An iterated greedy matheuristic for scheduling in steelmaking-continuous casting process
本文综述了炼钢连铸调度挑战，提出一个混合整数线性规划模型，并开发了迭代贪婪数学启发式算法来最小化加权惩罚和等待时间等目标，实验表明该算法优于两种遗传算法。
Steelmaking-Continuous Casting (SCC) is a bottleneck in the steel production process and its scheduling has become more challenging over time. In this paper, we provide an extensive literature review that highlights challenges in the SCC scheduling and compares existing solution methods. From the literature review, we collect the essential features of an SCC process, such as unrelated parallel machine environments, stage skipping, and maximum waiting time limits in between successive stages. We consider an SCC scheduling problem with as objective the minimisation of the weighted sum of cast break penalties, total waiting time, total earliness, and total tardiness. We formulate the problem as a mixed-integer linear programming model and develop an iterated greedy matheuristic that solves its subproblems to find a near-optimal solution. Through numerical experiments, we show that our algorithm outperforms two types of genetic algorithms when applied to test instances.