Application of genetic algorithms to strip hot rolling scheduling

Abstract

This paper presents an application of a genetic algorithm (GA) to the scheduling of hot rolling mills. The objective function used is based on earlier developments on flow stress modeling of steels. A hybrid two-phase procedure was applied in order to calculate the optimal pass reductions, in terms of minimum total rolling time. In the first phase, a non-linear optimization function was applied to evaluate the computational cost to the problem solution. For the second phase, a GA was applied. A comparison with two-point and simulated binary (SBX) crossover operators was established. The results were validated with data of industrial schedules. A GA with SBX crossover operator is shown to be an efficient method to calculate the multi-pass schedules at reduced processing time. © 2007 Springer-Verlag Berlin Heidelberg.