A Decision Support System with Ct_ACO Algorithm for the Hot Rolling Scheduling

This paper presents a hybrid strategy for the hot rolling scheduling problem, which is derived from the actual steel production. Some features such as the rolling length of the consecutive slabs with same width, temperature jump between adjacent slabs make the solution methodology more difficult. Therefore, the hybrid strategy is proposed to determine good approximate solutions for this complicated problem. The hybrid strategy is based on the solution construction mechanism of ant colony optimization (ACO) with cyclic transfers (CT), which is a new class of very large-scale neighborhood search algorithm. We call this approach CT_ACO. Moreover, a decision support system in which the algorithm has been embedded for the hot rolling scheduling is designed. The performance of the system has been tested on problem instances generated randomly and real production data. The computational experiments show that the CT_ACO method has more potential for improvement to solve the hot rolling scheduling problem compared with the manual scheduling method.