Two-Stage Intelligent Optimization Algorithm for Hot-Dip Galvanizing Batch Scheduling

In response to the challenges in batch production, customization of user demands, and the complexities of the hot-dip galvanizing process in practical steel production, a two-stage intelligent optimization algorithm (TIOA) is proposed. TIOA aims to enhance production efficiency and resource utilization by reducing internal variety and specification switches in the plans while smoothing the transition between plans. In hot-dip galvanizing production, the imperative is to minimize indirect continuity costs between materials while considering factors such as material priority, rolling schedule quantities, and critical process specifications. However, there exists a contradiction in the requirements for inter-batch continuity and intra-batch continuity in bulk production planning. To address this contradiction, in the first stage of TIOA, a variable neighborhood search algorithm is proposed to identify transition materials based on the characteristics of inter-batch transitions. Subsequently, a heuristic algorithm is employed to obtain the initial solution to the problem. In the second stage of TIOA, an adaptive neighborhood genetic algorithm is introduced. Mutation operations from genetic algorithms are embedded into an adaptive neighborhood search algorithm, taking advantage of the genetic algorithm’s strong exploration capability and the rapid local convergence of the adaptive neighborhood. Finally, the algorithm is validated using actual data from hot-dip galvanization production. The obtained results are compared with those derived from an expert-based heuristic approach, confirming the effectiveness of TIOA.