Ze-Peng Liu

A Two-Layer Multi-Agent Deep Reinforcement Learning Framework for Flexible Job-Shop Scheduling with Multiple Batch-Processing Machines

An extended FJSP with multiple BPMs is formed and an end-to-end two-layer multi-agent deep reinforcement learning framework is proposed, supporting the framework as an effective scheduling approach for deterministic FJSP with BPMs and indicating cross-scale generalization across evaluated instances.