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.

翻译：在多中心医疗环境中，复杂医疗预约调度的优化仍是一项重大运营挑战，其中临床安全规程与患者流程必须协调兼顾。本研究提出并评估了一种遗传算法（GA）框架，旨在自动化安排多项医疗活动，同时遵循严格的跨流程互斥规则。通过使用涵盖四个医疗机构共50项医疗活动的合成数据集，我们将两种GA变体（预排序型与无序型）与确定性的先到先服务（FCFS）及随机选择基线进行了对比。结果表明，GA框架实现了100%的约束满足率，有效解决了时间重叠和临床互斥问题——而FCFS基线分别有60%和40%的案例未能处理这些问题。此外，GA变体在患者中心指标上展现出统计学显著改进（p < 0.001），其空闲时间比（ITR）常低于0.4，并减少了跨医疗中心的往返次数。虽然GA（有序型）变体提供了更优的初始搜索定位，但两种进化模型均在100代时收敛至相当的全局最优解。这些发现表明，从人工调度转向自动化元启发式方法，能够增强临床完整性、降低管理成本，并通过最小化等待时间与流程负担显著改善患者体验。