The new algorithms for the dynamic flowshop scheduling

Abstract

Conventional scheduling theory and algorithms have not solved the practical needs of factory management. Because of the complexity of modern manufacturing systems and the fact that they operate in a stochastic and dynamic environment, dynamic scheduling is often required. Dynamic flowshop scheduling is frequently met in practical situations. In this paper we apply an adaptive optimization framework and develop a dynamic flowshop scheduling model with the objective of minimizing the mean of flowtime of jobs arriving as a Poisson process. Static heuristic algorithms are embedded in control model as scheduling controller. The most commonly used dispatching rule method is also implemented and compared with the new model through simulation. Analysis is done for different shop load levels and different numbers of machines. The results show that the new model performs significantly better than the dispatching rule method.