A System-Level Decision-Support Framework for Integrated Operating Room and Bed Capacity Planning Under Emergency Uncertainty

Abstract

Coordinating operating room schedules with downstream inpatient bed availability remains a critical challenge for hospitals, particularly under emergency-driven uncertainty. Emergency arrivals introduce variability that propagates congestion across surgical and inpatient systems, reducing elective surgery throughput and resource utilization. Existing approaches often treat operating rooms and inpatient beds as isolated planning problems, limiting the ability to anticipate system-wide congestion effects. This study proposes a system-level decision-support framework that integrates elective operating room scheduling, emergency arrivals, and inpatient bed capacity within a unified stochastic optimization model. Uncertainty in surgical duration and patient length of stay is represented through scenario-based stochastic modeling. Computational experiments examine system performance under varying levels of emergency demand and bed availability. The results identify critical congestion thresholds beyond which elective throughput deteriorates rapidly, highlighting the role of downstream bed constraints in governing system capacity under uncertainty. The proposed framework provides hospital managers with practical insights for coordinated surgical and inpatient capacity planning, bridging operations research optimization with operations management principles at the system level.