Solving the Reentrant Permutation Flow-Shop Scheduling Problem with a Hybrid Genetic Algorithm

Most production scheduling-related research assumes that a job visits certain machines once at most, but this is often untrue in practical situations. A reentrant permutation flow-shop (RPFS) describes situations in which a job must be processed on machines in M1, M2, …, Mm, M1, M2, …, Mm, …, and M1, M2, …, Mm order and no job is allowed to pass a previous job. This study minimizes makespan by using the genetic algorithm to move from local optimal solutions to near-optimal solutions for RPFS scheduling problems. In addition, the hybrid genetic algorithm (HGA) improves the genetic algorithm’s performance in solving RPFS