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Citations (10)
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Daliang Chang, Haibo Shi, Chang Liu & Fu Meng. (2024) Scheduling optimization of flexible flow shop with buffer capacity limitation based on an improved discrete particle swarm optimization algorithm. Engineering Optimization 0:0, pages 1-27.
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Ruilin Pan, Qiong Wang, Jianhua Cao & Chunliu Zhou. (2024) Deep reinforcement learning for solving steelmaking-continuous casting scheduling problems under time-of-use tariffs. International Journal of Production Research 62:1-2, pages 404-420.
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Zhun Xu, Liyun Xu, Xufeng Ling & Beikun Zhang. (2023) Data-driven hierarchical learning and real-time decision-making of equipment scheduling and location assignment in automatic high-density storage systems. International Journal of Production Research 61:21, pages 7333-7352.
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Fuqing Zhao, Xiaotong Hu, Ling Wang, Tianpeng Xu, Ningning Zhu & Jonrinaldi. (2023) A reinforcement learning-driven brain storm optimisation algorithm for multi-objective energy-efficient distributed assembly no-wait flow shop scheduling problem. International Journal of Production Research 61:9, pages 2854-2872.
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Sharareh Taghipour, Hamed A. Namoura, Mani Sharifi & Mageed Ghaleb. Real-time production scheduling using a deep reinforcement learning-based multi-agent approach. INFOR: Information Systems and Operational Research 0:0, pages 1-25.
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Articles from other publishers (5)
Haizhu Bao, Quanke Pan, Rubén Ruiz & Liang Gao. (2023) A collaborative iterated greedy algorithm with reinforcement learning for energy-aware distributed blocking flow-shop scheduling. Swarm and Evolutionary Computation 83, pages 101399.
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Aykut Uzunoglu, Christian Gahm & Axel Tuma. (2023) A machine learning enhanced multi-start heuristic to efficiently solve a serial-batch scheduling problem. Annals of Operations Research.
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Marcel Panzer, Benedict Bender & Norbert Gronau. (2022) Neural agent-based production planning and control: An architectural review. Journal of Manufacturing Systems 65, pages 743-766.
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Liping Zhang, Yifan Hu, Chuangjian Wang, Qiuhua Tang & Xinyu Li. (2022) Effective dispatching rules mining based on near-optimal schedules in intelligent job shop environment. Journal of Manufacturing Systems 63, pages 424-438.
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Byungwook Min & Chang Ouk Kim. (2022) State-Dependent Parameter Tuning of the Apparent Tardiness Cost Dispatching Rule Using Deep Reinforcement Learning. IEEE Access 10, pages 20187-20198.
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