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A deep reinforcement learning based algorithm for a distributed precast concrete production scheduling

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  • Du, Yu
  • Li, Jun-qing

Abstract

The environmental-friendly production demands higher manufacturing efficiency and lower energy cost; therefore, time-of-use electricity price constraint and distributed production have attracted more attention. For concrete precast architecture construction, the tardiness penalty and warehouse cost cannot be ignored, which should be optimized for lower cost. In this study, the concrete precast process is investigated as the group scheduling of a distributed flexible job shop problem. Each concrete precast is managed in a group, the setup time between different groups is considered. Two objectives, total weighted earliness and tardiness and total time-of-use electricity cost are minimized, simultaneously. To solve the integrated problem, three coordinated double deep Q-networks (DQN) are applied, which are organized as a learn-to-improve reinforcement learning approach. For distributed scheduling problem, operators in a single factory or in multiple factories differs in solution improvement; so selection DQN is designed to decide the type of the operators according to scheduling circumstances. Other two DQNs, i.e., local DQN and global DQN, are to select the optimization operators in one factory or in multiple factories, respectively. Furthermore, two solution refinement strategies are designed to decrease the objectives after reinforcement learning component. Numerical experiment and statistical analysis suggest that the proposed deep reinforcement learning based algorithm has superiority in solving the considered problem.

Suggested Citation

  • Du, Yu & Li, Jun-qing, 2024. "A deep reinforcement learning based algorithm for a distributed precast concrete production scheduling," International Journal of Production Economics, Elsevier, vol. 268(C).
  • Handle: RePEc:eee:proeco:v:268:y:2024:i:c:s0925527323003341
    DOI: 10.1016/j.ijpe.2023.109102
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    References listed on IDEAS

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    1. De Giovanni, L. & Pezzella, F., 2010. "An Improved Genetic Algorithm for the Distributed and Flexible Job-shop Scheduling problem," European Journal of Operational Research, Elsevier, vol. 200(2), pages 395-408, January.
    2. Renke Liu & Rajesh Piplani & Carlos Toro, 2022. "Deep reinforcement learning for dynamic scheduling of a flexible job shop," International Journal of Production Research, Taylor & Francis Journals, vol. 60(13), pages 4049-4069, July.
    3. Najafzad, Hamid & Davari-Ardakani, Hamed & Nemati-Lafmejani, Reza, 2019. "Multi-skill project scheduling problem under time-of-use electricity tariffs and shift differential payments," Energy, Elsevier, vol. 168(C), pages 619-636.
    4. Hao-Chin Chang & Tung-Kuan Liu, 2017. "Optimisation of distributed manufacturing flexible job shop scheduling by using hybrid genetic algorithms," Journal of Intelligent Manufacturing, Springer, vol. 28(8), pages 1973-1986, December.
    5. Francisco Yuraszeck & Gonzalo Mejía & Jordi Pereira & Mariona Vilà, 2022. "A Novel Constraint Programming Decomposition Approach for the Total Flow Time Fixed Group Shop Scheduling Problem," Mathematics, MDPI, vol. 10(3), pages 1-26, January.
    6. Zhao, Liyuan & Yang, Ting & Li, Wei & Zomaya, Albert Y., 2022. "Deep reinforcement learning-based joint load scheduling for household multi-energy system," Applied Energy, Elsevier, vol. 324(C).
    7. Luo, Hao & Du, Bing & Huang, George Q. & Chen, Huaping & Li, Xiaolin, 2013. "Hybrid flow shop scheduling considering machine electricity consumption cost," International Journal of Production Economics, Elsevier, vol. 146(2), pages 423-439.
    8. Shijin Wang & Zhanguo Zhu & Kan Fang & Feng Chu & Chengbin Chu, 2018. "Scheduling on a two-machine permutation flow shop under time-of-use electricity tariffs," International Journal of Production Research, Taylor & Francis Journals, vol. 56(9), pages 3173-3187, May.
    9. Mohammad Reza Hosseinzadeh & Mehdi Heydari & Mohammad Mahdavi Mazdeh, 2022. "Mathematical modeling and two metaheuristic algorithms for integrated process planning and group scheduling with sequence-dependent setup time," Operational Research, Springer, vol. 22(5), pages 5055-5105, November.
    10. Minh Hung Ho & Faicel Hnaien & Frederic Dugardin, 2021. "Electricity cost minimisation for optimal makespan solution in flow shop scheduling under time-of-use tariffs," International Journal of Production Research, Taylor & Francis Journals, vol. 59(4), pages 1041-1067, February.
    11. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
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