Logistics-involved service composition in a dynamic cloud manufacturing environment: A DDPG-based approach

•A Deep Deterministic Policy Gradient (DDPG)-based approach to service composition in cloud manufacturing is proposed.•Performance of DDPG is examined in both static and dynamic cloud manufacturing environments.•DDPG is able to effectively coping with cloud manufacturing service composition and outp...

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Published in:Robotics and computer-integrated manufacturing Vol. 76; p. 102323
Main Authors: Liu, Yongkui, Liang, Huagang, Xiao, Yingying, Zhang, Haifeng, Zhang, Jingxin, Zhang, Lin, Wang, Lihui
Format: Journal Article
Language:English
Published: Oxford Elsevier Ltd 01.08.2022
Elsevier BV
Subjects:
Algorithms
Ant Colony Optimization
Cloud manufacturing
Composition
Convergence
deep deterministic policy gradient algorithm
deep deterministic policy gradient algorithm, Ant Colony Optimization
deep reinforcement learning
Heuristic methods
Logistics
Machine learning
Manufacturing
service composition
Cloud manufacturing
deep deterministic policy gradient algorithm, Ant Colony Optimization
deep reinforcement learning
service composition
ISSN:0736-5845, 1879-2537, 1879-2537
Online Access:Get full text
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Summary:•A Deep Deterministic Policy Gradient (DDPG)-based approach to service composition in cloud manufacturing is proposed.•Performance of DDPG is examined in both static and dynamic cloud manufacturing environments.•DDPG is able to effectively coping with cloud manufacturing service composition and outperforms DQN.•DDPG achieves good adaptability and scalability to dynamic environment that are beyond the capacity of ACO. Service composition as an important technique for combining multiple services to construct a value-added service is a major research issue in cloud manufacturing. Highly dynamic environments present great challenges to cloud manufacturing service composition (CMfg-SC). Most of previous studies employ heuristic algorithms to solve service composition issues in cloud manufacturing, which, however, are designed for specific problems and lack adaptability necessary to dynamic environment. Hence, CMfg-SC calls for new adaptive approaches. Recent advances in deep reinforcement learning (DRL) provide a new means for solving this issue. Based on DRL, we propose a Deep Deterministic Policy Gradient (DDPG)-based service composition approach to cloud manufacturing, with which optimal service composition solutions can be learned through repeated training. Performance of DDPG in solving CMfg-SC in both static and dynamic environments is examined. Results obtained with another DRL algorithm - Deep Q-Networks (DQN) and the traditional Ant Colony Optimization (ACO) are also presented. Comparison indicates that DDPG has better adaptability, robustness, and extensibility to dynamic environments than ACO, although ACO converges faster and its steady QoS value of the service composition solution is higher than that of DDPG by 0.997%. DDPG outperforms DQN in convergence speed and stability, and the QoS value of the service composition solution of DDPG is higher than that of DQN by 3.249%.
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ISSN:0736-5845
1879-2537
1879-2537
DOI:10.1016/j.rcim.2022.102323