Discrete-Review Control of Service Processing Networks with Applications to Patient Flow Management in Emergency Departments

随机排队网络的控制通常用于服务系统(例如，医院、联系中心和骑行/自行车共享系统)的建模和分析，在过去一直是广泛研究的主题。文献主要涉及连续时间控制，由此控制器可以在任何时间点与系统交互。然而，在许多服务设置中，系统管理员只能以离散且可能较长的时间间隔与系统交互。也就是说，尽管相关信息正在变得可用，但在下一次审查时间开始之前，管制员无法进行控制。 对离散时间或离散审查策略的控制限制往往从根本上改变了最优控制策略的结构，使得相应的连续时间策略(如果知道)不切实际，从中获得的见解也很难解释。然而，尽管有应用和潜在的好处，排队网络的离散回顾控制在文献中大多还没有被探索过。 提出的研究方案旨在首先提供关于施加离散审查限制对动态控制策略性能的影响的基本见解，其次，开发实用而有效的离散审查策略来控制复杂排队网络。特别是，我将考虑两个重要的运营决策，即动态容量分配和容量规划，并研究主要受急诊科患者流管理应用程序驱动的排队模型。通过与两家大型医疗保健提供商(密西索加的氚健康合作伙伴(THP)和纽约州纽约的纽约长老会(NYP)医院)合作并使用他们的数据，该计划将涉及校准模型、评估拟议政策的性能并努力在实践中实施。 这一研究计划的一个重要特点是，它强调实际政策，并依赖于由真实世界数据提供信息的模型。因此，如果成功，这项研究的结果可以帮助缩短加拿大各地急诊室的等待时间，因为在加拿大，过度拥挤越来越令人担忧。此外，尽管建议的模型主要是由患者流管理应用程序驱动的，但它们捕捉到了各种服务系统中存在的动态和特征。因此，预计本程序中开发的方法和见解将有用，并导致对其他服务系统的进一步研究，在这些系统中，由于人为影响或与系统交互的高成本，连续时间控制是不可行的。

Control of stochastic queueing networks which are often used in modeling and analysis of service systems (e.g., hospitals, contact centers, and ride/bike sharing systems) has been the subject of extensive research in the past. The literature is predominantly concerned with continuous-time control, whereby the controller can interact with the system at any point in time. In many service settings, however, the system manager can only interact with the system at discrete and possibly long time-intervals. That is, although relevant information is becoming available, the controller cannot exercise control before the beginning of the next review time. The limitation of control to discrete-time or discrete-review policies often fundamentally changes the structure of the optimal control policy, making the corresponding continuous-time policy (if known) impractical and the insights from it hard to interpret. Despite applications and potential benefits, however, discrete-review control of queueing networks has remained mostly unexplored in the literature. The proposed research program aims to first provide fundamental insights on the impact of imposing the discrete-review limitation on the performance of dynamic control policies, and second, to develop practical and effective discrete-review policies for control of complex queueing networks. In particular, I will consider two important operational decisions, namely dynamic capacity allocation and capacity planning, and study queueing models that are mainly motivated by applications to patient flow management in Emergency Departments (EDs). Through collaboration with two large healthcare providers (Tritium Health Partners (THP) in Mississauga, ON, and New York Presbyterian (NYP) hospital in New York, NY) and using their data, the program will involve calibrating the models, estimating the performance of the proposed polices, and working towards their implementation in practice. An important characteristic of this research program is that it emphasises practical policies and relies on models that are informed by real-world data. Therefore, if successful, the outcome of the research can contribute to reduction of waiting times in EDs across Canada, where overcrowding is of growing concern. Further, although the proposed models are mainly motivated by applications to patient flow management, they capture dynamics and features that are present in various service systems. Therefore, it is expected that the methods and insights developed in this program will be useful and lead to further research for other service systems where continuous-time control is not feasible, due to either human-effects or high cost of interacting with the system.