A hybrid genetic algorithm for the re-entrant flow-shop scheduling problem

This study considers the production environment of the re-entrant flow-shop (RFS). In a RFS, all jobs have the same routing over the machines of the shop and the same sequence is traversed several times to complete the jobs. The aim of this study is to minimize makespan by using the genetic algorithm (GA) to move from local optimal solution to near optimal solution for RFS scheduling problems. In addition, hybrid genetic algorithms (HGA) are proposed to enhance the performance of pure GA. The HGA is compared to the optimal solutions generated by the integer programming technique, and to the near optimal solutions generated by pure GA and the non-delay schedule generation procedure. Computational experiments are performed to illustrate the effectiveness and efficiency of the proposed HGA algorithm.