A multi-stage dynamic soft scheduling algorithm for the uncertain steelmaking-continuous casting scheduling problem

The steelmaking-continuous casting (SCC) manufacturing system is usually regarded as a cornerstone as well as a bottleneck in a modern integrated steel company. In this study, we consider an uncertain scheduling problem that arises from the SCC manufacturing system where the processing times and arrival times are in intervals. To solve this problem, we propose a multi-stage dynamic soft scheduling (MDSS) algorithm based on an improved differential evolution. In the proposed algorithm, the uncertain SCC scheduling problem is decomposed into global and local scheduling problems. The global scheduling problem comprising cast units is solved by a dynamic multi-objective differential evolutionary algorithm based on decomposition where each solution is evaluated in the worst-case scenario. The local scheduling problem comprising charge units is solved by the knowledge-based differential evolutionary algorithm where all the solutions are sorted by the interval TOPSIS method. A modified critical ratio-based rule is also developed for real-time dispatching. Finally, computational results demonstrate that the MDSS algorithm outperforms previously described algorithms.