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This paper proposes a sliding-window-based reinforcement learning framework (SWRL) for end-to-end online scheduling in dynamic assembly flow shop scheduling with complex kitting constraints, demonstrating consistent tardiness reductions over classical dispatching rules and existing deep reinforcement learning methods on real-world instances.