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

点击翻译按钮获取中文摘要

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.)