Intelligent scheduling and reconfiguration via deep reinforcement learning in smart manufacturing

Yang, Shengluo; Xu, Zhigang

doi:10.1080/00207543.2021.1943037

Cited by 40 publications

(22 citation statements)

References 44 publications

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“…, 2020; Shi et al. , 2020; Yang and Xu, 2021). The A2C algorithm is a deep reinforcement learning method that combines two kinds of reinforcement learning algorithms (i.e.…”

Section: Science Mapping Of DL Applications In Manufacturing Operatio...mentioning

confidence: 99%

Deep learning applications in manufacturing operations: a review of trends and ways forward

Sahoo

Kumar

Abedin

et al. 2022

JEIM

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PurposeDeep learning (DL) technologies assist manufacturers to manage their business operations. This research aims to present state-of-the-art insights on the trends and ways forward for DL applications in manufacturing operations.Design/methodology/approachUsing bibliometric analysis and the SPAR-4-SLR protocol, this research conducts a systematic literature review to present a scientific mapping of top-tier research on DL applications in manufacturing operations.FindingsThis research discovers and delivers key insights on six knowledge clusters pertaining to DL applications in manufacturing operations: automated system modelling, intelligent fault diagnosis, forecasting, sustainable manufacturing, environmental management, and intelligent scheduling.Research limitations/implicationsThis research establishes the important roles of DL in manufacturing operations. However, these insights were derived from top-tier journals only. Therefore, this research does not discount the possibility of the availability of additional insights in alternative outlets, such as conference proceedings, where teasers into emerging and developing concepts may be published.Originality/valueThis research contributes seminal insights into DL applications in manufacturing operations. In this regard, this research is valuable to readers (academic scholars and industry practitioners) interested to gain an understanding of the important roles of DL in manufacturing operations as well as the future of its applications for Industry 4.0, such as Maintenance 4.0, Quality 4.0, Logistics 4.0, Manufacturing 4.0, Sustainability 4.0, and Supply Chain 4.0.

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“…, 2020; Shi et al. , 2020; Yang and Xu, 2021). The A2C algorithm is a deep reinforcement learning method that combines two kinds of reinforcement learning algorithms (i.e.…”

Section: Science Mapping Of DL Applications In Manufacturing Operatio...mentioning

confidence: 99%

Deep learning applications in manufacturing operations: a review of trends and ways forward

Sahoo

Kumar

Abedin

et al. 2022

JEIM

View full text Add to dashboard Cite

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“…Furthermore, dynamic environments complicate the model and run-time limits the application of mathematical optimization to this NP-hard problem [34]. New methodologies such as deep reinforcement learning should be explored to study the real-time optimization and collaborative control of RMS in intelligent manufacturing [35].…”

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confidence: 99%

Process Planning, Scheduling, and Layout Optimization for Multi-Unit Mass-Customized Products in Sustainable Reconfigurable Manufacturing System

2021

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Currently, manufacturers seek to provide customized and sustainable products, requiring flexible manufacturing systems and advanced production management to cope with customization complexity and improve environmental performance. The reconfigurable manufacturing system (RMS) is expected to provide cost-effective customization in high responsiveness. However, reconfiguration optimization to produce sustainable mass-customized products in RMS is a complex problem requiring multi-criteria decision making. It is related to three problems, process planning, scheduling, and layout optimization, which should be integrated to optimize the RMS performance. This paper aims at integrating the above three problems and developing an effective approach to solving them concurrently. It formulates a multi-objective mathematical model simultaneously optimizing process planning, job-shop scheduling, and open-field layout problem to improve RMS sustainability. The penalty for product tardiness, the total manufacturing cost, the hazardous waste, and the greenhouse gases emissions are minimized. Economic and environmental indicators are defined to modify the Pareto efficiency when searching the Pareto-optimal solutions. Exact Pareto-optimal solutions are obtained by brute-force search and compared with those of the non-environmental indicator model. NSGA-III is adopted to obtain the approximate Pareto-optimal solutions in high effectiveness and efficiency. A small numerical example is applied to validate the mathematical model and resolution methods.

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“…Despite very early attempts that couple machine learning with optimization methods [2][3][4], recent deep learning based methods present promising results on some COPs, which are comparable to classic optimization methods [5,6]. Their success has inspired miscellaneous deep models to cope with specific COPs, e.g., vehicle routing [7][8][9][10][11], scheduling [12][13][14][15], and enhance algorithmic paradigms for general COPs, e.g., integer programming (IP) [16][17][18][19], stochastic integer programming (SIP) [20][21][22][23][24]. All the above studies on the novel and active NCO domain greatly enrich the learning for optimization community.…”

Section: Motivationsmentioning

confidence: 99%

“…The remaining nodes are only for transition without demands. We set two types of commodities and their quantities are uniformly sampled from [5,15] in each scenario. For each edge, we uniformly sample the opening cost, shipping cost and capacity from [3,11], [5,11] and [10,41], respectively.…”

Section: Settingsmentioning

confidence: 99%

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Neural combinatorial optimization: from routing to integer programming

Wu¹

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He gives me so much valuable guidance in my research and the biggest support in the project. I feel so proud of being his student and hope that I could be a great teacher like him one day. I'm extremely grateful to Cao Zhiguang who initially lead me into the research field and always provide me all his possible help in every aspect of my life. I also extend my gratitude to Song Wen who teach and advise me a lot regarding my research without reservation. Meawhile, I greatly thank

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Intelligent scheduling and reconfiguration via deep reinforcement learning in smart manufacturing

Cited by 40 publications

References 44 publications

Deep learning applications in manufacturing operations: a review of trends and ways forward

Deep learning applications in manufacturing operations: a review of trends and ways forward

Process Planning, Scheduling, and Layout Optimization for Multi-Unit Mass-Customized Products in Sustainable Reconfigurable Manufacturing System

Neural combinatorial optimization: from routing to integer programming

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