A genetic algorithm for solving dynamic scheduling problems in distributed manufacturing systems

It have been proven that the distributed manufacturing system, if managed properly, can enable enterprises to reduce manufacturing costs, increase products quality and make better use of manufacturing resources. However, the dynamic scheduling in distributed manufacturing environments can be much more complex than that in the single integrated enterprise cases. In this paper, a distributed scheduling method is developed, which is composed of an iterative coordination mechanism and a modified genetic algorithm. The complicated scheduling problem is divided into several sub-problems to make the problem easier. The scheduling objective is to achieve a multiple performance index, i.e. minimizing the. manufacturing cost and meeting the duedate. The capability of the proposed method has been tested with satisfactory results through several numerical experiments.