Optimising stochastic systems using streaming simulation.

排队论是一个成熟的数学领域，该领域的方法经常用于模拟随机系统。然而，在许多复杂的、现实世界的情况下，从这样的模型推导出封闭形式的解决方案变得不切实际，甚至不可能。正是在这些情况下，我们必须转向替代优化方法。在经典的排队理论方法论中，一个不容易被捕获的假设是客户放弃形式的客户不耐烦的概念。最常见的形式是食言（在接受服务之前离开系统）和犹豫（一开始就没有加入系统）。客户根据自己的规则或偏好来决定是否拒绝，而系统控制器可能不知道这些规则或偏好。我们的目标是开发新的顺序决策政策，以自适应优化这些系统。在这些策略中，我们必须解决参数估计、处理缺失数据和平衡学习与有效决策的问题。

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.