Robust combined operating room planning and personnel scheduling under uncertainty

Abstract

Providing timely access to costly surgical services in a manner that balances needs of multiple stakeholders (patients, staff, administrators) is made even more challenging by inherent uncertainty. Decisions about clinician scheduling, shift preferences, operating room planning, and patient assignment also often are decentralized or made separately. We develop a robust optimization model that combines staffing and scheduling decisions to minimize the impact of foreseeable variation in surgery durations, staff availability, and urgent or emergency arrivals. Model performance is tested with data from a major academic medical center, resulting in improved service level (% patients served), overtime, utilization, and shift preferences. Although robustness to staffing, duration, and urgent or emergency uncertainty increases operating costs by 6% on average, overtime is reduced by 68% while utilization decreases by only 6%. The number of necessary schedule adjustments on the day of surgery also is reduced by 13% on average in the robust model compared to the nominal model.