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3188. Solving three-stage operating room scheduling problems with uncertain surgery durations

Invited abstract in session WD-15: Surgery Scheduling and Operating Room Planning (2), stream OR in Health Services (ORAHS).

Wednesday, 14:30-16:00
Room: 18 (building: 116)

Authors (first author is the speaker)

1. Yang Kuei Lin

Abstract

Most of scheduling problems consider a deterministic environment for which the
data are known. However, in many real-world problems, the precise values of data in
scheduling models might not know in advance. For example, the precise duration of
surgery is hard to predict because it can vary widely from one case to another, even for identical procedures. The post-surgery duration is also hard to predict because it is influenced by the patient's age, gender, condition, type of surgery and type of anesthesia, etc. We consider a three-stage operating room surgery scheduling problem with uncertain surgery and post-surgery durations. The three successive stages are pre-surgery, surgery, and post-surgery. No-wait constraint is considered among the three stages. Surgeries are known in advance (elective). Multiple resources are considered throughout the surgical process: PHU (pre-operative holding unit) beds in the first stage; ORs (operating rooms) in the second stage; and PACU (post-anesthesia care unit) beds in the third stage. The objective is to minimize the makespan. We have proposed a genetic algorithm for robust scheduling (GARS). Randomly generated problem instances are tested to evaluate the performance of the proposed GARS. Computational results show that the robust schedules found by the GARS are relatively insensitive to uncertain surgery durations when compared with the non-robust schedules found by the basic GA (BGA).

Keywords

Status: accepted

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