Optimisation of complex simulation models
Computer simulation models are widely and frequently used to model real systems to predict output responses under specified input conditions. Choosing optimal simulation parameters leads to improved operation of the model but it is still a challenge as to how to go about optimally selecting these pa...
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| Format: | Dissertation |
| Language: | English |
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University of Cape Town
2014
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| Online Access: | http://hdl.handle.net/11427/6572 |
| Summary: | Computer simulation models are widely and frequently used to model real systems to predict output responses under specified input conditions. Choosing optimal simulation parameters leads to improved operation of the model but it is still a challenge as to how to go about optimally selecting these parameter values. The aim of this thesis was to see if a method could be found to optimise a simulation model provided by a client. This thesis provides a review of the literature of various simulation optimisation techniques that exist. Five of these simulation optimisation techniques - Simulated Annealing, Genetic Algorithms, Nested Partitions, Ordinal Optimisation and the Nelson-Matejcik Method - were selected and applied to a test case stochastic simulation model to gain an understanding into the techniques for their use in optimising the test model. These techniques were then used and applied to optimise a real life simulation model provided by a client. A technique combining the Ordinal Optimisation and Simulated Annealing optimisation methods provided the best results. This technique was provided to the client as a strategy to implement into their simulation model. |
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