Stochastic scheduling and capacity planning models and methods for operations and service management, with an application to healthcare industry

用于运营和服务管理的随机调度和容量规划模型和方法，应用于医疗保健行业

• 批准号: 402380-2011
• 负责人: Begen, Mehmet
• 金额: $ 1.75万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2013
• 资助国家: 加拿大
• 起止时间: 2013-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=529549
• 关键词: Stochastic; scheduling; capacity; planning; models

The goal of this proposal is to develop a system-wide mathematical framework for improving scheduling and capacity planning decisions under uncertainty in operations and service management and apply it in the healthcare industry to achieve better service to patients (less wait times and/or achieve wait time targets) and less cost to service providers (less overtime and/or less capacity requirements). Healthcare challenges are growing in the world and timely access to healthcare is especially concerning in Canada. One way for improvement is with effective scheduling and capacity planning. This becomes more important and challenging when there is uncertainty in the system. Uncertainty is involved with patients (types, volumes), resources (availability of beds, surgery durations) and operations (length of stay in a hospital/recovery unit). I consider a system where jobs (patients) are arriving at each period and waiting in queues until they are processed in a processor (e.g., operating room (OR)) and then kept in a limited capacity storage area (e.g., recovery unit) for some time before they leave the system. There is uncertainty involved with the arrival process of jobs, processing durations and time spent in the storage area. (A subset of the same or similar type system applies to other settings in operations and service management.) Effective scheduling and capacity planning of such a system calls for a system-wide approach. However, mathematical challenges have forced researchers (including myself) to work on smaller problems in isolation for this system. I will build on my previous research in appointment and OR block scheduling to develop a system-wide mathematical framework for the system considered. I will use dynamic programming, non-smooth convex optimization methods, algorithms based on discrete convexity, simulation and process mapping. The potential benefits of this research are reduced patient wait times, improved capacity planning under uncertainty, and advancement of combined scheduling and capacity planning methodology. I am also eager to put our research in practice by working with collaborators in the healthcare sector. (I have working relationships with three BC and two ON health organizations.)

这项建议的目标是开发一个全系统范围的数学框架，用于在运营和服务管理的不确定性下改进调度和能力规划决策，并将其应用于医疗行业，以实现对患者的更好服务(更少的等待时间和/或实现等待时间目标)，以及对服务提供者的更低成本(更少的加班和/或更少的能力要求)。世界各地的医疗保健挑战日益严峻，在加拿大，及时获得医疗保健尤其令人担忧。改进的一种方法是有效的调度和能力规划。当系统中存在不确定性时，这就变得更加重要和具有挑战性。不确定性涉及患者(类型、数量)、资源(床位可获得性、手术持续时间)和手术(在医院/康复单元的住院时间)。我考虑这样一个系统，其中作业(患者)在每个周期到达并排队等待，直到它们在处理器(例如，手术室(OR))中被处理，然后在离开系统之前被保存在有限容量存储区(例如，恢复单元)中一段时间。作业的到达过程、处理持续时间和在存储区域花费的时间都存在不确定性。(相同或类似类型系统的子集适用于运营和服务管理中的其他设置。)这样一个系统的有效调度和能力规划需要一个全系统的办法。然而，数学挑战迫使研究人员(包括我自己)在这个系统中孤立地研究更小的问题。我将在我以前对预约和或分组排班的研究的基础上，为所考虑的系统开发一个全系统的数学框架。我将使用动态规划、非光滑凸优化方法、基于离散凸性的算法、仿真和进程映射。这项研究的潜在好处是减少了患者等待时间，改善了不确定情况下的容量规划，并促进了联合调度和容量规划方法的进步。我也渴望通过与医疗保健部门的合作者合作，将我们的研究付诸实践。(我与三个卑诗省和两个卫生组织有工作关系。)