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A reinforcement learning based approach for multi-projects scheduling in cloud manufacturing

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  • Shengkai Chen
  • Shuiliang Fang
  • Renzhong Tang

Abstract

This paper discussed the multi-projects scheduling problem in Cloud Manufacturing system, where each of the projects is a set of interrelated tasks, and these projects need to be scheduled timely and carefully. However, scheduling massive projects can be challenging due to the uneven distribution of the services and the uncertain arrival of projects. Therefore, we (1) established a dual-objectives optimisation model to minimise both the total makespan and the logistical distance; (2) proposed a Reinforcement Learning based Assigning Policy (RLAP) approach to obtain non-dominated solution set; (3) designed a dynamic state representing an algorithm for agents to determine their decision environment when using RLAP. Experiment results show that RLAP can adjust the distribution of service load according to the nearby tasks, and the schedule quality is improved by $ 32.1\% $ 32.1% and $ 5.7\% $ 5.7% compared with NSGA-II and Q-learning, respectively. Besides, the RLAP method has the ability to schedule stochastically arriving projects.

Suggested Citation

  • Shengkai Chen & Shuiliang Fang & Renzhong Tang, 2019. "A reinforcement learning based approach for multi-projects scheduling in cloud manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 57(10), pages 3080-3098, May.
    • Handle: RePEc:taf:tprsxx:v:57:y:2019:i:10:p:3080-3098
    DOI: 10.1080/00207543.2018.1535205
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    Cited by:

    1. Kaibo Liang & Li Zhou & Jianglong Yang & Huwei Liu & Yakun Li & Fengmei Jing & Man Shan & Jin Yang, 2023. "Research on a Dynamic Task Update Assignment Strategy Based on a “Parts to Picker” Picking System," Mathematics, MDPI, vol. 11(7), pages 1-29, March.

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