Research On ACO For Dynamic Job Shop Scheduling

With rapid development of information technology and economical globalization, Scheduling is always the key part in manufacturing and becoming more important in recent years. The actual environment that will influence the effect of the scheduling is filled with dynamic events such as the arrival of the new orders, the cancellation of the original orders and the malfunction of machines. Consequently, the dynamic job shop scheduling is more needed.This paper presents a methodology adopting the new structure of MAS(multiagent system) equipped with ACO(ant colony optimization) algorithm for a better schedule in dynamic job shop. The methodology is tested on a simulated job shop to determine the impact with the new structure. And the efficiency and stability in the proposed method are compared with those in other methods.