshop scheduling Meta-heuristic Total flowtime Q-learning
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title	Improved meta-heuristics with Q-learning for solving distributed assembly permutation flowshop scheduling problems
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title_full	Improved meta-heuristics with Q-learning for solving distributed assembly permutation flowshop scheduling problems
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title_sort	improved meta-heuristics with q-learning for solving distributed assembly permutation flowshop scheduling problems
title_auth	Improved meta-heuristics with Q-learning for solving distributed assembly permutation flowshop scheduling problems
abstract	This study addresses a distributed assembly permutation flowshop scheduling problem, which is of great significance in practical manufacturing systems. We aim to sequence products and jobs, and assign jobs to the appropriate factory to minimize the total flowtime of products. First, a mathematical model is developed to describe the concerned problems. Then, four meta-heuristics, e.g., artificial bee colony, particle swarm optimization, genetic algorithm, and Jaya algorithm, and their variants are proposed. Three initialization strategies are developed to generate high-quality initial solutions. Four local search operators are designed to improve the performance of the algorithms. Q-learning is embedded to select the premium local search strategy during iterations. Based on 81 large-scale benchmark instances, comprehensive numerical experiments are carried out to evaluate the effectiveness of the proposed algorithms. The experimental results show that the proposed Jaya with Q-learning-based local search has strong competitiveness, and it updates optimal solutions for 51 out of 81 benchmark instances.
abstractGer	This study addresses a distributed assembly permutation flowshop scheduling problem, which is of great significance in practical manufacturing systems. We aim to sequence products and jobs, and assign jobs to the appropriate factory to minimize the total flowtime of products. First, a mathematical model is developed to describe the concerned problems. Then, four meta-heuristics, e.g., artificial bee colony, particle swarm optimization, genetic algorithm, and Jaya algorithm, and their variants are proposed. Three initialization strategies are developed to generate high-quality initial solutions. Four local search operators are designed to improve the performance of the algorithms. Q-learning is embedded to select the premium local search strategy during iterations. Based on 81 large-scale benchmark instances, comprehensive numerical experiments are carried out to evaluate the effectiveness of the proposed algorithms. The experimental results show that the proposed Jaya with Q-learning-based local search has strong competitiveness, and it updates optimal solutions for 51 out of 81 benchmark instances.
abstract_unstemmed	This study addresses a distributed assembly permutation flowshop scheduling problem, which is of great significance in practical manufacturing systems. We aim to sequence products and jobs, and assign jobs to the appropriate factory to minimize the total flowtime of products. First, a mathematical model is developed to describe the concerned problems. Then, four meta-heuristics, e.g., artificial bee colony, particle swarm optimization, genetic algorithm, and Jaya algorithm, and their variants are proposed. Three initialization strategies are developed to generate high-quality initial solutions. Four local search operators are designed to improve the performance of the algorithms. Q-learning is embedded to select the premium local search strategy during iterations. Based on 81 large-scale benchmark instances, comprehensive numerical experiments are carried out to evaluate the effectiveness of the proposed algorithms. The experimental results show that the proposed Jaya with Q-learning-based local search has strong competitiveness, and it updates optimal solutions for 51 out of 81 benchmark instances.
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title_short	Improved meta-heuristics with Q-learning for solving distributed assembly permutation flowshop scheduling problems
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We aim to sequence products and jobs, and assign jobs to the appropriate factory to minimize the total flowtime of products. First, a mathematical model is developed to describe the concerned problems. Then, four meta-heuristics, e.g., artificial bee colony, particle swarm optimization, genetic algorithm, and Jaya algorithm, and their variants are proposed. Three initialization strategies are developed to generate high-quality initial solutions. Four local search operators are designed to improve the performance of the algorithms. Q-learning is embedded to select the premium local search strategy during iterations. Based on 81 large-scale benchmark instances, comprehensive numerical experiments are carried out to evaluate the effectiveness of the proposed algorithms. The experimental results show that the proposed Jaya with Q-learning-based local search has strong competitiveness, and it updates optimal solutions for 51 out of 81 benchmark instances.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Distributed assembly permutation flowshop scheduling</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Meta-heuristic</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Total flowtime</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Q-learning</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Gao, Kai-Zhou</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0002-9252-6928</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ma, Zhen-Fang</subfield><subfield code="e">verfasserin</subfield><subfield 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Improved meta-heuristics with Q-learning for solving distributed assembly permutation flowshop scheduling problems

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