270. Enhancing Robustness in Surgical Scheduling for Elective Surgery Planning under multiple uncertainties: A Column-and-Constraint Generation Algorithm
Contributed abstract in session TC-1: Poster session, stream Posters.
Tuesday, 14:00-15:30Room: Auditorium
Authors (first author is the speaker)
| 1. | Salma Makboul
|
| LIST3N, UTT |
Abstract
This research introduces a novel approach to handling the challenges of the dynamic Master Surgical Schedule (MSS) and the advance scheduling problem under various uncertainties. We propose a Column-and-Constraint Generation (C\&CG) algorithm with the main goal of minimizing assignment costs while considering different operating room (OR) restrictions and downstream resources capacity. These uncertainties include surgery duration, postoperative intensive care unit length of stays, and emergency arrival. We highlight the importance of robust optimization in managing the dynamic nature of surgical scheduling, where uncertain factors can disrupt operational efficiency. Our approach involves a two-stage robust optimization method and incorporates polyhedral uncertainty sets to improve the scheduling process's resilience. Through iterative refinement, our algorithm broadens the solution space, leading to better upper and lower bounds. We compare C\&CG with the cutting-plane algorithm. Additionally, we analyze how risk adjustment affects the OR's utilization rate and the occurrence of cancellations using real data from a French hospital. This sheds light on how our methodology can optimize OR and downstream resources in hospitals, making scheduling and planning of the ORs more efficient and effective.
Keywords
- Operating room planning and scheduling
- Optimization algorithms
- Patient flow
Status: accepted
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