Group Arrivals of Emergency Patients: A Resilient Approach to Operating Room Scheduling

Document Type : Original Article

Authors

1 MSc, Department of Industrial Engineering, Faculty of Engineering, University of Yazd, Yazd, Iran. Email: farshadahmadian12@gmail.com

2 Corresponding Author, Prof, Department of Industrial Engineering, Faculty of Engineering, University of Yazd, Yazd, Iran. Email: mfakhrzad@yazd.ac.ir

3 Associate Prof, Department of Industrial Engineering, Faculty of Engineering, University of Yazd, Yazd, Iran. Email: hhn@yazd.ac.ir

4 Prof, Department of Industrial Engineering, Faculty of Engineering, University of Yazd, Yazd, Iran. Email: shishebori@yazd.ac.ir

10.22091/jemsc.2026.15475.1358

Abstract

Addressing the challenge of operating-room management under unpredictable group arrivals of emergency patients, we propose a resilient, block-based scheduling strategy that balances elective throughput with rapid emergency response. The day is partitioned into contiguous time blocks; an initial elective surgery schedule is generated for the full horizon and then dynamically updated at the start of each block as emergency arrivals materialize. Our policy preferentially assigns shorter elective procedures to blocks with a higher estimated probability of emergency arrivals, thereby reducing elective disruption, increasing the likelihood of on-time completion for scheduled cases, and expediting care for emergency patients. To support this operational design, we formulated an initial mixed-integer programming model for baseline planning, and two rescheduling models that are triggered when emergencies occur. For computational tractability on realistic instance sizes, we solved these models using the Invasive Weed Optimization (IWO) metaheuristic. Computational experiments on large, randomly generated benchmarks indicate that IWO consistently produces high-quality solutions within modest computation times, preserving elective performance while significantly improving system resilience to grouped emergency arrivals. The proposed framework is readily adaptable to hospital preferences and uncertainty estimates, making it suitable for practical deployment.

Keywords

Main Subjects

References

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