Adaptive NSGA-II based flexible job shop scheduling with dual resource constraints

Aiming at the multi-objective dual-resource flexible job shop scheduling problem, a model with the optimization objectives of minimizing the maximum completion time, the maximum machine load and the total cost is established, and an improved NSGA-II algorithm with adaptive crossover and mutation operators is proposed. The algorithm firstly designs a hybrid initialization strategy combined with three-stage coding to improve the quality of the initial population; then introduces a hybrid crossover and mutation strategy with adaptive operator to iterate different layers of coding separately to improve the global search capability of the algorithm; finally, the effectiveness of the algorithm is verified by example simulation. The results show that the new scheme obtained by using the improved NSGA-II algorithm is significantly improved in terms of maximum completion time, machine load and processing cost, which effectively improves the production management efficiency.