Course overview
This course is focused on equipping students with simulation techniques to underpin decision making. Simulation is widely used to model systems, to evaluate risk, and to optimise objective functions, with the goal to inform decisions. Building up from uniform random generation, some of the key simulation techniques used for efficient simulation to support decision-making will be presented. Topics covered are: Uniform random number and random variable generation; random process generation; discrete-event simulation; basic statistical analysis of simulation data; variance reduction techniques; rare-event simulation; randomized optimization; applications in systems modelling, risk analysis and optimisation.
Course learning outcomes
- Communicate how randomness and controlled variation can be used to model complex systems in a range of application domains such as industry, health, and transportation
- Create a model of a real-world problem specified in words and implement it as a discrete-event simulation
- Validate results from a discrete-event simulation
- Explore scenarios using simulation to elicit and compare possibilities
- Systematically simulate to derive quantitative information with measures of confidence
- Design simulation-based workflows to support decision-making in real-world contexts