A Two-Stage Operating Room Allocation Framework for Reducing Surgical Waiting Lists in Public Hospitals

Abstract

Operating-room allocation is a major challenge for public hospitals with limited surgical capacity and large elective waiting lists. This work proposes a two-stage operating-room allocation framework for specialty-block scheduling environments. The methodology combines a mixed-integer linear programming model for medical specialty block allocation with a priority-based patient allocation procedure. The framework was evaluated through one-week and multi-week simulation scenarios using parameters estimated from historical surgical and waiting-list data. The proposed methodology was compared against an integrated mixed-integer linear programming baseline adapted from the literature under realistic operational disruptions, including failed patient confirmations and surgery suspensions. Results show that the proposed framework achieves a more balanced distribution between offered and demanded surgical time across specialties while maintaining high operating-room utilization and substantially lower computational times than the integrated baseline. In the 20-week scenario, the proposed methodology achieved lower waiting times and operated a larger number of patients while preserving operational flexibility. These results suggest that decomposition-based operating-room allocation strategies provide a practical and computationally efficient alternative for reducing surgical waiting lists in public hospitals.