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Out-of-order execut...
Out-of-order execution enabled deep reinforcement learning for dynamic additive manufacturing scheduling
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- Sun, Mingyue (författare)
- Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China
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- Ding, Jiyuchen (författare)
- Research Institute for Advanced Manufacturing, The Hong Kong Polytechnic University, Hong Kong, China
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- Zhao, Zhiheng (författare)
- Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China; Research Institute for Advanced Manufacturing, The Hong Kong Polytechnic University, Hong Kong, China; State Key Laboratory of Intelligent Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, China
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- Chen, Jian (författare)
- Department of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing, China
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- Huang, George Q. (författare)
- Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China; Research Institute for Advanced Manufacturing, The Hong Kong Polytechnic University, Hong Kong, China
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- Wang, Lihui (författare)
- KTH,Industriella produktionssystem
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(creator_code:org_t)
- Elsevier BV, 2025
- 2025
- Engelska.
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Ingår i: Robotics and Computer-Integrated Manufacturing. - : Elsevier BV. - 0736-5845 .- 1879-2537. ; 91
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Additive Manufacturing (AM) has revolutionized the production landscape by enabling on-demand customized manufacturing. However, the efficient management of dynamic AM orders poses significant challenges for production planning and scheduling. This paper addresses the dynamic scheduling problem considering batch processing, random order arrival and machine eligibility constraints, aiming to minimize total tardiness in a parallel non-identical AM machine environment. To tackle this problem, we propose the out-of-order enabled dueling deep Q network (O3-DDQN) approach. In the proposed approach, the problem is formulated as a Markov decision process (MDP). Three-dimensional features, encompassing dynamic orders, AM machines, and delays, are extracted using a ‘look around’ method to represent the production status at a rescheduling point. Additionally, five novel composite scheduling rules based on the out-of-order principle are introduced for selection when an AM machine completes processing or a new order arrives. Moreover, we design a reward function that is strongly correlated with the objective to evaluate the agent's chosen action. Experimental results demonstrate the superiority of the O3-DDQN approach over single scheduling rules, randomly selected rules, and the classic DQN method. The average improvement rate of performance reaches 13.09% compared to composite scheduling rules and random rules. Additionally, the O3-DDQN outperforms the classic DQN agent with a 6.54% improvement rate. The O3-DDQN algorithm improves scheduling in dynamic AM environments, enhancing productivity and on-time delivery. This research contributes to advancing AM production and offers insights into efficient resource allocation.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Maskinteknik -- Produktionsteknik, arbetsvetenskap och ergonomi (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Mechanical Engineering -- Production Engineering, Human Work Science and Ergonomics (hsv//eng)
Nyckelord
- Additive manufacturing
- Dueling DQN
- Dynamic order arrival
- Dynamic scheduling
- Out-of-order
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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