Research on adaptive intelligent scheduling algorithm for complex production environment and its application

Aiming at the scheduling problem in complex production environment, an adaptive intelligent scheduling algorithm framework is proposed. This study designs an algorithm framework including task information collection, information processing and analysis, scheduling strategy formulation, execution and feedback, and realizes real-time optimization of scheduling strategy through dynamic weight adjustment and resource utilization prediction by combining task-resource multi-objective optimization model. In the aspect of algorithm implementation, the improved NSGA-III algorithm is used for multi-objective optimization, and the LSTM-GRU hybrid prediction model is introduced to improve the prediction accuracy of task arrival and resource demand. The experimental results show that the algorithm is significantly superior to the traditional methods in task completion time, resource utilization, cost and load balance, especially in dynamic disturbance conditions. In addition, in the practical application of semiconductor wafer manufacturing, the algorithm significantly shortens the production cycle, reduces the equipment idle rate and unit energy consumption by optimizing the energy consumption of equipment preheating and process dependence, and verifies its effectiveness and applicability in complex production environment.