Increased Surgical Capacity without Additional Resources: Generalized Operating Room Planning and Scheduling

Abstract

We study a generalized operating room planning and scheduling (GORPS) problem at the Toronto General Hospital (TGH) in Ontario, Canada GORPS allocates elective patients and resources (i e , operating rooms, surgeons, anesthetists) to days, assigns resources to patients, and sequences patients in each day We consider patients’ due-date, resource eligibility, heterogeneous performances of resources, downstream unit requirements, and lag times between resources The goal is to create a weekly surgery schedule that minimizes fixed- and over-time costs We model GORPS using mixed-integer and constraint programming models To efficiently and effectively solve these models, we develop new‘ multi-featured logic-based Benders decomposition approaches Using data from TGH, we demonstrate that our best algorithm solves GORPS with an average optimality gap of 2 71% which allows us to provide our practical recommendations First, we can increase daily OR utilization to reach 80%—25% higher than the status quo in TGH Second, we do not require to optimize for the daily selection of anesthetists—this finding allows for the development of effective dominance rules that significantly mitigate intractability Third, solving GORPS without downstream capacities (like many papers in literature) makes GORPS easier to solve, but such OR schedules are only feasible in 24% of instances Finally, with existing ORs’ safety capacities, TGH can manage 40% increase in its surgical volumes We provide recommendations on how TGH must adjust its downstream capacities for varying levels of surgical volume increases (e g , current urgent need for more capacity due to the current Covid-19 pandemic) © 2021 Production and Operations Management Society