Work

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

Year: 2025

Type: book-chapter

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

Source: Atlantis Highlights in Intelligent Systems/Atlantis highlights in intelligent systems

DOI: 10.2991/978-94-6463-654-3_14

Authors: Khouloud Bennour, Imen Ghazouani, Asma Ouled Bedhief, Safa Bhar Layeb, Najla Omrane Aissaoui

Institutions: Tunis University, National Engineering School of Tunis, Tunis El Manar University, IMT Mines Albi, Centre de Génie Industriel

Language: English

Topic: Healthcare Operations and Scheduling Optimization

Subfield: Emergency Medical Services

Field: Health Professions

Domain: Health Sciences

Open Access status: hybrid

OpenAlex ID: W4407808024

FWCI: 30

Cites:

Cited by: 3

Related to: 10