Hierarchical collaborative multi-agent reinforcement learning for hot-rolling production planning with order-splitting flexibility

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In mass personalized hot rolling, intricate constraints cause load imbalances and low order fulfilment. While order splitting alleviates these bottlenecks, it increases changeover frequency and planning complexity. We propose a bi-level model: the upper level optimizes production cost and time, while the lower minimizes makespan and shutdowns. To address the strong coupling between allocation, splitting and sequencing, a hierarchical cooperative multi-agent reinforcement learning (HCMARL) algorithm is developed using a cross-module closed-loop decoupling mechanism. Decoupled adaptive multi-objective deep deterministic policy gradient (DAM-DDPG) and collaborative constraint optimization Monte Carlo tree search (CCO-MCTS) process continuous allocation and splitting decisions to generate physical constraints. These guide the lower-level flexible coupling production planning optimization (FC-PPO), combined with a corrected generalized advantage estimation (CE-GAE), to synchronize maintenance windows and process adjustments. Experiments show HCMARL closely approaches Gurobi’s exact solutions with significantly higher computational efficiency for industrial-scale problems.