Title of talk
Stochastic and Adaptive Operating Room Scheduling
The performance of operating room is highly impacted by stochasticity, e.g. regarding arrival of urgent patients, cancellation of others, changes in duration, et cetera. There is considerable evidence that the capabilities of planners to cope with these events and adapt the operating room schedules during execution significantly determines performance, e.g. in terms of process measures such as waiting time, idle time, overtime, and in outcome measures such as employee satisfaction and patient health. In this presentation we present two methods to provide optimal answers to the corresponding adaptive scheduling problems.
In a first research (Xiao, et al. 2017) we explore the potential of previously adaptive scheduling frameworks to operating room scheduling, presenting theoretical results on adaptive scheduling and the stochastic knapsack problem, and a computational analysis. In adaptive scheduling, the schedule can in principle be reoptimized whenever a surgical case has finished. In practice, such optimization is often restricted to once per day (or specific moments in time).
In a second study (Xiao et al. 2016), we present a 3-stage stochastic programming approach (using sample average approximation) to minimize a weighted sum of reducing overtime and cancellation of surgeries by the planner. We present theoretical results, as well as a case study from a large overcrowded hospital in Shanghai.