We study healthcare scheduling across the patient care pathway, from surgery to post-discharge home care. The first project focuses on operating room scheduling with ward inpatient capacity constraints. Elective surgery decisions induce uncertainty in surgery durations and subsequent ward needs, measured by length of stay (LOS), across inpatient units. We develop a data-driven robust optimization framework that jointly coordinates operating room scheduling and downstream capacity planning under surgery duration and LOS uncertainty, leading to improved service levels and more efficient resource utilization.
A second arm of this project focuses on home-care scheduling, an alternative to inpatient and long-term institutional care that improves patient outcomes while reducing system costs. Patients discharged from hospitals and community clients compete for limited caregiver capacity. Home-care operations must manage time-window constraints, travel efficiency, and caregiver–client continuity in the presence of demand uncertainty and implementation delays. We propose a decomposition-based optimization framework that integrates long-term caregiver–client assignment with short-term scheduling decisions and captures uncertainty propagation over time using a Dynamic Bayesian Network (DBN) and Distributionally Robust Optimization (DRO).
