Optimising stochastic systems using streaming simulation.

使用流模拟优化随机系统。

• 批准号: 2753514
• 金额: --
• 依托单位: Lancaster University
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2753514
• 关键词: Optimising; stochastic; systems; using; streaming

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.

随机理论是一个成熟的数学领域，该领域的方法通常用于对随机系统进行建模。然而，在许多复杂的现实情况下，从这样的模型中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。经典排队论方法中无法轻易捕捉的一个假设是以客户放弃形式出现的客户不耐烦的概念。其中最常见的形式是食言（在接受服务之前离开系统）和畏缩不前（首先没有加入系统）。客户根据他们自己的规则或偏好来决定是拒绝还是违约，这些规则或偏好可能不为系统控制器所知。我们的目标是开发新的顺序决策政策，以适应性地优化这些系统。在这些政策中，我们必须解决参数估计、处理缺失数据以及平衡学习与有效决策的问题。