Research on Dynamic Scheduling Method for Reentrant Hybrid Flow Shop with Batch Processing Machines

The reentrant hybrid flow shop scheduling with batch processing machines necessitates addressing the integrated scheduling of single processing and batch processing machines. Allocating limited batch processing machine resources in a reentrant production system significantly increases the complexity of the problem. Thus, a mathematical model is established in this study with optimization objectives focused on enterprise production efficiency, customer satisfaction, equipment energy costs, and scheduling robustness. An improved decomposition-based multi-objective optimization algorithm is proposed to solve this problem model. Initially, for the batch processing sub-problem, a variable threshold batching strategy based on sequences and heuristic rules is introduced to explore optimal batch size grouping schemes. Subsequently, during the updating process of decomposed sub-problems, to prevent the algorithm from getting trapped in local optima, six neighborhood structures along with corresponding variable neighborhood search strategies are designed to enhance the algorithm's search capability. Additionally, to address the issue of reduced population diversity in the later iterations of the algorithm, reinforced search techniques are applied to the elite solution set to further enhance the algorithm's depth search capability and population diversity. Finally, the effectiveness of the variable threshold batching strategy and the improved decomposition-based multi-objective optimization algorithm in solving this problem is validated across various test cases.