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中国管理科学

中国管理科学 ›› 2019, Vol. 27 ›› Issue (10): 110-119.doi: 10.16381/j.cnki.issn1003-207x.2019.10.011

• 论文 • 上一篇    下一篇

考虑空间随机需求的急救站点选址规划

苏强1, 杨微2, 王秋根3   

  1. 1. 同济大学经济与管理学院, 上海 201804;
    2. 深圳大学管理学院, 广东 深圳 518060;
    3. 上海市第一人民医院创伤中心, 上海 201620
  • 收稿日期:2018-01-04 修回日期:2018-04-09 出版日期:2019-10-20 发布日期:2019-10-25
  • 通讯作者: 杨微(1987-),男(汉族),黑龙江大庆人,深圳大学管理学院,博士,研究方向:物流与供应链管理、路径与流量规划,E-mail:yangvmail@163.com. E-mail:yangvmail@163.com
  • 基金资助:
    国家自然科学基金资助项目（71432007，71972146）

Ambulance Location Planning Considering the Spatial Randomness of Demand

SU Qiang1, YANG Wei2, WANG Qiu-gen3   

  1. 1. School of Economics & Management, Tongji University, Shanghai 201804, China;
    2. College of Management, Shenzhen University, Shenzhen 518060, China;
    3. Trauma center, Shanghai General Hospital, Shanghai 201620, China
  • Received:2018-01-04 Revised:2018-04-09 Online:2019-10-20 Published:2019-10-25

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摘要： 随着人民生活水平的提高和人口老龄化加剧，公众对急救医疗服务的要求越来越高。为保证急救需求的响应及时性，急救站点的选址规划问题受到广泛关注。急救站点选址的依据是需求的分布，然而现有研究未能充分考虑急救需求在空间分布上的随机性影响，通常将其空间分布简化为若干个集中需求点，或将规划空间划分为若干矩形网格，然而这种需求刻画过于粗略，导致需求覆盖水平的计算不够准确，影响配置方案的有效性。针对该问题，本研究应用高斯混合模型解决了急救需求的空间分布刻画问题，创新性地提出基于高斯混合聚类的站点选址规划方法，考虑急救需求时空随机性，建立了相应的机会约束规划模型。实际数据的验证分析表明，该选址方法能够显著减少服务延误时间和次数，保证急救服务的响应及时性。

关键词: 急救站点规划, 空间随机性, 高斯混合模型, 机会约束规划

Abstract: To guarantee that the emergency calls can be responded to in time, the government is obliged to implement an effective ambulance location plan. In practice, the emergency medical service (EMS) system works in an uncertain environment with stochastic demand, response time, and travel time. The uncertainty of these factors significantly affects ambulance location planning. However, most recent studies in this field fail to consider adequately the effect of the spatial randomness of demand, since it is difficult to describe quantitatively. As a result, most location plans are not efficient.
In this study, Gaussian mixture model clustering is innovatively utilized to describe the spatial uncertain demand quantitatively. The entire planning region can be re-clustered into several Gaussian-distributed demand areas. Based on the depiction of the spatial randomness of demand, an integer programming model for ambulance location planning is constructed. Additionally, in this model, a strict service preference order is specified among the responsible sites for each demand area. The chance-constrained programming method is used to solve the proposed model.
Two years' data from the Shanghai Songjiang District are used to validate the method proposed. The data from 2013 are utilized to fit the spatial distribution of demand. The data from 2014 are used to test and verify the obtained models. Compare with the location plan which ignores the spatial randomness of demand, the performances of the optimal plan obtained by the proposed method are much better. The experimental results indicate that the spatial randomness of demand can significantly affect the effectiveness of the ambulance location plan. Assumptions without considering spatial randomness will result in lots of unexpected delays. Consequently, the plan obtained is unreliable to implement in practice.
The effects of the spatial randomness of EMS demands are considered in our proposed method. Therefore, the service delay caused by spatial distribution randomness can be significantly decreased. For life-improving projects such as the establishment of the emergency medical services network, reducing delay in rendering emergency care is the basic requirement to increase the public's satisfaction with the healthcare system.

Key words: ambulance location planning, spatial randomness, Gaussian mixture model, chance-constrained programming

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