Abstract
Aiming at the distributed permutation flow shop scheduling problem (DPFSP), a mathematical model to minimize the maximum completion time was established, and a scheduling method based on deep reinforcement learning was proposed. Firstly, a factory allocation rule based on the K-means algorithm is designed to generate initial factory allocation results. Secondly, according to the characteristics of DPFSP, state variables and actions are designed to more effectively guide the interaction between agents and the scheduling environment. Deep Q-network (DQN) is used to fit the nonlinear relationship between state and action during the training process, enabling agents to quickly solve problems of different scales after completing training. Finally, simulation experiments verify that the proposed scheduling method can effectively solve the DPFSP problem.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lillicrap, T.P., et al.: Continuous control with deep reinforcement learning. Computer (2015)
Mnih, V., et al.: Playing Atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602 (2013)
Andrychowicz, M., et al.: Learning to learn by gradient descent by gradient descent (2016)
Pan, Z., Wang, L., Wang, J., Lu, J.: Deep reinforcement learning based optimization algorithm for permutation flow-shop scheduling. IEEE Trans. Emerg. Top. Comput. Intell. (2021)
Naderi, B., Ruiz, R.: The distributed permutation flowshop scheduling problem. Comput. Oper. Res. 37(4), 754–768 (2010)
Fernandez-Viagas, V., Perez-Gonzalez, P., Framinan, J.M.: The distributed permutation flow shop to minimise the total flowtime. Comput. Ind. Eng. 118, 464–477 (2018)
Ruiz, R., Pan, Q.-K., Naderi, B.: Iterated Greedy methods for the distributed permutation flowshop scheduling problem. Omega 83, 213–222 (2019)
Shiue, Y.-R., Lee, K.-C., Su, C.-T.: Real-time scheduling for a smart factory using a reinforcement learning approach. Comput. Ind. Eng. 125, 604–614 (2018)
Lee, Y.H., Lee, S.: Deep reinforcement learning based scheduling within production plan in semiconductor fabrication. Expert Syst. Appl. 191, 116222 (2022)
Wang, S.-Y., Wang, L., Liu, M., Xu, Y.: An effective estimation of distribution algorithm for solving the distributed permutation flow-shop scheduling problem. Int. J. Prod. Econ. 145(1), 387–396 (2013)
Panwalkar, S.S., Iskander, W.: A survey of scheduling rules. Oper. Res. 25(1), 45–61 (1977)
Sabuncuoglu, I.: A study of scheduling rules of flexible manufacturing systems: a simulation approach. Int. J. Prod. Res. 36(2), 527–546 (1998)
Acknowledgment
This research was supported by the National Natural Science Foundation of China (62173169 and 61963022) and the Basic Research Key Project of Yunnan Province (202201AS070030).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Wang, Y., Qian, B., Hu, R., Yang, Y., Chen, W. (2023). Deep Reinforcement Learning for Solving Distributed Permutation Flow Shop Scheduling Problem. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14086. Springer, Singapore. https://doi.org/10.1007/978-981-99-4755-3_29
Download citation
DOI: https://doi.org/10.1007/978-981-99-4755-3_29
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-4754-6
Online ISBN: 978-981-99-4755-3
eBook Packages: Computer ScienceComputer Science (R0)