Research

My project: Optimising stochastic systems using streaming simulation data.

Queueing theory is a well-established field of mathematics and methodologies from this field are often used to model stochastic systems. However, in many complex, real-world situations, deriving closed-form results from such models becomes impractical or even impossible. It is in these situations that we must turn to alternative optimisation methods.

One assumption that cannot be easily captured within classical queueing theory methodologies is the concept of customer impatience in the form of customer abandonment. The most common forms of these are reneging (leaving the system before receiving service) and balking (failing to join the system in the first place). Customers decide whether to balk or renege according to their own rules or preferences that might be unknown to the system controller.

Within the wider literature, stochastic optimisation problems are often solved in an offline setting, with the optimisation being carried out based on a single batch of observations. The field of streaming simulation is a new area that considers the where observations are acquired sequentially. By integrating streaming simulation methods within sequential decision-making policies, we aim to strike an appropriate balance between parameter exploration (estimating unknown system parameters) and exploitation (making effective decisions based on currently-accumulated data).

If you are interested in my research do not hesitate to get in touch.