Browsing by Author "Prajapat, Neha Venilal"

Now showing 1 - 1 of 1
  • Thumbnail Image
    ItemOpen Access
    Enhancing the decision making capabilities of discrete event simulation using optimisation and visualisation.
    (2017-05) Prajapat, Neha Venilal; Tiwari, Ashutosh
    Technological innovations for data collection and an increasingly competitive global market have led to an increase in the application of Discrete Event Simulation by manufacturing companies in recent years. Scenario analysis and optimisation methods are often applied to these simulation models to improve objectives such as cost, profit and throughput. New visualisation technologies such as Virtual Reality have gained significant interest in a variety of industries and can be linked with simulation to provide significant benefits. The literature review has identified four main research gaps: the development of optimisation-ready simulation models, the application of multi-objective optimisation methods to simulation models, the development of an interactive immersive visualisation framework and the development of a framework for visualising the relationship between inputs and outputs of simulation models. The aim of this research is to develop a framework to simulate, optimise and visualise manufacturing processes to assist stakeholders with decision making. A framework is presented to generate ready-to-optimise simulation models including an interface for input and output variables. Following the development objectivessimultaneously using design of experiments and meta-models to create a Pareto front of solutions. The results show the resource allocation meta-model provides acceptable prediction accuracy whilst the lead time meta-model was not able to provide accurate prediction. A closed-loop immersive interactive interface for simulation is also presented. Regression trees have been proposed to assist stakeholders with understanding the relationships between input and output variables. The framework uses regression and classification trees with overlaid values for multiple objectives and random forests to improve prediction accuracy for new points. The research is validated using a set of real-life test cases.