A Review of Reinforcement Learning Based Intelligent Optimization for Manufacturing Scheduling

Wang, Ling; Pan, Zixiao; Wang, Jing-jing

doi:10.23919/csms.2021.0027

Cited by 137 publications

(60 citation statements)

References 63 publications

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“…Smart cities have been valued by countries all over the world since they came into being; they provide more convenient conditions for people's lives, while improving the intellectual capital of cities [12][13][14][15][16]. As a smart city is highly dependent on some novel technical means such as cloud computing and IoT [17][18][19][20][21][22][23][24], the hidden danger of di usion of information risk accompanies the technical application. It brings impacts from di erent perspectives on urban information security [25][26][27][28][29][30].…”

Section: Related Workmentioning

confidence: 99%

Internal Resource Allocation to Information Security of Smart Cities Using Evolutionary Game Model

Zou

Xing

2022

Discrete Dynamics in Nature and Society

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The internal resource allocation approach is proposed for the information security of smart cities using the evolutionary game model. Focusing on the information resource allocation of smart cities, the relationship among information security factors is first analyzed, then the directed connection graph and adjacency matrix are constructed to obtain its directed hierarchical structure diagram by calculating the reachable matrix, thereby developing an explanatory structure model for information security resources in smart cities. Furthermore, an evolutionary game model is established, and the replicator dynamics are used to adjust the model. Finally, three types of typical problems are investigated through simulation experiments to verify and explain the structural model.

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Section: Related Workmentioning

confidence: 99%

Internal Resource Allocation to Information Security of Smart Cities Using Evolutionary Game Model

Zou

Xing

2022

Discrete Dynamics in Nature and Society

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“…Furthermore, ref. [ 185 ] summarize the designs of state and action, provides RL-based algorithms for scheduling, and reviews the applications of RL for different types of scheduling problems.…”

Section: Maintenancementioning

confidence: 99%

Applied Machine Learning for IIoT and Smart Production—Methods to Improve Production Quality, Safety and Sustainability

Frankó

Hollósi

Ficzere

et al. 2022

Sensors

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Industrial IoT (IIoT) has revolutionized production by making data available to stakeholders at many levels much faster, with much greater granularity than ever before. When it comes to smart production, the aim of analyzing the collected data is usually to achieve greater efficiency in general, which includes increasing production but decreasing waste and using less energy. Furthermore, the boost in communication provided by IIoT requires special attention to increased levels of safety and security. The growth in machine learning (ML) capabilities in the last few years has affected smart production in many ways. The current paper provides an overview of applying various machine learning techniques for IIoT, smart production, and maintenance, especially in terms of safety, security, asset localization, quality assurance and sustainability aspects. The approach of the paper is to provide a comprehensive overview on the ML methods from an application point of view, hence each domain—namely security and safety, asset localization, quality control, maintenance—has a dedicated chapter, with a concluding table on the typical ML techniques and the related references. The paper summarizes lessons learned, and identifies research gaps and directions for future work.

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“…Zhou et al [9] proposed a self-adaptive differential evolution algorithm for scheduling a single batchprocessing machine with arbitrary job sizes and release times. Zhao et al [10][11][12] proposed three new improved algorithms for different flowshop scheduling scenarios. However, in the FJSSP-RP problem, the difficulty and complexity of encoding and decoding using traditional heuristic algorithms are increased due to resource preemption, and the convergence speed is also affected.…”

Section: Introductionmentioning

confidence: 99%

Optimal Design of Flexible Job Shop Scheduling Under Resource Preemption Based on Deep Reinforcement Learning

Chen

Zhang

Wang

et al. 2022

Complex Syst. Model. Simul.

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With the popularization of multi-variety and small-batch production patterns, the flexible job shop scheduling problem (FJSSP) has been widely studied. The sharing of processing resources by multiple machines frequently occurs due to space constraints in a flexible shop, which results in resource preemption for processing workpieces. Resource preemption complicates the constraints of scheduling problems that are otherwise difficult to solve. In this paper, the flexible job shop scheduling problem under the process resource preemption scenario is modeled, and a two-layer rule scheduling algorithm based on deep reinforcement learning is proposed to achieve the goal of minimum scheduling time. The simulation experiments compare our scheduling algorithm with two traditional metaheuristic optimization algorithms among different processing resource distribution scenarios in static scheduling environment. The results suggest that the two-layer rule scheduling algorithm based on deep reinforcement learning is more effective than the meta-heuristic algorithm in the application of processing resource preemption scenarios. Ablation experiments, generalization, and dynamic experiments are performed to demonstrate the excellent performance of our method for FJSSP under resource preemption.

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A Review of Reinforcement Learning Based Intelligent Optimization for Manufacturing Scheduling

Cited by 137 publications

References 63 publications

Internal Resource Allocation to Information Security of Smart Cities Using Evolutionary Game Model

Internal Resource Allocation to Information Security of Smart Cities Using Evolutionary Game Model

Applied Machine Learning for IIoT and Smart Production—Methods to Improve Production Quality, Safety and Sustainability

Optimal Design of Flexible Job Shop Scheduling Under Resource Preemption Based on Deep Reinforcement Learning

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