Anaesthetist Rostering Synthetic Dataset: Multi-Constraint Healthcare Staff Scheduling Benchmark

Dataset DescriptionThis synthetic dataset generator creates realistic test instances for the anaesthetist rostering problem in hospital settings. The dataset is designed for benchmarking optimisation algorithms and includes:Key Features:<b>3 Dataset Sizes</b>: Small (20), Medium (50), and Large (100) anaesthetists<b>11 Hard Constraints</b>: Based on real hospital requirements<b>10 Soft Constraints</b>: For solution quality measurement<b>3 Months of Data</b>: 28 days per month (84 days total)<b>17 Hospital Locations</b>: 7 monthly + 10 weekly roster locations<b>10 Request Types</b>: Including leave, training, and special dutiesSpecial Characteristics:SICU special staffing rule (2 anaesthetists when CICU active)Weekend/holiday pairing requirementsWorkload distribution tracking across monthsPrevious month continuity assignmentsLocation combination constraintsFile Structure:Each dataset size contains:22 CSV files per month × 3 months = 66 filesStatistical analysis results (analysis.json)Quality assurance metrics Complete documentationResearch Applications:Algorithm benchmarkingConstraint satisfaction research Healthcare optimisation studiesFairness in scheduling researchMulti-objective optimizationValidation:90% quality score achieved across all datasetsStatistical similarity to real-world patterns verifiedConstraint compliance validatedReproducible with seed 12345<br>This synthetic dataset generator is based on the problem structure and constraints described in:Abdullah et al. (2025). "Advancing Anaesthetist Rostering Quality: A Practical Approach Toward Fairness and Efficiency." IEEE Access, 13, 12692-12708.<br>Note: The IEEE Access paper used real hospital data. This synthetic dataset is a new contribution that creates artificial test instances based on the exact problem specification, enabling reproducible research while protecting sensitive information.