Hybrid genetic algorithms for flowshop scheduling with synchronous material movement

This paper considers a flowshop scheduling problem with synchronous material movement in an automated machining center. This automated machining center consists of a loading/unloading (L/U) station, m processing machines, and a rotary table. The L/U station and the processing machines surround the rotary table which transports jobs between machines. The table rotates to simultaneously move jobs when all machines finish with their jobs, including the loading and unloading operations at the L/U station. The cycle time of each rotation is determined by the longest operation on machines. Finding an optimal sequence which minimizes its makespan in this type of machining centers has been shown to be strongly NP-hard. A genetic algorithm combined with a local search is proposed to solve the problem in a large scale. For a given sequence, the local search is applied to the cycle with the largest deviation of completion times. A numerical result shows that the proposed algorithm finds a better solution against other algorithms such as Tabu search, a particle swarm optimization algorithm, and genetic algorithms.