A hyper-heuristic for distributed parallel machine scheduling with machine-dependent processing and sequence-dependent setup times

Today, because the market is scattered around world, manufacturing activities are not limited to a single location and have spread globally. As result, discussion of scheduling factory has changed from classic network as need in real world. In this regard, study considers multiple factories by taking into account job transportation time between factories. The main problem here that each would be assigned which machine. research, unrelated parallel machines considered processing jobs depends on machine setup time. To minimize makespan, first, mixed-integer linear model was proposed two types modeling been combined. Then, hyper-heuristic algorithm (HHA) designed solve reasonable choosing best method among four low-level heuristic methods precisely according properties problem. Finally, efficiency compared with imperialist competitive (ICA) conducting experiments. results show performs very well ICA and, more than 75% test problems, superior. Also, based analysis, comparing ICA, it can concluded there significant difference results, all cases, HHA remarkably better. Considering challenges rapid changes today’s traditional centralized production planning does enough flexibility respond them, research expected useful attractive for planners field.