Abstract: Steelmaking-Continuous Casting(SCC)is the bottleneck in the iron and steel production.The processing times of the last stage in the SCC production process are adjustable.The SCC scheduling problem with adjustable processing times is a NP-hard combination optimization problem,high-quality SCC scheduling algorithms can enhance the production efficiency greatly.In this paper,based on the problem characteristics,an effective Grey Wolf Optimizer (GWO) is proposed for the SCC scheduling problem with adjustable processing times.A new decoding method is devised to decode the solutions.Then,a population initialization method is presented to obtain an initial population with certain quality and exploration.Meanwhile,a search operator based on multiple operations is designed,where three different operations are contained.The search operator can achieve a balance of exploitation and exploration to some extent.Moreover,a restart operator is devised to enhance the exploration.Comparison experiments have shown the effectiveness of the search operator based on multiple operations.Furthermore,comparison experiments with four efficient scheduling algorithms have demonstrated the high-performance and superiority of the proposed GWO.

Key words: steelmaking-continuous casting scheduling, iron and steel production, scheduling algorithm, adjustable processing times, grey wolf optimizer

摘要： 炼钢-连铸(SCC)是钢铁生产中的瓶颈,SCC生产过程中最后一个阶段的加工时间可调。可调加工时间SCC调度问题是NP难组合优化问题,高质量的SCC调度算法可以较大地提高生产效率。基于问题特征,研制了求解该问题的高效灰狼优化(GWO)算法。首先设计了新的解码方法对解进行解码。同时提出了种群初始化方法,以得到具有一定质量和多样性的初始种群。其次,研制了一种基于多操作的搜索算子,该算子包含3种不同操作,在一定程度上实现了GWO算法的集中性和多样性的平衡。此外,设计了重启操作,以提高GWO算法的多样性。对比实验说明了基于多操作的搜索算子的有效性。此外,与4种有效调度方法的对比说明了GWO算法的高性能和优越性。

关键词: 炼钢-连铸调度, 钢铁生产, 调度算法, 可调加工时间, 灰狼优化算法