A Sliding-Window-Based Reinforcement Learning for Dynamic Assembly Flow Shop Scheduling with Multi-Product Delivery
This paper proposes a sliding-window-based reinforcement learning (SWRL) framework for end-to-end online scheduling in flexible assembly flow shop scheduling with complex kitting constraints. It formulates the problem as a heterogeneous graph Markov decision process, and integrates sliding-window filtering, spatiotemporal graph encoding, and dynamic action mapping. Experiments on real-world home appliance manufacturing data show that SWRL achieves consistent tardiness reductions over classical dispatching rules and existing deep reinforcement learning methods, with robust performance across varying resource configurations.
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[Submitted on 3 Jul 2026]
Title:A Sliding-Window-Based Reinforcement Learning for Dynamic Assembly Flow Shop Scheduling with Multi-Product Delivery
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Abstract:Multi-product kitting delivery imposes significant challenges for real-time scheduling in hybrid manufacturing systems that integrate processing and assembly, as dynamic order arrivals simultaneously alter supply dependencies and the set of feasible job-machine assignments. This paper proposes a sliding-window-based reinforcement learning (SWRL) framework for end-to-end online scheduling in the flexible assembly flow shop scheduling problem with complex kitting constraints. The problem is formulated as a heterogeneous graph-based Markov decision process that captures the dual-layer kitting structure and the tail-product bottleneck dynamics that produce a sparse reward landscape. To address the resulting challenges, SWRL integrates a sliding-window filtering mechanism that filters inactive nodes and prioritizes kitting-critical operations, a spatiotemporal graph encoding network that tracks bottleneck shifts across consecutive decision states, and a dynamic action mapping module with a constrained waiting strategy that adapts to the changing action space under variable topologies. Experiments on real-world instances from a home appliance manufacturer demonstrate that SWRL achieves consistent tardiness reductions over classical dispatching rules and existing deep reinforcement learning methods, and exhibits robust performance across varying resource configurations, order loads, and arrival concentrations.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.02941 [cs.AI]
(or arXiv:2607.02941v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.02941
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Junhao Qiu [view email] [v1] Fri, 3 Jul 2026 04:27:52 UTC (1,135 KB)
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