Bi-level multi-agent collaborative dynamic scheduling for flexible flow shops with tight temporal and limited buffer constraints

As modern manufacturing systems become increasingly complex, flexible flow shops (FFS) face challenges posed by tightly coupled temporal and buffer constraints, dynamic disturbances, and evolving data distributions. To address these challenges, a novel bi-level multi-agent framework for dynamic scheduling is developed, integrating real-time decision-making with adaptive model maintenance. At the decision level, a graph-based state representation is used to encode production semantics and structural relationships. A two-stage predictive model, consisting of a genetic algorithm-based feature selection process and a multi-head graph attention network combined with a multilayer perceptron, is employed to accurately estimate global optimization objectives. These predictions are then used as reward signals to guide a graph-based proximal policy optimization policy, where a constraint-aware reference mechanism is incorporated to ensure feasibility under coupled temporal and buffer constraints. At the model maintenance level, a sliding window-based multi-level statistical test is used to detect concept drift, and a reinforcement learning-based adaptive retraining strategy is applied to update the model using weighted historical data, enabling sustained performance over time. The framework is validated using 1198 industrial scheduling instances collected from a textile manufacturing workshop. Experimental results demonstrate that the proposed framework outperforms representative metaheuristic and reinforcement learning methods, including GA-SAGE, DCN, D3QN, and industrial scheduling rules. It achieves a high prediction accuracy ( R 2 = 0.93) and a scheduling feasibility rate of 92% in real-world workshop deployment. The results highlight the effectiveness of integrating predictive modeling, constraint-aware policy learning, and drift-adaptive model updating into a unified framework for robust industrial scheduling.