A Multi-Objective Optimisation Approach to Multi-Modality Radiology Scheduling Problem Using Multi-Objective Genetic Algorithm (INTERNATIONAL JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING, 2026)
Congestion and long waiting times are significant problems for patients attempting to access radiology services in public tertiary hospitals in Nigeria. This paper develops a multi-objective optimisation model for the Multimodality Radiology Scheduling Problem (MRSP).  The MRSP was formulated to minimize patient waiting time, average delay beyond deadlines, and total operational cost, based on the stated constraints. A Multi-Objective Genetic Algorithm was developed for the MRSP and tested for four radiology modalities: ultrasound, CT, MRI, and X-ray. The Pareto solutions were analysed and further partitioned by k-means clustering. Sensitivity analysis was used to determine the effect of changes in the waiting time and duration. The model generated a Gantt–Pareto chart that shows the schedules for each scanner. The schedules reduce patient waiting times and minimize scanner idle periods. Patient waiting times were dispersed, with a mean ± standard deviation of 76.8 ± 56.75.  The K-means clustering revealed clusters with [short, medium, and long] waiting times [0, 9, 23, and 42; 58, 77, 86, 89, 98, 102, and 110; 120, 127, 132, 137, 153, and 173] minutes.  Sensitivity analysis results confirm that the waiting times were most affected by early jobs in each modality’s sequence. The multi-objective optimisation model was developed and effectively used to solve multimodal radiology scheduling problems. This model can be of immense benefit in planning and scheduling radiology examination problems in hospitals to reduce waiting times and average delay beyond deadlines.
Keywords: Genetic Algorithm, Multi-objective Optimisation, Radiology Scheduling Problem, Scanner Time.