| With the integration of steel making-continuous casting-hot rolling, slab-yard is an important process in steel industry. As the intermediate buffer between casting and hot rolling, it plays a vital role in coordination with rolling capacity, steel-making capacity, and the balance of entire production process. The slab storage planning has been studied in this paper, taking the production practice of a hot mill as background, where aims at the continuous production and the production efficiency.To the optimization problem of slab-yard, a class of slab storage optimization model is established considering the selection of slab location and stack comprehensively. It consists of two sub-models, which are respectively the slab storage optimization and the fuzzy slab transport optimization. And, the slab storage planning is accomplished by the layered collaborations between two sub-models. An adaptive quantum genetic algorithm (AQGA) is proposed to solve the first sub-model, where the quantum door revolving gate angle is adaptively adjusted depending on the state of population's concentration-dispersion and Iterations, which improves the computing speed of the algorithm. To the uncertainty of transiting time of each slab, a triangular fuzzy numbers are employed to address the uncertainty, and form the second sub-model together with fuzzy due dates. The optimal location and stack of each slab are given in the second sub-model finally. The simulation with real production data shows that the model and algorithm proposed in this paper are feasible.Based on the proposed models and algorithms, an application software system for the slab yard management is developed. The system can simulate the actual production conditions of slab-yard and reduce the operational costs compared to the manual experience based operation. The running results of the system demonstrate that the approach has an excellent application effectiveness, which improves the production efficiency of steel enterprise.
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