Modelling and a genetic algorithm solution for the slab stack shuffling problem when implementing steel rolling schedules

Iron and steel production is a multistage process. A slab yard serves as a storage buffer between the continuous casting stage and the steel rolling mill. Slabs from continuous casting are stacked in the yard to await rolling. When implementing the rolling production schedule, slabs need to be picked up from the slab yard one by one according to the scheduled rolling sequence, heated in the heating furnace and then rolled. There are usually a number of suitable candidate slabs in the yard satisfying the requirement for each rolling item. In general, these candidate slabs are not at the most convenient positions in the stacks. To pick up a required slab, the slabs above it need to be shuffled. To save slab-handling cost and prevent the handling cranes from becoming a bottleneck, slabs for a rolling schedule must be selected from among the candidate slabs so that the total number of slabs shuffled is minimized. The paper studies the slab stack-shuffling problem using the hot strip rolling mill in Shanghai Baoshan Iron and Steel Complex as an application background. We first formulate the problem using an integer-programming model and then develop a modified genetic algorithm to solve it. Genetic codes and genetic operators are specially designed to ensure the feasibility of solutions and to speed up the solution process. The algorithm is implemented and compared with the existing algorithm that the mill has been using. Experiments on randomly generated problems show that the proposed new method reduces the number of slab shuffles by 11.19% on average.