Using simulation as an adaptive source of knowledge for the control of material handling systems

Abstract

Recent advances in computing have allowed simulation to be used as a source of data in the real-time control of logistics systems such as Material Handling Systems (MHS). For a real-world MHS, in development by Lödige Industries GmbH, Germany, we demonstrate the benefit of generating data offline using a parametrized simulation model that real-time operational control is based on. The data consist of mappings of control situations to optimal actions respectively. Our approach allows for self-adaptation of the simulation by observing current system parameters that are fed into the model. The control automatically triggers regeneration when necessary, detects changes in the system and also proactively anticipates them, resulting in consistently high performance. We furthermore use a simulation-based look-ahead method to consider uncertainties when evaluating alternative actions. Evaluation results show a significant increase in system performance compared to fixed application of a control action and demonstrate the benefits of the self-adaptive properties.

Keywords:

• MHS
• simulation
• machine learning

Acknowledgements

Note—This paper is a revised and expanded version of a paper entitled Simulationsgestützte, Selbstadaptierende Wissensbasierte Steuerung von logistischen Systemen presented at 15th ASIM Dedicated Conference Simulation in Production and Logistics, Paderborn, Germany, 9–11 October 2013.