Simulation optimization for decision support in operating a robotic manufacturing system

Abstract

The operation of a robotic manufacturing system can be a complex task for which little experience is now available. Simulation has often been used as a means of modeling large complex systems. Optimization methods use such models to make good choices for system parameters. This paper describes a simulation-optimization approach combined with pattern recognition to develop an operating procedure for a manufacturing system which contains robots. This procedure is adaptive in the sense that it is updated on a periodic basis to account for changing shop load and pending orders.