How to cite this paper
Xia, M., Liu, H., Li, M & Wang, L. (2023). A dynamic scheduling method with Conv-Dueling and generalized representation based on reinforcement learning.International Journal of Industrial Engineering Computations , 14(4), 805-820.
Refrences
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Bellman, R. (1957). A Markovian decision process. Journal of mathematics and mechanics, 6(5), 679-684.
Brucker, P., & Schlie, R. (1990). Job-shop scheduling with multipurpose machines. Computing.
Burggräf, P., Wagner, J., Saßmannshausen, T., Ohrndorf, D. & subramani, K. (2022). Multi-agent-based deep reinforcement learning for dynamic flexible job shop scheduling. Procedia CIRP, 112, 57-62.
Chang, J., Yu, D., Hu, Y., He, W., & Yu, H. (2022). Deep reinforcement learning for dynamic flexible job shop scheduling with random job arrival. Processes, 10(4), 760.
Chao, L.-F. & Lapaugh, A. (1993). Rotation scheduling: A loop pipelining algorithm. Proceedings of the 30th international Design Automation Conference, 566-572.
Gao, Y., Rong, H., & Huang, J. Z. (2005). Adaptive grid job scheduling with genetic algorithms. Future Generation Computer Systems, 21(1), 151-161.
Garey, M. R., Johnson, D. S., & Sethi, R. (1976). The complexity of flowshop and jobshop scheduling. Mathematics of operations research, 1(2), 117-129.
Gonçalves, J. F., de Magalhães Mendes, J. J., & Resende, M. G. (2005). A hybrid genetic algorithm for the job shop scheduling problem. European journal of operational research, 167(1), 77-95.
Han, B. A., & Yang, J. J. (2020). Research on adaptive job shop scheduling problems based on dueling double DQN. IEEE Access, 8, 186474-186495.
Jianfang, C., Junjie, C., & Qingshan, Z. (2014). An optimized scheduling algorithm on a cloud workflow using a discrete particle swarm. Cybernetics and Information Technologies, 14(1), 25-39.
Lee, Z., Wang, Y., & Zhou, W. (2011, August). A dynamic priority scheduling algorithm on service request scheduling in cloud computing. In Proceedings of 2011 International Conference on Electronic & Mechanical Engineering and Information Technology (Vol. 9, pp. 4665-4669). IEEE.
Liu, R., Piplani, R., & Toro, C. (2022). Deep reinforcement learning for dynamic scheduling of a flexible job shop. International Journal of Production Research, 60(13), 4049-4069.
Liu, Z., Chen, W., Zhang, C., Yang, C., & Cheng, Q. (2021). Intelligent scheduling of a feature-process-machine tool super network based on digital twin workshop. Journal of manufacturing systems, 58, 157-167.
Lin, T. L., Horng, S. J., Kao, T. W., Chen, Y. H., Run, R. S., Chen, R. J., ... & Kuo, I. H. (2010). An efficient job-shop scheduling algorithm based on particle swarm optimization. Expert Systems with Applications, 37(3), 2629-2636.
Luo, S. (2020). Dynamic scheduling for flexible job shop with new job insertions by deep reinforcement learning. Applied Soft Computing, 91, 106208.
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., ... & Hassabis, D. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529-533.
Ouelhadj, D., & Petrovic, S. (2009). A survey of dynamic scheduling in manufacturing systems. Journal of scheduling, 12, 417-431.
Qu, S., Wang, J., & Shivani, G. (2016, September). Learning adaptive dispatching rules for a manufacturing process system by using reinforcement learning approach. In 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA) (pp. 1-8). IEEE.
Rahmani Hosseinabadi, A. A., Vahidi, J., Saemi, B., Sangaiah, A. K., & Elhoseny, M. (2019). Extended genetic algorithm for solving open-shop scheduling problem. Soft computing, 23, 5099-5116.
Shahrabi, J., Adibi, M. A., & Mahootchi, M. (2017). A reinforcement learning approach to parameter estimation in dynamic job shop scheduling. Computers & Industrial Engineering, 110, 75-82.
Shi, D., Fan, W., Xiao, Y., Lin, T., & Xing, C. (2020). Intelligent scheduling of discrete automated production line via deep reinforcement learning. International journal of production research, 58(11), 3362-3380.
Shiue, Y. R., Lee, K. C., & Su, C. T. (2018). Real-time scheduling for a smart factory using a reinforcement learning approach. Computers & Industrial Engineering, 125, 604-614.
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., ... & Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484-489.
Song, W., Chen, X., Li, Q., & Cao, Z. (2022). Flexible Job-Shop Scheduling via Graph Neural Network and Deep Reinforcement Learning. IEEE Transactions on Industrial Informatics, 19(2), 1600-1610.
Sutton, R. S. & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT press.
Tassel, P., Gebser, M., & Schekotihin, K. (2021). A reinforcement learning environment for job-shop scheduling. arXiv preprint arXiv:2104.03760.
Wang, H. X., & Yan, H. S. (2016). An interoperable adaptive scheduling strategy for knowledgeable manufacturing based on SMGWQ-learning. Journal of Intelligent Manufacturing, 27, 1085-1095.
Wang, L., Hu, X., Wang, Y., Xu, S., Ma, S., Yang, K., ... & Wang, W. (2021). Dynamic job-shop scheduling in smart manufacturing using deep reinforcement learning. Computer Networks, 190, 107969.
Wang, Y. C., & Usher, J. M. (2004). Learning policies for single machine job dispatching. Robotics and Computer-Integrated Manufacturing, 20(6), 553-562.
Wang, Y. F. (2020). Adaptive job shop scheduling strategy based on weighted Q-learning algorithm. Journal of Intelligent Manufacturing, 31(2), 417-432.
Wang, Z., Schaul, T., Hessel, M., Hasselt, H., Lanctot, M., & Freitas, N. (2016, June). Dueling network architectures for deep reinforcement learning. In International conference on machine learning (pp. 1995-2003). PMLR.
Waschneck, B., Reichstaller, A., Belzner, L., Altenmüller, T., Bauernhansl, T., Knapp, A., & Kyek, A. (2018, April). Deep reinforcement learning for semiconductor production scheduling. In 2018 29th annual SEMI advanced semiconductor manufacturing conference (ASMC) (pp. 301-306). IEEE.
Wei, Y., & Zhao, M. (2004, December). Composite rules selection using reinforcement learning for dynamic job-shop scheduling. In IEEE Conference on Robotics, Automation and Mechatronics, 2004. (Vol. 2, pp. 1083-1088). IEEE.
Zhang, W., & Dietterich, T. G. (1995, August). A reinforcement learning approach to job-shop scheduling. In IJCAI (Vol. 95, pp. 1114-1120).
Yang, H., & Yan, H. (2007, August). An adaptive policy of dynamic scheduling in knowledgeable manufacturing environment. In 2007 IEEE International Conference on Automation and Logistics (pp. 835-840). IEEE.
Zhang, C., Song, W., Cao, Z., Zhang, J., Tan, P. S., & Chi, X. (2020a). Learning to dispatch for job shop scheduling via deep reinforcement learning. Advances in Neural Information Processing Systems, 33, 1621-1632.
Zhang, G., Hu, Y., Sun, J., & Zhang, W. (2020b). An improved genetic algorithm for the flexible job shop scheduling problem with multiple time constraints. Swarm and Evolutionary Computation, 54, 100664.
Zhang, S., Wu, Y., Ogai, H., Inujima, H., & Tateno, S. (2021). Tactical decision-making for autonomous driving using dueling double deep Q network with double attention. IEEE Access, 9, 151983-151992.
Zhang, Y., Zhu, H., Tang, D., Zhou, T., & Gui, Y. (2022). Dynamic job shop scheduling based on deep reinforcement learning for multi-agent manufacturing systems. Robotics and Computer-Integrated Manufacturing, 78, 102412.
Zhao, F., Qin, S., Yang, G., Ma, W., Zhang, C., & Song, H. (2019). A factorial based particle swarm optimization with a population adaptation mechanism for the no-wait flow shop scheduling problem with the makespan objective. Expert Systems with Applications, 126, 41-53.
Bellman, R. (1957). A Markovian decision process. Journal of mathematics and mechanics, 6(5), 679-684.
Brucker, P., & Schlie, R. (1990). Job-shop scheduling with multipurpose machines. Computing.
Burggräf, P., Wagner, J., Saßmannshausen, T., Ohrndorf, D. & subramani, K. (2022). Multi-agent-based deep reinforcement learning for dynamic flexible job shop scheduling. Procedia CIRP, 112, 57-62.
Chang, J., Yu, D., Hu, Y., He, W., & Yu, H. (2022). Deep reinforcement learning for dynamic flexible job shop scheduling with random job arrival. Processes, 10(4), 760.
Chao, L.-F. & Lapaugh, A. (1993). Rotation scheduling: A loop pipelining algorithm. Proceedings of the 30th international Design Automation Conference, 566-572.
Gao, Y., Rong, H., & Huang, J. Z. (2005). Adaptive grid job scheduling with genetic algorithms. Future Generation Computer Systems, 21(1), 151-161.
Garey, M. R., Johnson, D. S., & Sethi, R. (1976). The complexity of flowshop and jobshop scheduling. Mathematics of operations research, 1(2), 117-129.
Gonçalves, J. F., de Magalhães Mendes, J. J., & Resende, M. G. (2005). A hybrid genetic algorithm for the job shop scheduling problem. European journal of operational research, 167(1), 77-95.
Han, B. A., & Yang, J. J. (2020). Research on adaptive job shop scheduling problems based on dueling double DQN. IEEE Access, 8, 186474-186495.
Jianfang, C., Junjie, C., & Qingshan, Z. (2014). An optimized scheduling algorithm on a cloud workflow using a discrete particle swarm. Cybernetics and Information Technologies, 14(1), 25-39.
Lee, Z., Wang, Y., & Zhou, W. (2011, August). A dynamic priority scheduling algorithm on service request scheduling in cloud computing. In Proceedings of 2011 International Conference on Electronic & Mechanical Engineering and Information Technology (Vol. 9, pp. 4665-4669). IEEE.
Liu, R., Piplani, R., & Toro, C. (2022). Deep reinforcement learning for dynamic scheduling of a flexible job shop. International Journal of Production Research, 60(13), 4049-4069.
Liu, Z., Chen, W., Zhang, C., Yang, C., & Cheng, Q. (2021). Intelligent scheduling of a feature-process-machine tool super network based on digital twin workshop. Journal of manufacturing systems, 58, 157-167.
Lin, T. L., Horng, S. J., Kao, T. W., Chen, Y. H., Run, R. S., Chen, R. J., ... & Kuo, I. H. (2010). An efficient job-shop scheduling algorithm based on particle swarm optimization. Expert Systems with Applications, 37(3), 2629-2636.
Luo, S. (2020). Dynamic scheduling for flexible job shop with new job insertions by deep reinforcement learning. Applied Soft Computing, 91, 106208.
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., ... & Hassabis, D. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529-533.
Ouelhadj, D., & Petrovic, S. (2009). A survey of dynamic scheduling in manufacturing systems. Journal of scheduling, 12, 417-431.
Qu, S., Wang, J., & Shivani, G. (2016, September). Learning adaptive dispatching rules for a manufacturing process system by using reinforcement learning approach. In 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA) (pp. 1-8). IEEE.
Rahmani Hosseinabadi, A. A., Vahidi, J., Saemi, B., Sangaiah, A. K., & Elhoseny, M. (2019). Extended genetic algorithm for solving open-shop scheduling problem. Soft computing, 23, 5099-5116.
Shahrabi, J., Adibi, M. A., & Mahootchi, M. (2017). A reinforcement learning approach to parameter estimation in dynamic job shop scheduling. Computers & Industrial Engineering, 110, 75-82.
Shi, D., Fan, W., Xiao, Y., Lin, T., & Xing, C. (2020). Intelligent scheduling of discrete automated production line via deep reinforcement learning. International journal of production research, 58(11), 3362-3380.
Shiue, Y. R., Lee, K. C., & Su, C. T. (2018). Real-time scheduling for a smart factory using a reinforcement learning approach. Computers & Industrial Engineering, 125, 604-614.
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., ... & Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484-489.
Song, W., Chen, X., Li, Q., & Cao, Z. (2022). Flexible Job-Shop Scheduling via Graph Neural Network and Deep Reinforcement Learning. IEEE Transactions on Industrial Informatics, 19(2), 1600-1610.
Sutton, R. S. & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT press.
Tassel, P., Gebser, M., & Schekotihin, K. (2021). A reinforcement learning environment for job-shop scheduling. arXiv preprint arXiv:2104.03760.
Wang, H. X., & Yan, H. S. (2016). An interoperable adaptive scheduling strategy for knowledgeable manufacturing based on SMGWQ-learning. Journal of Intelligent Manufacturing, 27, 1085-1095.
Wang, L., Hu, X., Wang, Y., Xu, S., Ma, S., Yang, K., ... & Wang, W. (2021). Dynamic job-shop scheduling in smart manufacturing using deep reinforcement learning. Computer Networks, 190, 107969.
Wang, Y. C., & Usher, J. M. (2004). Learning policies for single machine job dispatching. Robotics and Computer-Integrated Manufacturing, 20(6), 553-562.
Wang, Y. F. (2020). Adaptive job shop scheduling strategy based on weighted Q-learning algorithm. Journal of Intelligent Manufacturing, 31(2), 417-432.
Wang, Z., Schaul, T., Hessel, M., Hasselt, H., Lanctot, M., & Freitas, N. (2016, June). Dueling network architectures for deep reinforcement learning. In International conference on machine learning (pp. 1995-2003). PMLR.
Waschneck, B., Reichstaller, A., Belzner, L., Altenmüller, T., Bauernhansl, T., Knapp, A., & Kyek, A. (2018, April). Deep reinforcement learning for semiconductor production scheduling. In 2018 29th annual SEMI advanced semiconductor manufacturing conference (ASMC) (pp. 301-306). IEEE.
Wei, Y., & Zhao, M. (2004, December). Composite rules selection using reinforcement learning for dynamic job-shop scheduling. In IEEE Conference on Robotics, Automation and Mechatronics, 2004. (Vol. 2, pp. 1083-1088). IEEE.
Zhang, W., & Dietterich, T. G. (1995, August). A reinforcement learning approach to job-shop scheduling. In IJCAI (Vol. 95, pp. 1114-1120).
Yang, H., & Yan, H. (2007, August). An adaptive policy of dynamic scheduling in knowledgeable manufacturing environment. In 2007 IEEE International Conference on Automation and Logistics (pp. 835-840). IEEE.
Zhang, C., Song, W., Cao, Z., Zhang, J., Tan, P. S., & Chi, X. (2020a). Learning to dispatch for job shop scheduling via deep reinforcement learning. Advances in Neural Information Processing Systems, 33, 1621-1632.
Zhang, G., Hu, Y., Sun, J., & Zhang, W. (2020b). An improved genetic algorithm for the flexible job shop scheduling problem with multiple time constraints. Swarm and Evolutionary Computation, 54, 100664.
Zhang, S., Wu, Y., Ogai, H., Inujima, H., & Tateno, S. (2021). Tactical decision-making for autonomous driving using dueling double deep Q network with double attention. IEEE Access, 9, 151983-151992.
Zhang, Y., Zhu, H., Tang, D., Zhou, T., & Gui, Y. (2022). Dynamic job shop scheduling based on deep reinforcement learning for multi-agent manufacturing systems. Robotics and Computer-Integrated Manufacturing, 78, 102412.
Zhao, F., Qin, S., Yang, G., Ma, W., Zhang, C., & Song, H. (2019). A factorial based particle swarm optimization with a population adaptation mechanism for the no-wait flow shop scheduling problem with the makespan objective. Expert Systems with Applications, 126, 41-53.