Outpatient Appointment Scheduling Optimization with a Genetic Algorithm Approach

• URL: http://arxiv.org/abs/2602.21995v1
• Date: Wed, 25 Feb 2026 15:15:57 GMT
• Title: Outpatient Appointment Scheduling Optimization with a Genetic Algorithm Approach
• Authors: Ana Rodrigues, Rui Rego,
• Abstract summary: This study proposes and evaluates a Genetic Algorithm framework designed to automate the scheduling of medical acts.<n>Using a synthetic dataset encompassing 50 medical acts, we compared two GA variants, Pre-Ordered and Unordered, against deterministic First-Come, First-Served (F) and Random Choice baselines.
• Score: 0.19575541492160772
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The optimization of complex medical appointment scheduling remains a significant operational challenge in multi-center healthcare environments, where clinical safety protocols and patient logistics must be reconciled. This study proposes and evaluates a Genetic Algorithm (GA) framework designed to automate the scheduling of multiple medical acts while adhering to rigorous inter-procedural incompatibility rules. Using a synthetic dataset encompassing 50 medical acts across four healthcare facilities, we compared two GA variants, Pre-Ordered and Unordered, against deterministic First-Come, First-Served (FCFS) and Random Choice baselines. Our results demonstrate that the GA framework achieved a 100% constraint fulfillment rate, effectively resolving temporal overlaps and clinical incompatibilities that the FCFS baseline failed to address in 60% and 40% of cases, respectively. Furthermore, the GA variants demonstrated statistically significant improvements (p < 0.001) in patient-centric metrics, achieving an Idle Time Ratio (ITR) frequently below 0.4 and reducing inter-healthcenter trips. While the GA (Ordered) variant provided a superior initial search locus, both evolutionary models converged to comparable global optima by the 100th generation. These findings suggest that transitioning from manual, human-mediated scheduling to an automated metaheuristic approach enhances clinical integrity, reduces administrative overhead, and significantly improves the patient experience by minimizing wait times and logistical burdens.

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