摘要：Flow shop scheduling problems much studied by several researchers. One problem with scheduling is the tardiness. Total tardiness is the performance to minimize tardiness jobs. it is the right performance if there is a due date. This study proposes the Cross-Entropy Genetic Algorithm (CEGA) method to minimize the mean tardiness in the flow shop problem. In some literature, the CEGA algorithm is used in the case of minimizing the makespan. However, CEGA not used in the case of minimizing total tardiness. CEGA algorithm is a combination of the Cross-Entropy Algorithm which has a function to provide optimal sampling distribution and Genetic Algorithms that have functions to get new solutions. In some numeric experiments, the proposed algorithm provides better performance than some algorithms. For computing time, it is affected by the number of iterations. The higher the iteration, computing requires high time.
关键词：Scheduling; total tardiness; flow shop; cross entropy; genetic algorithm
