Task scheduling for flexible manufacturing systems based on genetic algorithms

The authors present a genetic algorithm approach to solving the task scheduling problem in flexible manufacturing systems (FMSs) An FMS is modeled as a collection of m workstations and p automated guided vehicles (AGVs). The FMS completes a task by performing a series of operations through the workstations, and the parts are transported between the workstations by the AGVs. The problem of task scheduling in an FMS can be stated as finding a schedule for the p AGVs among the m workstations such that n tasks can be completed in the shortest time. The genetic algorithm developed uses a reproduction operator and five mutation operators to perform the task scheduling. Computer simulations of the proposed genetic algorithm are also presented