Abstract
Recently, optimization technologies are commonly applied in logistics scheduling owing to significantly advanced computing technologies. In this paper, a new application for scheduling of crane handling in a slab yard is presented for efficient logistics in steel works. The proposed method consists of two phases: scheduling optimization and logistics simulation. The first phase in this approach utilizes a genetic algorithm that is employed to solve a relaxed scheduling problem of rearranging steel slabs in an approximate manner. Next the partial solution is iterated upon by a rule-based algorithm to obtain a feasible solution. Computational experiments are conducted with operation data of JFE Steel, allowing a comparison to be made between actual and theoretical crane handling operations. The resulted data show that this paper's proposal can reduce the number of handlings by 30%. The effective transportation of slabs contributes to achieving the delivery time and then to the increase of the customer satisfaction.
