Simulation Based Parameter and Structure Optimisation of Discrete Event Systems

Abstract

Modelling and simulation based on discrete event systems is used routinely in research and industrial applications e.g. in the design, planning and real time control of manufacturing systems. An advanced, but now well established, technique is modelling and simulation with integrated parameter optimisation to improve system performance. In using these established approaches model structure is considered to be fixed as the relationships between model elements are defined during model development. As model performance is optimised it may be necessary to redesign the model structure, normally carried out manually by an analyst using previous simulation results, observations or decisions based on previous experience. With increasingly complex, flexible and reconfigurable discrete event systems such as manufacturing systems, modelling and simulation methods are becoming more challenging. As the number of possible structure variants increases the potential benefit of automatic model structure optimisation becomes significant. The research reported in this thesis details a new approach providing automatic reconfiguration and optimisation of both model structure and model parameters. This is achieved through a combination of simulation, optimisation and model management methods. Simulation is used to determine current model performance and an optimisation method, assisted by model management, searches for an optimal solution with repeated model parameter and model structure changes. In contrast to conventional modelling and simulation methods this approach employs a meta- modelling method. It defines a set of model structure variants and includes a model base with pre-defined basic components. With this meta-modelling method the model management can determine specific model structures and create executable models. To validate the simulation based optimisation approach a prototype was implemented. Several variants of a Photofinishing Laboratory part were modelled. In different experiments the introduced approach and the prototype were validated. This research project extends the work of Pawletta et al. [35]...[46], supports other projects of the Research Group Computational Engineering and Automation at Hochschule Wismar University of Applied Sciences Technology, Business and Design, Germany and follows another collaborative LJMU School of Engineering / Wismar research project in this field [23] [24].