Proceedings of the International Conference on Decision Aid and Artificial Intelligence (ICODAI 2024)

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Towards Tailoring Reinforcement Learning to Solve the Online Surgery-Planning and Scheduling Problem

Authors
Khouloud Bennour1, Imen Ghazouani1, Asma Ouled Bedhief1, Safa Bhar Layeb1, 2, *, Najla Omrane Aissaoui1
1LR-OASIS, National Engineering School of Tunis, University of Tunis El Manar, Tunis, Tunisia
2Centre Génie Industriel, Université Toulouse, IMT Mines Albi, Albi, France
*Corresponding author. Email: safa.layeb@enit.utm.tn
Corresponding Author
Safa Bhar Layeb
Available Online 24 February 2025.
DOI
10.2991/978-94-6463-654-3_14How to use a DOI?
Keywords
Operating room; recovery bed; planning; scheduling; planning horizon; surgery; reinforcement learning; makespan
Abstract

This paper addresses the online surgery planning and scheduling problem for operating rooms and recovery beds. We aim to minimize the makespan by dynamically assigning surgery dates, operating rooms, and recovery beds.

Our integrated framework uses a Mixed-Integer Linear Program (MILP) solved with Python’s PuLP package for initial scheduling and Reinforcement Learning (RL) for real-time adjustments. The MILP provides a static schedule, while RL handles disruptions and updates schedules using experience replay and target networks for stable training. Preliminary results demonstrate that this approach effectively manages scheduling complexities, improving operational efficiency and minimizing idle times. This highlights the potential of combining MILP and RL for adaptive surgical scheduling.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Decision Aid and Artificial Intelligence (ICODAI 2024)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
24 February 2025
ISBN
978-94-6463-654-3
ISSN
2589-4919
DOI
10.2991/978-94-6463-654-3_14How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Khouloud Bennour
AU  - Imen Ghazouani
AU  - Asma Ouled Bedhief
AU  - Safa Bhar Layeb
AU  - Najla Omrane Aissaoui
PY  - 2025
DA  - 2025/02/24
TI  - Towards Tailoring Reinforcement Learning to Solve the Online Surgery-Planning and Scheduling Problem
BT  - Proceedings of the  International Conference on Decision Aid and Artificial Intelligence (ICODAI 2024)
PB  - Atlantis Press
SP  - 170
EP  - 185
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-654-3_14
DO  - 10.2991/978-94-6463-654-3_14
ID  - Bennour2025
ER  -