A hybrid genetic algorithm for a loading problem in flexible manufacturing systems

One of the operating decisions involved in the scheduling of flexible manufacturing systems (FMS) is that of loading the FMS. Given a pool of jobs, which can be processed on alternate machines and alternate tools, the scheduler has to decide on the allocation of tools and machines to the different jobs. Various versions of this problem have appeared in the literature. We consider the version where jobs are selected for processing in a FMS in a planning horizon, operations for these jobs are assigned to machines, and corresponding tools are allocated to the slots in the machines. The objective is to minimise system unbalance. A hybrid genetic algorithm is presented that addresses this problem. Computational comparison between the genetic algorithm and previous algorithms is presented