A Sliding-Window-Based Reinforcement Learning for Dynamic Assembly Flow Shop Scheduling with Multi-Product Delivery
arXiv:2607.02941v1 Announce Type: new 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
The increasing complexity of hybrid manufacturing systems and multi-product delivery challenges necessitate advanced AI-driven scheduling solutions to optimize real-time operations.
This development offers a significant step towards fully autonomous and highly efficient manufacturing process management, potentially transforming supply chains and production economics.
Traditional scheduling methods are being augmented by adaptive AI frameworks that can dynamically respond to real-time changes in manufacturing, integrating processing and assembly with complex kitting constraints.
- · Hybrid manufacturing companies
- · Logistics and supply chain optimization software providers
- · AI/ML solution developers for industrial applications
- · Legacy manufacturing execution system (MES) providers
- · Companies slow to adopt AI-driven automation
Manufacturing plants can achieve higher throughput and reduced costs through optimized scheduling and resource allocation.
The efficiency gains contribute to more resilient and agile supply chains, capable of adapting quickly to market fluctuations and disruptions.
The widespread adoption of such AI systems could lead to a significant acceleration in industrial automation and potentially a restructuring of global manufacturing labor requirements.
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Read at arXiv cs.AI