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Intelligent decision-making of scheduling for dynamic permutation flowshop via deep reinforcement learning
Yang SL(杨圣落)1,2,3; Xu ZG(徐志刚)1,2; Wang JY(王军义)1,2
Department智能产线与系统研究室
Source PublicationSensors (Switzerland)
ISSN14248220
2021
Volume21Issue:3Pages:1-20
Indexed BySCI ; EI
EI Accession number20210509874692
WOS IDWOS:000615486700001
Contribution Rank1
Funding OrganizationNational Natural Science Foundation of China (61803367) ; Natural Science Foundation of Liaoning Province (2019-MS-346)
Keywordpermutation flowshop scheduling problem deep reinforcement learning actor-critic dynamic scheduling real-time scheduling new job arrival tardiness cost
Abstract

Dynamic scheduling problems have been receiving increasing attention in recent years due to their practical implications. To realize real-time and the intelligent decision-making of dynamic scheduling, we studied dynamic permutation flowshop scheduling problem (PFSP) with new job arrival using deep reinforcement learning (DRL). A system architecture for solving dynamic PFSP using DRL is proposed, and the mathematical model to minimize total tardiness cost is established. Additionally, the intelligent scheduling system based on DRL is modeled, with state features, actions, and reward designed. Moreover, the advantage actor-critic (A2C) algorithm is adapted to train the scheduling agent. The learning curve indicates that the scheduling agent learned to generate better solutions efficiently during training. Extensive experiments are carried out to compare the A2C-based scheduling agent with every single action, other DRL algorithms, and meta-heuristics. The results show the well performance of the A2C-based scheduling agent considering solution quality, CPU times, and generalization. Notably, the trained agent generates a scheduling action only in 2.16 ms on average, which is almost instantaneous and can be used for real-time scheduling. Our work can help to build a self-learning, real-time optimizing, and intelligent decision-making scheduling system.

Language英语
WOS SubjectChemistry, Analytical ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS KeywordITERATED GREEDY ALGORITHM ; WEIGHTED TARDINESS ; BATCH DELIVERY ; LOCAL SEARCH ; SHOP ; MAKESPAN ; TIME ; OPTIMIZATION ; MINIMIZATION ; JOBS
WOS Research AreaChemistry ; Engineering ; Instruments & Instrumentation
Funding ProjectNational Natural Science Foundation of China[61803367] ; Natural Science Foundation of Liaoning Province[2019-MS-346]
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/28306
Collection智能产线与系统研究室
Corresponding AuthorXu ZG(徐志刚)
Affiliation1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China
3.University of Chinese Academy of Sciences, Beijing 100049, China
Recommended Citation
GB/T 7714
Yang SL,Xu ZG,Wang JY. Intelligent decision-making of scheduling for dynamic permutation flowshop via deep reinforcement learning[J]. Sensors (Switzerland),2021,21(3):1-20.
APA Yang SL,Xu ZG,&Wang JY.(2021).Intelligent decision-making of scheduling for dynamic permutation flowshop via deep reinforcement learning.Sensors (Switzerland),21(3),1-20.
MLA Yang SL,et al."Intelligent decision-making of scheduling for dynamic permutation flowshop via deep reinforcement learning".Sensors (Switzerland) 21.3(2021):1-20.
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