Multi Objective Job-shop Dynamic Scheduling Based On Improved Ant Colony Algorithm

Job-shop scheduling methods and optimization techniques are crucial elements for advanced manufacturing. With the influence of scheduling methods and optimization techniques for manufacturing production benefit, cost of production, the production speed is outstanding day by day, more and more researchers begin to pay attention to Job-shop which can bring revolutionary power on the manufacturing industry.This paper will deeply research on the characteristics of multi objective Job-shop, built to minimize the maximum completion time and the total drag double objective scheduling model of minimal time, research combined with the improved ant colony algorithm for multi objective dynamic scheduling.Firstly, this paper introduces the current research situation of the job shop scheduling problem, the generation and development of the ant colony algorithm. Then it analyses the mathematical model, disjunctive graph model and the Gantt chart model of the job shop. And establish the dynamic scheduling strategy based the dynamic events. After that it introduces the basic ant colony algorithm and improved the search method.Finally, it will simulate the improved ant colony algorithm and multi-objective genetic algorithm based on the test function, the improved ant colony algorithm has better effect. Then verify the dynamic events in actual scheduling and give the Gantt chart from simulation. The algorithm proposed in this paper and the strategy can go to good effect.