A novel genetic algorithm for flexible job shop scheduling problems with machine disruptions

Either partial flexible job shop or total flexible job shop were studied and discussed in large amount. However, it is still far from a real-world manufacturing environment, in which disruptions such as machine failure must be taken into account. The goal of this paper is to create a genetic algorithm with very special chromosome encoding to handle flexible job shop scheduling that can adapt to disruption to reflect more closely the real-world manufacturing environment. We hope that by using just-in-time machine assignment and adapting scheduling rules, we can achieve the robustness and flexibility we desire. After detailed algorithm design and description, experiments were carried out. In the experiments, we compared our novel approach to two benchmark algorithms: a right-shifting rescheduler and a prescheduler. A right-shifting rescheduler repairs schedules by delaying affected operations until the disruption is over. A prescheduler works on each disruption scenario separately, treating disruptions like prescheduled downtime. Experiments showed that our approach was able to adapt to disruptions in a manner that minimized lost time than compared benchmark algorithms.