Outpatient Appointment Scheduling Optimization with a Genetic Algorithm Approach

Researchers Ana Rodrigues and Rui Rego have published a new study demonstrating a Genetic Algorithm (GA) framework that can fully automate complex outpatient appointment scheduling. The paper, 'Outpatient Appointment Scheduling Optimization with a Genetic Algorithm Approach,' tackles the significant operational challenge of reconciling clinical safety protocols with patient logistics in multi-center healthcare environments. The system is designed to schedule multiple medical acts while adhering to strict inter-procedural incompatibility rules, a task that often overwhelms manual or simple automated systems.

The study compared two GA variants—Pre-Ordered and Unordered—against deterministic First-Come, First-Served (FCFS) and Random Choice baselines using a synthetic dataset of 50 medical acts across four healthcare facilities. The results were striking: the GA framework achieved a 100% constraint fulfillment rate, successfully resolving all scheduling conflicts. It fixed temporal overlaps and clinical incompatibilities that the FCFS baseline failed to address in 60% and 40% of cases, respectively. Furthermore, the GA showed statistically significant improvements (p < 0.001) in patient-centric metrics, frequently achieving an Idle Time Ratio below 0.4 and reducing unnecessary trips between health centers. Both evolutionary models converged to comparable global optima by the 100th generation, suggesting that transitioning to such automated metaheuristic approaches could enhance clinical integrity, slash administrative overhead, and dramatically improve the patient experience by minimizing wait times and logistical burdens.