Automatic Design of Intercell Scheduling Heuristics.

Though intercell scheduling problems have been studied in the literature, extant algorithms can hardly come into play in practice. This is because of two reasons: 1) transportation among cells, which is important for practical intercell scheduling, has not been adequately considered and 2) the problem size is large in practice, which may lead to intolerable computation efficiency. The motivation of this paper is to automatically design intercell scheduling heuristics that are suitable for practical application. A genetic programming algorithm with a pretraining strategy (GP-PS) is proposed. Production within cells and transportation among cells are simultaneously considered, and a cooperative coevolutionary framework is designed. To evolve better heuristics, a PS is developed. A speedup strategy is designed to accelerate the evolutionary process. Comparative experiments are conducted with other GP-based algorithms, speedup strategies, and with some state-of-the-art intercell scheduling algorithms. GP-PS is also put into use in a large manufacturing enterprise of China. Computational experiments and application results both verify the effectiveness of GP-PS. Note to Practitioners—Although intercell scheduling problems have been studied in the literature, it is still common to rely on manually generated schedules in industrial environments. This paper does not attempt to seek solutions directly, but try to automatically design good heuristic rules to address the problem, so that the computation efficiency that is required by real-time decision and the optimization performance that is important for a complex problem, can be satisfied at the same time. [ABSTRACT FROM AUTHOR]