A novel predictive-reactive scheduling method for parallel batch processor lot-sizing and scheduling with sequence-dependent setup time

In practical printed circuit boards (PCB) drilling production systems, multiple spindle processing capabilities and uncertainties are commonplace. This paper addresses predictive-reactive scheduling, considering parallel batch processor lot-sizing and scheduling with sequence-dependent setup times in a dynamic environment. We propose a predictive scheduling algorithm named S-AGAIG to balance machine spindle utilization and order tardiness in steps. Multiple splitting solutions are first formed by using a split heuristic algorithm to size the sub -lots of orders. Then the adaptive genetic algorithm with iterated greedy search is applied to select the solutions and scheduling. Furthermore, we present a schedule repair strategy based on sub -lot co-processing considering the impact of critical sub -lots, and construct a scheduling stability metric for rescheduling. In 36 sets of case experiments encompassing diverse load and machine type configurations, S-AGAIG showcases a 21.27% enhancement in average tardiness performance when compared to its closest rival. Across 28 cases involving varying disturbances, the framework demonstrates exceptional robustness.