Potential of data-driven simulation-based optimization for adaptive scheduling and control of dynamic manufacturing systems

The increasing customization of products, which leads to greater variances and smaller lot sizes, requires highly flexible manufacturing systems. These systems are subject to dynamic influences and demand increasing effort for the generation of feasible production schedules and process control. This paper presents an approach for dealing with these challenges. First, production scheduling is executed by coupling an optimization heuristic with a simulation model. Second, real-time system state data, to be provided by forthcoming cyber-physical systems, is fed back, so that the simulation model is continuously updated and the optimization heuristic can either adjust an existing schedule or generate a new one. The potential of the approach was tested by means of a use case embracing a semiconductor manufacturing facility, in which the simulation results were employed to support the selection of better dispatching rules, improving flexible manufacturing systems performance regarding the average production cycle time.