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[2602.21995] Outpatient Appointment Scheduling Optimization with a Genetic Algorithm Approach
Summary
This article presents a Genetic Algorithm framework for optimizing outpatient appointment scheduling in healthcare, demonstrating significant improvements over traditional methods.
Why It Matters
Efficient appointment scheduling is crucial in healthcare to enhance patient experience and operational efficiency. This study showcases how genetic algorithms can automate scheduling, reduce wait times, and ensure compliance with clinical protocols, addressing a common challenge in multi-center healthcare environments.
Key Takeaways
- The Genetic Algorithm framework achieved a 100% constraint fulfillment rate in scheduling.
- Compared to traditional methods, the GA significantly reduced patient wait times and logistical burdens.
- Both GA variants converged to similar optimal solutions, indicating robustness in the approach.
Computer Science > Neural and Evolutionary Computing arXiv:2602.21995 (cs) [Submitted on 25 Feb 2026] Title:Outpatient Appointment Scheduling Optimization with a Genetic Algorithm Approach Authors:Ana Rodrigues, Rui Rego View a PDF of the paper titled Outpatient Appointment Scheduling Optimization with a Genetic Algorithm Approach, by Ana Rodrigues and 1 other authors View PDF HTML (experimental) 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-health...
