ORAHS2025
Abstract Submission

39. Scheduling surgery requests from outdoctors and indoctors under uncertainty

Invited abstract in session FA-2: Surgery scheduling 2, stream Sessions.

Friday, 9:00-10:30
Room: NTNU, Realfagbygget R8

Authors (first author is the speaker)

1. Serhat Gül
TED University
2. ARSHAM ATASHI KHOEI
School of Management, University of Bath
3. Melih Celik
School of Management, University of Bath

Abstract

Hospitals increasingly allow surgeons who are not full-time employees (outdoctors) to rent operating rooms (ORs) to generate revenue. However, to have their surgeries scheduled, outdoctors need to compete with the full-time hospital-employed surgeons of the hospital (indoctors) for limited resources. Balancing the needs of both surgeon types while accounting for uncertainty in surgery durations creates a challenging surgery planning problem. To address this problem, we formulate a stochastic mixed-integer programming model to select outdoctor surgery requests and schedule both indoctor and selected outdoctor surgeries across ORs and days within a finite planning horizon. Key performance measures include revenue from accepted outdoctor requests, patient waiting times, and expected OR overtime and idle time. To solve the model, we propose a problem-based scenario reduction algorithm based on loss function minimization (LFM). We solve the LFM problem using both a mixed-integer second-order cone programming model and a sub-gradient-based heuristic using real surgery duration data. We compare our scenario reduction algorithm against three alternatives from the literature. Additionally, we provide insights into the benefits of incorporating outdoctor surgeries into hospital surgery planning and exploring different levels of flexibility in handling these requests. Finally, we perform sensitivity analyses on various model parameters and estimate the value of the stochastic solution.

Keywords

Status: accepted

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