Abstract: To address the optimization of emergency medical facilities in expressway service areas, a two-stage stochastic programming location model that simultaneously considered stochastic demand, stochastic travel time, road conditions and casualty triage characteristics on expressways was constructed. To validate the performance of the proposed model, Monte Carlo simulation was first employed to generate scenarios incorporating multiple random factors, and then the K-means clustering method was used to select representative scenario clusters as the final scenario set. Secondly, a hybrid-greedy simulated annealing-adaptive large neighborhood search (H-GSA-ALNS) algorithm, integrating greedy strategies, simulated annealing and adaptive large neighborhood search, was proposed and solved. In numerical analysis, the effectiveness of the proposed H-GSA-ALNS algorithm was demonstrated through comparison, and the site selection and ambulance vehicle allocation under different demand levels were analyzed. The research results indicate that the proposed H-GSA-ALNS algorithm outperforms single simulated annealing and conventional ALNS algorithm in both solution quality and convergence stability. Sensitivity analysis further reveals the impacts of different demand levels, travel-time uncertainty and maximum service distance on site selection results and ambulance allocation strategies. Under various stochastic scenarios, the proposed model achieves high demand coverage and a reasonable response-time distribution, exhibiting overall good robustness and adaptability. The proposed model and solution approach can effectively support decision-making for site selection and ambulance allocation of expressway emergency medical service system, providing quantitative decision-making basis and management insights for improving expressway emergency medical service capabilities.

Key words: traffic and transportation engineering; expressway; emergency medical services; site selection research; Monte Carlo simulation; hybrid-greedy simulated annealing-adaptive large neighborhood search (H-GSA-ALNS) algorithm

摘要： 针对高速公路服务区应急医疗设施优化问题，构建了一个同时考虑高速公路随机需求、随机行驶时间、道路状况及伤员分级特征的两阶段随机规划选址模型。为了验证模型性能，首先基于蒙特卡洛模拟构建了包含多种随机因素的场景，并使用K-均值聚类算法（K-means）选择了具有代表性的场景簇作为最终的场景集；其次基于贪婪策略、模拟退火和自适应大邻域搜索算法提出了一个混合元启发式算法（hybrid-greedy simulated annealing-adaptive large neighborhood search, H-GSA-ALNS）并求解。在数值分析中，对比证明了混合元启发式算法的有效性，并分析了不同需求水平下的选址和救护车车辆配置情况。研究结果表明：所提出的混合元启发式算法在解的质量与收敛稳定性方面均优于单一模拟退火和传统ALNS算法。敏感性分析进一步揭示了不同需求水平、行驶时间不确定性及最大服务距离对选址结果和车辆配置策略的影响。在多种随机场景下，模型均能够实现较高的需求覆盖率和合理的响应时间分布，整体表现出良好的鲁棒性和适应性。该模型及求解方法能够有效支撑高速公路应急医疗服务系统的选址与车辆配置决策，为提升高速公路应急医疗服务能力提供了定量决策依据和管理启示。

关键词: 交通运输工程；高速公路；应急医疗服务；选址研究；蒙特卡洛模拟；混合元启发式算法