急救车资源布局与运营调度优化研究综述  

Resource Layout and Operation Optimization Review of Emergency Medical Service Vehicles

作　　者：沈煜 毕维翰 王兰[2] 杜豫川[1] SHEN Yu;BI Wei-han;WANG Lan;DU Yu-chuan(Key Laboratory of Road and Traffic Engineering of Ministry of Education,Tongji University,Shanghai 201804,China;College of Architecture and Urban Planning,Tongji University,Shanghai 200092,China)

机构地区：[1]同济大学道路与交通工程教育部重点实验室,上海201804 [2]同济大学建筑与城市规划学院,上海200092

出　　处：《中国公路学报》2025年第1期249-267,共19页China Journal of Highway and Transport

基　　金：国家自然科学基金项目(52272321);中央高校基本科研业务费专项资金项目。

摘　　要：为系统分析和总结急救车资源布局与运营调度的研究现状与发展趋势,基于Web of Science数据库收录的1 502篇文献,从战略层、战术层、操作层3个层次梳理急救车资源布局与运营调度的研究脉络。研究结果表明,在战略层,急救车定位问题的研究重点在于持续改善覆盖定义、准确刻画系统内在的不确定性,主要研究方法包括随机规划、鲁棒优化等不确定性建模和求解方法。在战术层,急救车重定位问题按照触发重定位决策的方式分为多阶段重定位和动态重定位,由于重定位较定位更为复杂,研究重点在于应用启发式、强化学习等算法求解现实中的大规模问题。操作层关键的决策问题包括急救车指派、目的地选择和路径规划:急救车指派相关研究呈现从基于规则到基于模型,从独立优化到和重定位联合优化的发展历程;目的地选择涉及与医院工作负载的协同优化,路径规划则主要针对灾难响应等特殊场景。在未来的研究中,需要紧扣动态性和随机性2条研究主线,在准确刻画院前医疗急救系统不确定性来源的同时,充分利用更细粒度的数据辅助实时决策。在建模和求解的具体技术上,应考虑打通不同层次的多个决策问题开展联合优化,实现急救车定位与调度方案从局部最优到系统最优的迭代,并持续开发能处理现实大规模场景的高效求解算法以支撑联合优化的求解。To systematically analyze and summarize the current research status and development trends in the operational management of emergency medical service(EMS)vehicles,this study organizes the research framework of EMS vehicle operational management into three levels:strategic,tactical,and operational,based on 1502 articles indexed from the Web of Science database.The findings reveal that at the strategic level,research on EMS vehicle location focuses on continuous improvement of coverage definition and accurate characterization of inherent uncertainties within the system.Key research methods include stochastic planning and robust optimization as uncertainty modeling and optimization approaches.At the tactical level,EMS vehicle relocation is categorized into multiperiod and dynamic relocation based on the triggering of relocation decisions.Given the complexity of relocation with respect to location,the research emphasizes the application of heuristic and reinforcement learning algorithms in addressing real-world large-scale problems.Decisive issues at the operational level include EMS vehicle dispatch,destination selection,and route planning.Research on EMS vehicle dispatch has evolved from rule-to model-based and from independent to joint optimization in relocation.Destination selection involves coordinated optimization with hospital workload,and route planning primarily addresses special scenarios such as disaster response.In future research,optimization in EMS vehicle operational management should focus on the dual research threads of dynamics and uncertainty.This entails accurately characterizing the sources of system uncertainty while leveraging finer-grained data to assist real-time decision-making.In terms of specific modeling and solving techniques,joint optimization of multiple decision problems across different levels should be conducted to progress from local to system optimum EMS vehicle location and dispatch schemes.However,efficient algorithms for handling real-world large-scale scenarios continue to pos

关 键 词：交通工程 急救车运营调度 综述 紧急医疗服务 资源布局优化 

分 类 号：U492.2[交通运输工程—交通运输规划与管理]