An online optimization scheme of the dynamic flexible job shop scheduling problem for intelligent manufacturing

Abstract

Flexible production lines are the mainstream choice in the current manufacturing industry. In a flexible line, a single machine can execute a variety of processing tasks. Machine breakdowns are common unexpected disturbances in manufacturing. Machine breakdowns in a flexible production line that result in the unexpected shutdown of the flexible production line's operation unit will have a significant influence on the overall manufacturing process. A two-stage dynamic scheduling strategy is used in this paper to solve the problem that the repair time of the machine cannot be accurately estimated when the machine fails in the production process of the flexible production lines. When any machine fails or is repaired, this strategy is used to set up the best production schedule for flexible production lines. The two-stage scheduling strategy can avoid estimating the repair time of the machine so that dynamic scheduling can be carried out accurately according to the actual situation. The imperialist competitive algorithm(ICA) is originally suitable for continuous optimization problems, while this problem falls within the category of discrete optimization. In this paper, the idea of hybridization of genetic algorithm is used to improve the ICA, so that it is suitable for discrete optimization problems to solve dynamic scheduling. Experiments demonstrate the effectiveness of the two-stage dynamic scheduling strategy and the improved imperialist competitive algorithm.