Simulation aided, self-adapting knowledge based control of material handling systems

Abstract

Knowledge based methods have recently been applied to the control of material handling systems, specifically using simulation as a source of knowledge. Little research has been done however on ensuring a consistently high quality of the data generated by the simulation, especially under changing circumstances such as differing load patterns in the system. We propose a self-adapting control that is able to automatically generate knowledge according to current circumstances using a parametrized simulation model, which uses observed system parameters as input. The control automatically triggers generation when necessary, detects changes in the system and also proactively anticipates them, resulting in consistently high performance. For the problem of knowledge generation (determining an optimal control action to a given situation), we present a look ahead simulation method that considers uncertainties. We validated our approach in a real world material handling system, developed by Lödige Industries GmbH.