A deep reinforcement learning hyper-heuristic with feature fusion for online packing problems

In recent years, deep reinforcement learning has shown great potential in solving computer games with sequential decision-making scenarios. Hyper-heuristic is a generic search framework, capable of intelligently selecting or generating algorithms to solve a class of optimisation problems with stocha...
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Gespeichert in:

Autor*in:	Tu, Chaofan [verfasserIn] Bai, Ruibin [verfasserIn] Aickelin, Uwe [verfasserIn] Zhang, Yuchang [verfasserIn] Du, Heshan [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Hyper-heuristic Deep reinforcement learning Feature fusion Knapsack problem Strip packing problem

Übergeordnetes Werk:	Enthalten in: Expert systems with applications - Amsterdam [u.a.] : Elsevier Science, 1990, 230
Übergeordnetes Werk:	volume:230

DOI / URN:	10.1016/j.eswa.2023.120568

Katalog-ID:	ELV010519211

Internformat


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245	1	0	\|a A deep reinforcement learning hyper-heuristic with feature fusion for online packing problems
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520			\|a In recent years, deep reinforcement learning has shown great potential in solving computer games with sequential decision-making scenarios. Hyper-heuristic is a generic search framework, capable of intelligently selecting or generating algorithms to solve a class of optimisation problems with stochastic or dynamic settings. This paper proposes a new general framework for solving online packing problems using deep reinforcement learning hyper-heuristics. Although analytical approaches can address most offline packing problems successfully, their online versions have proved much more challenging and the performance of the existing methods is often not satisfactory. In this paper, we extend a recent deep reinforcement learning hyper-heuristic framework by fusing the visual information of real-time packing with distributional information of random parameters of the problem. Computational experiments show that our method outperforms the state of the art online methods with reductions in optimality gap between 2%–19% for knapsack problem and 0.7% for the online strip packing problem. In addition, a new visual analysis presentation is also devised to better interpret the learned packing strategies, which can reveal more information than the widely used landscape analysis. As online packing problems are widely available in production environments, the proposed approach can serve as an important reference to solve other similar combinatorial optimisation problems for which visual layout inputs would aid learning.
650		4	\|a Hyper-heuristic
650		4	\|a Deep reinforcement learning
650		4	\|a Feature fusion
650		4	\|a Knapsack problem
650		4	\|a Strip packing problem
700	1		\|a Bai, Ruibin \|e verfasserin \|4 aut
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700	1		\|a Zhang, Yuchang \|e verfasserin \|4 aut
700	1		\|a Du, Heshan \|e verfasserin \|4 aut
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publishDate	2023
allfields	10.1016/j.eswa.2023.120568 doi (DE-627)ELV010519211 (ELSEVIER)S0957-4174(23)01070-9 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Tu, Chaofan verfasserin aut A deep reinforcement learning hyper-heuristic with feature fusion for online packing problems 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, deep reinforcement learning has shown great potential in solving computer games with sequential decision-making scenarios. Hyper-heuristic is a generic search framework, capable of intelligently selecting or generating algorithms to solve a class of optimisation problems with stochastic or dynamic settings. This paper proposes a new general framework for solving online packing problems using deep reinforcement learning hyper-heuristics. Although analytical approaches can address most offline packing problems successfully, their online versions have proved much more challenging and the performance of the existing methods is often not satisfactory. In this paper, we extend a recent deep reinforcement learning hyper-heuristic framework by fusing the visual information of real-time packing with distributional information of random parameters of the problem. Computational experiments show that our method outperforms the state of the art online methods with reductions in optimality gap between 2%–19% for knapsack problem and 0.7% for the online strip packing problem. In addition, a new visual analysis presentation is also devised to better interpret the learned packing strategies, which can reveal more information than the widely used landscape analysis. As online packing problems are widely available in production environments, the proposed approach can serve as an important reference to solve other similar combinatorial optimisation problems for which visual layout inputs would aid learning. Hyper-heuristic Deep reinforcement learning Feature fusion Knapsack problem Strip packing problem Bai, Ruibin verfasserin aut Aickelin, Uwe verfasserin aut Zhang, Yuchang verfasserin aut Du, Heshan verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 230 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:230 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_150 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_2336 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 54.72 Künstliche Intelligenz VZ AR 230
spelling	10.1016/j.eswa.2023.120568 doi (DE-627)ELV010519211 (ELSEVIER)S0957-4174(23)01070-9 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Tu, Chaofan verfasserin aut A deep reinforcement learning hyper-heuristic with feature fusion for online packing problems 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, deep reinforcement learning has shown great potential in solving computer games with sequential decision-making scenarios. Hyper-heuristic is a generic search framework, capable of intelligently selecting or generating algorithms to solve a class of optimisation problems with stochastic or dynamic settings. This paper proposes a new general framework for solving online packing problems using deep reinforcement learning hyper-heuristics. Although analytical approaches can address most offline packing problems successfully, their online versions have proved much more challenging and the performance of the existing methods is often not satisfactory. In this paper, we extend a recent deep reinforcement learning hyper-heuristic framework by fusing the visual information of real-time packing with distributional information of random parameters of the problem. Computational experiments show that our method outperforms the state of the art online methods with reductions in optimality gap between 2%–19% for knapsack problem and 0.7% for the online strip packing problem. In addition, a new visual analysis presentation is also devised to better interpret the learned packing strategies, which can reveal more information than the widely used landscape analysis. As online packing problems are widely available in production environments, the proposed approach can serve as an important reference to solve other similar combinatorial optimisation problems for which visual layout inputs would aid learning. Hyper-heuristic Deep reinforcement learning Feature fusion Knapsack problem Strip packing problem Bai, Ruibin verfasserin aut Aickelin, Uwe verfasserin aut Zhang, Yuchang verfasserin aut Du, Heshan verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 230 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:230 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_150 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_2336 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 54.72 Künstliche Intelligenz VZ AR 230
allfields_unstemmed	10.1016/j.eswa.2023.120568 doi (DE-627)ELV010519211 (ELSEVIER)S0957-4174(23)01070-9 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Tu, Chaofan verfasserin aut A deep reinforcement learning hyper-heuristic with feature fusion for online packing problems 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, deep reinforcement learning has shown great potential in solving computer games with sequential decision-making scenarios. Hyper-heuristic is a generic search framework, capable of intelligently selecting or generating algorithms to solve a class of optimisation problems with stochastic or dynamic settings. This paper proposes a new general framework for solving online packing problems using deep reinforcement learning hyper-heuristics. Although analytical approaches can address most offline packing problems successfully, their online versions have proved much more challenging and the performance of the existing methods is often not satisfactory. In this paper, we extend a recent deep reinforcement learning hyper-heuristic framework by fusing the visual information of real-time packing with distributional information of random parameters of the problem. Computational experiments show that our method outperforms the state of the art online methods with reductions in optimality gap between 2%–19% for knapsack problem and 0.7% for the online strip packing problem. In addition, a new visual analysis presentation is also devised to better interpret the learned packing strategies, which can reveal more information than the widely used landscape analysis. As online packing problems are widely available in production environments, the proposed approach can serve as an important reference to solve other similar combinatorial optimisation problems for which visual layout inputs would aid learning. Hyper-heuristic Deep reinforcement learning Feature fusion Knapsack problem Strip packing problem Bai, Ruibin verfasserin aut Aickelin, Uwe verfasserin aut Zhang, Yuchang verfasserin aut Du, Heshan verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 230 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:230 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_150 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_2336 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 54.72 Künstliche Intelligenz VZ AR 230
allfieldsGer	10.1016/j.eswa.2023.120568 doi (DE-627)ELV010519211 (ELSEVIER)S0957-4174(23)01070-9 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Tu, Chaofan verfasserin aut A deep reinforcement learning hyper-heuristic with feature fusion for online packing problems 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, deep reinforcement learning has shown great potential in solving computer games with sequential decision-making scenarios. Hyper-heuristic is a generic search framework, capable of intelligently selecting or generating algorithms to solve a class of optimisation problems with stochastic or dynamic settings. This paper proposes a new general framework for solving online packing problems using deep reinforcement learning hyper-heuristics. Although analytical approaches can address most offline packing problems successfully, their online versions have proved much more challenging and the performance of the existing methods is often not satisfactory. In this paper, we extend a recent deep reinforcement learning hyper-heuristic framework by fusing the visual information of real-time packing with distributional information of random parameters of the problem. Computational experiments show that our method outperforms the state of the art online methods with reductions in optimality gap between 2%–19% for knapsack problem and 0.7% for the online strip packing problem. In addition, a new visual analysis presentation is also devised to better interpret the learned packing strategies, which can reveal more information than the widely used landscape analysis. As online packing problems are widely available in production environments, the proposed approach can serve as an important reference to solve other similar combinatorial optimisation problems for which visual layout inputs would aid learning. Hyper-heuristic Deep reinforcement learning Feature fusion Knapsack problem Strip packing problem Bai, Ruibin verfasserin aut Aickelin, Uwe verfasserin aut Zhang, Yuchang verfasserin aut Du, Heshan verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 230 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:230 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_150 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_2336 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 54.72 Künstliche Intelligenz VZ AR 230
allfieldsSound	10.1016/j.eswa.2023.120568 doi (DE-627)ELV010519211 (ELSEVIER)S0957-4174(23)01070-9 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Tu, Chaofan verfasserin aut A deep reinforcement learning hyper-heuristic with feature fusion for online packing problems 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In recent years, deep reinforcement learning has shown great potential in solving computer games with sequential decision-making scenarios. Hyper-heuristic is a generic search framework, capable of intelligently selecting or generating algorithms to solve a class of optimisation problems with stochastic or dynamic settings. This paper proposes a new general framework for solving online packing problems using deep reinforcement learning hyper-heuristics. Although analytical approaches can address most offline packing problems successfully, their online versions have proved much more challenging and the performance of the existing methods is often not satisfactory. In this paper, we extend a recent deep reinforcement learning hyper-heuristic framework by fusing the visual information of real-time packing with distributional information of random parameters of the problem. Computational experiments show that our method outperforms the state of the art online methods with reductions in optimality gap between 2%–19% for knapsack problem and 0.7% for the online strip packing problem. In addition, a new visual analysis presentation is also devised to better interpret the learned packing strategies, which can reveal more information than the widely used landscape analysis. As online packing problems are widely available in production environments, the proposed approach can serve as an important reference to solve other similar combinatorial optimisation problems for which visual layout inputs would aid learning. Hyper-heuristic Deep reinforcement learning Feature fusion Knapsack problem Strip packing problem Bai, Ruibin verfasserin aut Aickelin, Uwe verfasserin aut Zhang, Yuchang verfasserin aut Du, Heshan verfasserin aut Enthalten in Expert systems with applications Amsterdam [u.a.] : Elsevier Science, 1990 230 Online-Ressource (DE-627)320577961 (DE-600)2017237-0 (DE-576)11481807X nnns volume:230 GBV_USEFLAG_U GBV_ELV SYSFLAG_U 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_150 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_2336 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 54.72 Künstliche Intelligenz VZ AR 230
language	English
source	Enthalten in Expert systems with applications 230 volume:230
sourceStr	Enthalten in Expert systems with applications 230 volume:230
format_phy_str_mv	Article
bklname	Künstliche Intelligenz
institution	findex.gbv.de
topic_facet	Hyper-heuristic Deep reinforcement learning Feature fusion Knapsack problem Strip packing problem
dewey-raw	004
isfreeaccess_bool	false
container_title	Expert systems with applications
authorswithroles_txt_mv	Tu, Chaofan @@aut@@ Bai, Ruibin @@aut@@ Aickelin, Uwe @@aut@@ Zhang, Yuchang @@aut@@ Du, Heshan @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	320577961
dewey-sort	14
id	ELV010519211
language_de	englisch
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author	Tu, Chaofan
spellingShingle	Tu, Chaofan ddc 004 bkl 54.72 misc Hyper-heuristic misc Deep reinforcement learning misc Feature fusion misc Knapsack problem misc Strip packing problem A deep reinforcement learning hyper-heuristic with feature fusion for online packing problems
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topic_title	004 VZ 54.72 bkl A deep reinforcement learning hyper-heuristic with feature fusion for online packing problems Hyper-heuristic Deep reinforcement learning Feature fusion Knapsack problem Strip packing problem
topic	ddc 004 bkl 54.72 misc Hyper-heuristic misc Deep reinforcement learning misc Feature fusion misc Knapsack problem misc Strip packing problem
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title	A deep reinforcement learning hyper-heuristic with feature fusion for online packing problems
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title_full	A deep reinforcement learning hyper-heuristic with feature fusion for online packing problems
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title_sort	a deep reinforcement learning hyper-heuristic with feature fusion for online packing problems
title_auth	A deep reinforcement learning hyper-heuristic with feature fusion for online packing problems
abstract	In recent years, deep reinforcement learning has shown great potential in solving computer games with sequential decision-making scenarios. Hyper-heuristic is a generic search framework, capable of intelligently selecting or generating algorithms to solve a class of optimisation problems with stochastic or dynamic settings. This paper proposes a new general framework for solving online packing problems using deep reinforcement learning hyper-heuristics. Although analytical approaches can address most offline packing problems successfully, their online versions have proved much more challenging and the performance of the existing methods is often not satisfactory. In this paper, we extend a recent deep reinforcement learning hyper-heuristic framework by fusing the visual information of real-time packing with distributional information of random parameters of the problem. Computational experiments show that our method outperforms the state of the art online methods with reductions in optimality gap between 2%–19% for knapsack problem and 0.7% for the online strip packing problem. In addition, a new visual analysis presentation is also devised to better interpret the learned packing strategies, which can reveal more information than the widely used landscape analysis. As online packing problems are widely available in production environments, the proposed approach can serve as an important reference to solve other similar combinatorial optimisation problems for which visual layout inputs would aid learning.
abstractGer	In recent years, deep reinforcement learning has shown great potential in solving computer games with sequential decision-making scenarios. Hyper-heuristic is a generic search framework, capable of intelligently selecting or generating algorithms to solve a class of optimisation problems with stochastic or dynamic settings. This paper proposes a new general framework for solving online packing problems using deep reinforcement learning hyper-heuristics. Although analytical approaches can address most offline packing problems successfully, their online versions have proved much more challenging and the performance of the existing methods is often not satisfactory. In this paper, we extend a recent deep reinforcement learning hyper-heuristic framework by fusing the visual information of real-time packing with distributional information of random parameters of the problem. Computational experiments show that our method outperforms the state of the art online methods with reductions in optimality gap between 2%–19% for knapsack problem and 0.7% for the online strip packing problem. In addition, a new visual analysis presentation is also devised to better interpret the learned packing strategies, which can reveal more information than the widely used landscape analysis. As online packing problems are widely available in production environments, the proposed approach can serve as an important reference to solve other similar combinatorial optimisation problems for which visual layout inputs would aid learning.
abstract_unstemmed	In recent years, deep reinforcement learning has shown great potential in solving computer games with sequential decision-making scenarios. Hyper-heuristic is a generic search framework, capable of intelligently selecting or generating algorithms to solve a class of optimisation problems with stochastic or dynamic settings. This paper proposes a new general framework for solving online packing problems using deep reinforcement learning hyper-heuristics. Although analytical approaches can address most offline packing problems successfully, their online versions have proved much more challenging and the performance of the existing methods is often not satisfactory. In this paper, we extend a recent deep reinforcement learning hyper-heuristic framework by fusing the visual information of real-time packing with distributional information of random parameters of the problem. Computational experiments show that our method outperforms the state of the art online methods with reductions in optimality gap between 2%–19% for knapsack problem and 0.7% for the online strip packing problem. In addition, a new visual analysis presentation is also devised to better interpret the learned packing strategies, which can reveal more information than the widely used landscape analysis. As online packing problems are widely available in production environments, the proposed approach can serve as an important reference to solve other similar combinatorial optimisation problems for which visual layout inputs would aid learning.
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title_short	A deep reinforcement learning hyper-heuristic with feature fusion for online packing problems
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author2	Bai, Ruibin Aickelin, Uwe Zhang, Yuchang Du, Heshan
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up_date	2024-07-06T18:16:37.266Z
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A deep reinforcement learning hyper-heuristic with feature fusion for online packing problems

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