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Job Shop Scheduling Problem (JSSP)

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Job Shop Scheduling Problem (JSSP) #

The Job Shop Scheduling Problem is a classic combinatorial optimization problem where jobs must be scheduled on machines with precedence constraints.

Recent Literature #

  1. Smart Manufacturing Scheduling With Edge Computing Using Multiclass Deep Q Network Transactions on Industrial Informatics, 2019. journal

    Chun-Cheng Lin, Der-Jiunn Deng, Yen-Ling Chih, Hsin-Ting Chiu

  2. Multi-Agent Reinforcement Learning for Job Shop Scheduling in Flexible Manufacturing Systems International Conference on Artificial Intelligence for Industries (AI4I), 2019. paper

    Schirin Baer, Jupiter Bakakeu, Richard Meyes, Tobias Meisen

  3. Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning. NeurIPS, 2020. paper, code

    Zhang, Cong and Song, Wen and Cao, Zhiguang and Zhang, Jie and Tan, Puay Siew and Xu, Chi.

  4. ScheduleNet: Learn to Solve Multi-agent Scheduling Problems with Reinforcement Learning Arxiv, 2021. paper

    Junyoung Park, Sanjar Bakhtiyar, Jinkyoo Park

  5. Dynamic job-shop scheduling in smart manufacturing using deep reinforcement learning Computer Networks, 2021. journal

    Libing Wang, Xin Hu, Yin Wang, Sujie Xu, Shijun Ma, Kexin Yang, Zhijun Liu, Weidong Wang

  6. Learning to schedule job-shop problems: Representation and policy learning using graph neural network and reinforcement learning. International Journal of Production Research, 2021. journal

    Junyoung Park, Jaehyeong Chun, Sang Hun Kim, Youngkook Kim, Jinkyoo Park

  7. Explainable reinforcement learning in production control of job shop manufacturing system. International Journal of Production Research, 2021. journal

    Andreas Kuhnle,Marvin Carl May,Louis Sch?fer & Gisela Lanza

  8. DeepACO: Neural-enhanced Ant Systems for Combinatorial Optimization NeurIPS, 2023. paper, code

    Ye, Haoran and Wang, Jiarui and Cao, Zhiguang and Liang, Helan and Li, Yong

  9. Winner Takes It All: Training Performant RL Populations for Combinatorial Optimization NeurIPS, 2023. paper

    Grinsztajn, Nathan and Furelos-Blanco, Daniel and Surana, Shikha and Bonnet, Cl{'e}ment and Barrett, Thomas D

  10. Combinatorial Optimization with Policy Adaptation using Latent Space Search NeurIPS, 2023. paper

    Chalumeau, Felix and Surana, Shikha and Bonnet, Cl{'e}ment and Grinsztajn, Nathan and Pretorius, Arnu and Laterre, Alexandre and Barrett, Thomas D

  11. Neural DAG Scheduling via One-Shot Priority Sampling ICLR, 2023. paper

    Jeon, Wonseok and Gagrani, Mukul and Bartan, Burak and Zeng, Weiliang Will and Teague, Harris and Zappi, Piero and Lott, Christopher

  12. Robust Scheduling with GFlowNets ICLR, 2023. paper

    Zhang, David W and Rainone, Corrado and Peschl, Markus and Bondesan, Roberto

  13. Continual Task Allocation in Meta-Policy Network via Sparse Prompting ICML, 2023. paper

    Yang, Yijun, Tianyi Zhou, Jing Jiang, Guodong Long and Yuhui Shi.

  14. Applicability of Neural Combinatorial Optimization: A Critical View TELO, 2024. journal, code

    Andoni I. Garmendia, Josu Ceberio, Alexander Mendiburu

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