首页|Production-logistics collaborative scheduling in dynamic flexible job shops using nested-hierarchical deep reinforcement learning
Production-logistics collaborative scheduling in dynamic flexible job shops using nested-hierarchical deep reinforcement learning
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NETL
NSTL
Elsevier
Effective manufacturing in flexible job shops often requires collaboratively organizing production and logistics activities. This necessitates a thorough exploration of corresponding collaborative scheduling problem. However, extant studies remain relatively preliminary, not only neglecting the inevitable disturbances in real-world but also failing to satisfy the essential need for collaboration, that is, to simultaneously optimize both activities' objectives. Therefore, this study proposes a novel production-logistics collaborative scheduling problem for dynamic flexible job shops, in which the common yet underappreciated disturbance of logistics equipment breakdowns is meticulously considered, and two typical objectives individually pursued by two activities are optimized simultaneously. To solve the proposed problem, a nested-hierarchical deep reinforcement learning method is developed. In this method, a new nested-hierarchical framework that rationally deploys multiple agents is designed to facilitate the required multi-objective optimization while ensuring the practicality of decision-making process. Based on this framework, appropriate state features, actions, and reward functions are devised for each agent, and a training mechanism based on multi-agent proximal policy optimization is proposed to train agents effectively. Experiments in an aviation component production shop are conducted to confirm the effectiveness of proposed method and problem.