Dynamic Scheduling Techniques in Cloud Manufacturing – An Exploration of Deep Reinforcement Learning as a Critical Opportunity for Future Research

Chambers, D; Lammers, T; Yu, K

Dynamic Scheduling Techniques in Cloud Manufacturing – An Exploration of Deep Reinforcement Learning as a Critical Opportunity for Future Research

Chambers, D Lammers, T

Yu, K

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Publisher:: AHFE International
Publication Type:: Conference Proceeding
Citation:: Production Management and Process Control, 2022, 36
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Chambers, D
dc.contributor.author	Lammers, T https://orcid.org/0000-0003-1000-2395
dc.contributor.author	Yu, K
dc.date.accessioned	2022-10-07T18:17:56Z
dc.date.available	2022-10-07T18:17:56Z
dc.date.issued	2022-01-01
dc.identifier.citation	Production Management and Process Control, 2022, 36
dc.identifier.issn	2771-0718
dc.identifier.uri	http://hdl.handle.net/10453/162393
dc.description.abstract	<jats:p>Objective: Determine the state-of-the-art in dynamic scheduling techniques for cloud manufacturing.Significance: This paper firmly establishes the underexplored technique of Deep Reinforcement Learning as the state-of-the-art for dynamic scheduling in cloud manufacturing, exposes a significant gap in the literature, and sets out critical future research objectives.Abstract:For many years, metaheuristic algorithms have represented the state of the art in manufacturing scheduling techniques, proving to be exceptionally reliable for optimising schedules. However, metaheuristics suffer from inherent weaknesses that inhibit their ability to be applied to dynamic cloud manufacturing (CMfg) scheduling problems in practice. Thanks to the very recent and rapidly accelerating development in deep reinforcement learning (DRL), a small sample of studies have described how those approaches have thoroughly outperformed metaheuristic algorithms in dynamic manufacturing scheduling problems, establishing a new state of the art. However, a significant lag in maturity exists between the algorithms used in CMfg and state-of-the-art DRL. This paper systematically reviews the CMfg scheduling literature published between 2010 and 2020, summarises the development of deep reinforcement learning in this context and offers valuable directions for future research.</jats:p>
dc.language	en
dc.publisher	AHFE International
dc.relation.ispartof	Production Management and Process Control
dc.relation.ispartof	Production Management and Process Control
dc.relation.isbasedon	10.54941/ahfe1001625
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Dynamic Scheduling Techniques in Cloud Manufacturing – An Exploration of Deep Reinforcement Learning as a Critical Opportunity for Future Research
dc.type	Conference Proceeding
utslib.citation.volume	36
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	open_access	*
dc.date.updated	2022-10-07T18:17:55Z
pubs.publication-status	Published online
pubs.volume	36

Abstract:

Objective: Determine the state-of-the-art in dynamic scheduling techniques for cloud manufacturing.Significance: This paper firmly establishes the underexplored technique of Deep Reinforcement Learning as the state-of-the-art for dynamic scheduling in cloud manufacturing, exposes a significant gap in the literature, and sets out critical future research objectives.Abstract:For many years, metaheuristic algorithms have represented the state of the art in manufacturing scheduling techniques, proving to be exceptionally reliable for optimising schedules. However, metaheuristics suffer from inherent weaknesses that inhibit their ability to be applied to dynamic cloud manufacturing (CMfg) scheduling problems in practice. Thanks to the very recent and rapidly accelerating development in deep reinforcement learning (DRL), a small sample of studies have described how those approaches have thoroughly outperformed metaheuristic algorithms in dynamic manufacturing scheduling problems, establishing a new state of the art. However, a significant lag in maturity exists between the algorithms used in CMfg and state-of-the-art DRL. This paper systematically reviews the CMfg scheduling literature published between 2010 and 2020, summarises the development of deep reinforcement learning in this context and offers valuable directions for future research.

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http://hdl.handle.net/10453/162393