SIMULATION OPTIMISATION OF ULTRASONOGRAPHY RESOURCE SCHEDULING WITH MACHINE LEARNING

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Saracoglu I., ÖZEN F.

International Journal of Simulation Modelling, vol.24, no.3, pp.437-448, 2025 (SCI-Expanded, Scopus) identifier identifier

  • Publication Type: Article / Article
  • Volume: 24 Issue: 3
  • Publication Date: 2025
  • Doi Number: 10.2507/ijsimm24-3-732
  • Journal Name: International Journal of Simulation Modelling
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Communication Abstracts, INSPEC, Metadex, Civil Engineering Abstracts
  • Page Numbers: pp.437-448
  • Keywords: Discrete-Event Simulation, Health System Resources, Healthcare Systems, Machine Learning, Resource Scheduling
  • Yıldız Technical University Affiliated: Yes

Abstract

This study proposes a decision-support framework to optimize radiologist staffing in the ultrasonography department. Patient arrival rates were forecast using Light Gradient Boosting Machine (LightGBM) with feature expansion, achieving 99.99 % accuracy over one-month period. A discrete-event simulation model was subsequently used to determine the number of radiologists required to meet target waiting times. Based on the simulation results, hourly radiologist requirements were identified, and an optimized schedule was generated. By integrating machine learning, simulation, and scheduling, this framework supports data-driven planning and can be applied to other healthcare services and facilities facing demand uncertainty. (Received in May 2025, accepted in July 2025. This paper was with the authors 3 weeks for 2 revisions.).