Integrating analytical hierarchy process to genetic algorithm for re-entrant flow shop scheduling problem

This research considers a hybrid flow shop scheduling problem with dynamic re-entrant characteristics substantiated by the complexity of the problem in a repairing company. Multiple types of jobs are involved in the problem with individual buffer times that are strongly related to the previous processing job. These jobs need to go through tandem workstations, while some jobs may re-enter the processing line more than once. In order to reduce complexity, jobs are considered as basic units for scheduling. A novel combination of the analytical hierarchy process (AHP) and genetic algorithm (GA) is proposed to deal with the dynamic re-entrant scheduling problem which takes many criteria into consideration. GA is applied to obtain near-optimal schedules, while AHP works with a twofold effect. One is to fulfil the multiple criteria, while the other is adopted in the selection process of GA to fasten GA's convergence speed. The proposed model and solution algorithm are applied to solve the problem in a repairing company under a set of actual constraints. Comprehensive studies are conducted with real-life data. The results are consistent with the company operational scenario and are better than those of the manual schedules