Optimising stochastic systems using streaming simulation.

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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 solutions 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. We aim to develop novel sequential decision making policies to adaptively optimise these systems. Within these policies we must tackle the problems of parameter estimation, handling missing data and balancing learning with effective decision-making.