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Dynamic multi-objective flexible job-shop scheduling via dynamic dual attention network-based deep reinforcement learning
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Dynamic multi-objective flexible job-shop scheduling via dynamic dual attention network-based deep reinforcement learning
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Journal Article
Dynamic multi-objective flexible job-shop scheduling via dynamic dual attention network-based deep reinforcement learning
2025
Overview
In today’s complex and unpredictable manufacturing environment, dynamic events such as new job arrivals and urgent insertions can occur at any time. Meanwhile, multiple conflicting objectives need to be optimized simultaneously in the flexible job-shop scheduling problem (FJSP). This necessitates real-time multi-objective FJSP scheduling methods that can balance time efficiency and solution quality. Therefore, this paper proposes a dynamic multi-objective FJSP method based on a dynamic dual-attention network (DDAN) and multi-agent reinforcement learning. The DDAN captures global feature representations of operations and machines and deeply explores their complex dependencies. By integrating dynamic attention coefficients with job urgency factors, the DDAN can respond in real-time to dynamic events and provide effective support for subsequent decision-making. Additionally, a multi-agent reinforcement learning framework is introduced to balance the conflicting objectives of makespan and average tardiness. The higher level agent is designed to optimize makespan, while the lower level agent focuses on minimizing average tardiness. Collaboration between the two agents is facilitated by a carefully designed state-sharing mechanism and distinct reward functions, enabling more flexible and efficient resolution of conflicts in multi-objective optimization. Extensive testing has demonstrated the exceptional performance of the proposed method, which consistently and rapidly converges to the optimal solution, outperforming traditional priority dispatching rules (PDRs), metaheuristic algorithms, and state-of-the-art reinforcement learning methods, particularly in handling large-scale test instances.
Publisher
Springer Nature B.V
Subject
/ Lateness
