Adaptive Capacity Planning for Ambulatory Surgery Centers

Problem Definition: Problem Definition: We develop a framework to plan capacity for ambulatory surgery centers (ASCs) typically consisting of three stages. The problem is to determine the capacity in each stage that efficiently covers possible daily patient demand and to coordinate the three stages of patient visits. The objective is to minimize the total cost incurred in satisfying the daily patient demand, where the total cost is defined as the sum of overtime cost and amortized construction cost for the three stages.Academic/Practical Relevance: Timely capacity adjustment is important for ASC practitioners, but related research is limited. We explicitly consider three sequential stages that are typical in ASCs: the pre-operative at holding room (HR), intra-operative at operating room (OR), and post-operative at post-anesthesia care unit (PACU). Such ASCs can be modeled as a hybrid flow shop (HFS) in the scheduling literature. The interdependence of activities and uncertainties in patient-mix as well as their durations pose a significant challenge to manage the capacity of each activity and achieving a smooth patient flow.Methodology: In contrast to the traditional top-down approach to capacity planning, our approach contributes by proposing a bottom-up strategy based on optimization methods combined with analytics that are informed by operational-level archival patient data. Specifically, we use analytics tools to classify patients into groups, which reduces the complexity of the capacity planning problem and improves the model's practicality. Later, we develop several mathematical formulations and heuristics based on scheduling theory to derive the most cost-efficient capacity solution. Results: Given the trade-off between overtime cost and amortized capacity construction cost, this study relaxes the fixed capacity assumption of traditional HFS problems. Because of the computational complexity of the exact HFS model, we develop a heuristic that is straightforward and easy to implement to find cost-efficient capacities for the three stages. Our computational study examines how uncertain business parameters, e.g., patient-mix, service durations, overnight-stay probabilities, affect the capacity planning decision.Managerial implications: This study highlights the benefit of considering multiple stages together in capacity planning, rather than focusing solely on the operating rooms exclusively. We expect our approach to guide the more than 5,000 ASCs in the U.S., performing 23 million surgeries annually, to make appropriate investments that will improve ASC operations via capacity adjustment and patient scheduling.