Hybrid genetic algorithm for bi-objective flow shop scheduling problems with re-entrant jobs

This paper presents a simulated genetic algorithm model of scheduling the flow shop problems with re-entrant jobs. The objectives of this research are to minimize the weighted tardiness and makespan. The proposed model considers that the jobs with non-identical due dates are processed on the machines with the same order. Furthermore, the re-entrant jobs are stochastic as only some jobs are required to reenter to the flow shop. The tardiness weight is adjusted once the jobs re-enter the shop. The performance of the proposed GA model is verified by a number of numerical experiments where the data come from the case company. The results show the proposed method has a higher order satisfaction rate than the industrial practices.