Research on flexible job-shop scheduling problem under uncertainty based on genetic algorithm

In this paper, an improved genetic algorithm for optimization of flexible job-shop scheduling problem with fuzzy processing time and fuzzy due date is presented, which is used to research the complexities and essences of this problem. Firstly, the optimization model under uncertainty environment is built, and the objectives are to maximize the average agreement index, and minimize the maximum of fuzzy completion time and the workload of machine. Then the paper discusses some kinds of different situation and definition of fuzzy processing time and due date, gives their graphic description as well. After that, an improved genetic algorithm is presented to optimize the flexible job-shop scheduling problem under uncertainty. The feasibility of the optimization model and the improved genetic algorithm are validated through some instances.