Scheduling of decentralized robot services in cloud manufacturing with deep reinforcement learning |
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| Affiliation: | 1. School of Mechano Electronic Engineering Xidian University, Xi''an, Shaanxi 710071, China;2. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China;3. Department of Production Engineering KTH Royal Institute of Technology, Stockholm 10044, Sweden;4. Department of Mechanical Engineering, The University of Auckland, Auckland 1142, New Zealand;1. Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China;2. Department of Advanced Manufacturing, Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China;1. State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, 310027, Hang Zhou, PR China;2. Engineering Research Center for Design Engineering and Digital Twin of Zhejiang Province, School of Mechanical Engineering, Zhejiang University, 310027, Hang Zhou, PR China;3. Hangzhou Innovation Institute, Beihang University, 310051, Hang Zhou, PR China;4. Department of mechanical engineering, Zhejiang University of Technology, 310023, Hang Zhou, PR China;1. Laboratory of Intelligent Manufacturing, Design and Automation (LIMDA), Department of Mechanical Engineering, University of Alberta, Edmonton, Canada;2. School of Intelligent Manufacturing Ecosystem, Xi''an Jiaotong-Liverpool University, Suzhou, China;3. Department of Mechanical and Construction Engineering, Northumbria University, Newcastle Upon Tyne, United Kingdom |
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| Abstract: | Cloud manufacturing is a service-oriented manufacturing model that offers manufacturing resources as cloud services. Robots are an important type of manufacturing resources. In cloud manufacturng, large-scale distributed robots are encapsulated into cloud services and provided to consumers in an on-demand manner. How to effectively and efficiently manage and schedule decentralized robot services in cloud manufacturing to achieve on-demand provisioning is a challenging issue. During the past few years, Deep Reinforcement Learning (DRL) has become very popular and successfully been applied to many different areas such as games, robotics, and manufacturing. DRL also holds tremendous potential for solving scheduling issues in cloud manufacturing. To this end, this paper is devoted to exploring effective approaches for scheduling of decentralized robot manufacturing services in cloud manufacturing with DRL. Specifically, both Deep Q-Networks (DQN) and Dueling Deep Q-Networks (DDQN)-based scheduling algorithms are proposed. Performance of different algorithms, including DQN, DDQN, and other three benchmark algorithms, indicates that DDQN performs the best with respect to each indicator. Effects of different combinations of weight coefficients and influencing degrees of different indicators on the overall scheduling objective are analyzed. Results indicate that the DDQN-based scheduling algorithm is able to generate scheduling solutions efficiently. |
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