Multi-objective Job-shop Dynamic Scheduling Based on Improved Ant Colony Algorithm

The dynamic multi-objective optimization problem has more application in the practical job shop scheduling. This paper uses the Pareto solution of the multi-objective optimization to construct the model of the maximum completion time minimum and minimum total drag time. Then the event-driven strategy is utilized to realize the dynamic scheduling. By using the multi-objective ant colony algorithm as the optimization methods and improving the transition probability and global pheromone updating, the proposed algorithm can speed up the searching and avoid falling into local optimum. Moreover, the simulation results show that the improved algorithm can achieve better Pareto front. Compared with the Gantt chart of the multi-objective and single target scheduling, the multi-objective optimization can attain better balance among various targets. Finally, the simulations on two dynamic events show that the improved ant colony algorithm can achieve better performance in actual dynamic scheduling.