A decision support flexible scheduling system for continuous galvanization lines using genetic algorithm

An important, complex problem for logistic optimization in the steel manufacturing plants is obtaining a flexible and adaptive scheduling system for a continuous galvanization line (CGL). The problem tackled in this work involves several constraints and characteristics inspired by real-life manufacturing goals. Given the complexity of the problem, which belongs to the class of NP-hard problems, a genetic algorithm (GA) methodology was developed, combining a penalty procedure defined for constraints with assigned weights for different characteristics of coils. By enlisting the ability and flexibility of GAs, a set of parameters are analyzed to achieve the best results for practical applications. This scheduling solution predicts a CGL sequences with a minimum number of coil transitions, to improve productivity and reduce costs.