A Weak-Robust Modeling and Knowledge-Driven Solution Approach for Scheduling Margin Calculation of Hot Rolling...

Uncertainty in the production process is the primary factor affecting the execution of hot rolling scheduling plans. Although robust optimization methods are commonly employed to address this challenge, existing approaches struggle to quantify the schedulable production capacity (time margin) over longer time scales. Furthermore, they fail to adequately consider planning delays, the capacity constraints of upstream and downstream processes, and various other uncertainties. Therefore, a weak-robust modeling and knowledge-driven solution framework for scheduling margin calculation (WRoKS-SMC) is proposed. To tackle the difficulties of solving continuous uncertainty scenarios, the weak-robust optimization problem is reformulated into a discretized Markov decision process (MDP) model. Considering multidimensional slab features such as material specifications, time margin, and logistics among multiple processes, a multihierarchical graph convolutional network (MH-GCN) is proposed to encode these features into scalable graph feature vectors that serve as the state representation in the MDP. In order to integrate the hard constraints of production scheduling into the MDP model, a knowledge-driven imitation learning framework (KDIL) is proposed, where the feasible solution is approximated by minimizing the cross-entropy loss between the expert strategies and policy network decisions, ensuring the satisfaction of hard constraints. To verify the effectiveness of the proposed method, experiments were conducted using practical data from a domestic steel enterprise. The results show that the proposed method outperforms the state-of-the-art approaches in terms of scheduling costs and margin calculation under uncertainty scenarios.