基于遗传算法的门诊预约调度优化
Outpatient Appointment Scheduling Optimization with a Genetic Algorithm Approach
AI总结：
本文提出基于遗传算法的门诊预约调度优化方法，有效解决时间重叠和临床不兼容问题，提升患者体验和医疗效率。
摘要：
复杂医疗预约调度的优化仍然是多中心医疗环境中的重大运营挑战，其中临床安全规程和患者物流必须协调一致。本研究提出并评估了一种遗传算法（GA）框架，旨在自动化多个医疗操作的调度，同时遵守严格的跨程序不兼容规则。使用包含50个医疗操作的合成数据集，涵盖四个医疗设施，我们比较了两种GA变体，即预排序和无序，与确定性先到先得（FCFS）和随机选择基线。我们的结果表明，GA框架实现了100%的约束满足率，有效解决了FCFS基线在60%和40%情况下未能解决的时间重叠和临床不兼容问题。此外，GA变体在患者导向指标上表现出统计学显著改进（p < 0.001），实现了空闲时间比率（ITR）经常低于0.4，并减少了跨医疗中心的出行。虽然GA（有序）变体提供了更优的初始搜索位置，但两种进化模型在第100代时收敛到可比的全局最优。这些发现表明，从人工调度转向自动化元启发式方法可以提高临床完整性，减少行政负担，并通过减少等待时间和物流负担显著改善患者体验。
英文摘要：
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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