A Rescheduling Method for Flexible Job Shops Based on an Improved Jaya Algorithm

This paper presents a novel method that integrates an improved Jaya algorithm with simulated annealing to address the challenges of rescheduling in flexible job shops, particularly in scenarios involving urgent orders. The approach enhances global search and diversity by dividing the population into three subpopulations, each utilizing distinct mutation operators. Additionally, a reheating mechanism is introduced to effectively escape local optima. Experimental results on flexible job shop scheduling problems demonstrate the algorithm’s superior performance in both static scheduling and rescheduling tasks, achieving improved solution quality and efficiency compared to traditional methods.