PhD Overview

Stochastic discrete-event simulation is a widely used tool for improving the design and performance of complex systems operating under uncertainty. A simulation model aims to imitate the fundamental processes and interactions which characterize the real-world system, and can thus be used as a surrogate to conveniently and cost-effectively explore potential improvements and what-ifs. Traditionally, evaluation of simulation performance has focused on high level summary statistics such as average daily costs or average customer delays, which naturally overlooks the finer detail of how a system behaves. Meanwhile, the success of modern analytics and machine learning in today's data-rich world leads us to confidently raise the bar on what we can hope to achieve with our understanding of simulation behaviour. This provides the motivation for my PhD; we explore ways to gain deeper insights into the behaviour of complex systems by exploiting the data generated by stochastic simulation. I am supervised by Dr. Lucy Morgan and Dr. Nicos Pavlidis at Lancaster University, and Prof. Barry Nelson at Northwestern University, Chicago.

Simulation Software

  • During the course of my research, I have used simulation software provided by Simul8.

Publications

Presentations

Posters