Application of the Hybrid Genetic Algorithm to Combinatorial Optimization Problems in Flow-shop Scheduling

Production scheduling has been recognized as common but challenging combinatorial problems. Because of their complexity, recent research has turned to genetic algorithms to address such problems. Although genetic algorithms have been proven to facilitate the entire space search, they lack in fine-tuning capability for obtaining the global optimum. Therefore, in this study a hybrid genetic algorithm was developed for optimization. Scheduling problems often involve more than one criterion and therefore require multicriteria analysis. Therefore a multicriteria flowshop scheduling problem with setup times is considered. The objective function of the problem is minimization of the weighted sum of total completion time, makespan, maximum tardiness and maximum earliness. An integer programming model is developed for the problem which belongs to NP-hard class. According to computational results, the hybrid genetic algorithm proposed is effective in finding problem solutions.