Hybrid Intelligent Algorithm for Job-Shop Scheduling under Uncertainty

Production scheduling plays an important role in improving efficiency and reducing cost. One of its core technologies is the establishment of an effective scheduling model and its corresponding optimization algorithms. Nowadays, most researches are concentrated on the optimization algorithms of classical scheduling problems without considering the uncertainties in the real Job-shop. Describing the uncertain information in the real job-shops with several stochastic variables, a stochastic multi-objectives and multi-priorities programming, model for job-shop scheduling is proposed, in which Time. Cost and Equilibrium serve as the three basic objectives for scheduling. The credibility of the delivery time of different types of work pieces serve as the scheduling constraints. In order to obtain the approximate Optimum solution, a hybrid intelligent algorithm which combines Stochastic Simulation, Neural Network with Genetic Algorithm is proposed, and the primary steps are discussed. A case is given to illustrate the feasibility of this model and the method.