Stochastic Modeling and Optimization to Support Emergency Response Systems

支持应急响应系统的随机建模和优化

• 批准号: 2035086
• 负责人: Shane Henderson
• 金额: $ 37.89万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2035086&HistoricalAwards=false
• 关键词: Stochastic; Modeling; Optimization; Support; Emergency

This award will contribute to the Nation's health and welfare by developing new mathematical and computational tools to inform the design of volunteer schemes for out-of-hospital health emergencies such as cardiac arrest. Cardiac arrest occurs when a patient’s heart enters an atypical rhythm. Death follows rapidly unless the patient receives medical attention. Lives can be saved if the patient receives cardiopulmonary resuscitation (CPR) quickly. Recently, volunteer schemes have arisen whereby volunteers install an app on their smartphone that tracks their location. Volunteers near a cardiac arrest are notified by the app and can choose to respond, thereby saving valuable minutes in the response time to initiate CPR. Volunteer schemes are emerging worldwide, but key questions relating to their design remain unanswered. For example, how many volunteers are needed to ensure impact on survival rates, and when one can potentially recruit volunteers of multiple types, which should be prioritized? This project develops research methods to answer these questions and others. Related research directions address the potential impact of broadband connections that enable a remote paramedic or doctor to advise on-scene treatment by a paramedic or ambulance officer, and how to prioritize dispatch decisions in periods when emergency services are severely loaded and/or traffic is congested.This award develops mathematical and computational tools to inform the design of volunteer schemes. Central principles include the integrated use of spatial Poisson point processes to model volunteer locations with convex optimization to solve problems relating to the distribution of volunteer capacity across a city to maximize patient survival. The related question of ambulance dispatch when ambulances are severely loaded is addressed through use of approximate dynamic programming, exploiting deep neural networks to approximate both the value function and the choice of policy. Simulation optimization will underlie some aspects of this work. In that context, the use of biased gradient estimators in search will be explored. Most methods for gradient estimation in simulation optimization result in biased estimators which are viewed in practice as unsatisfactory. This project will explore their potential efficacy; they have been seen to be of great value in some examples and the research will explore their potential more broadly.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项将通过开发新的数学和计算工具，为诸如心脏骤停等院外健康紧急情况的志愿者计划的设计提供信息，从而为国家的健康和福利做出贡献。心脏骤停发生在病人的心脏进入非典型心律时。除非病人得到医疗照顾，否则很快就会死亡。如果病人迅速接受心肺复苏术（CPR），就可以挽救生命。最近，志愿者计划兴起，志愿者在智能手机上安装一个应用程序，跟踪他们的位置。心脏骤停附近的志愿者会收到应用程序的通知，并可以选择做出响应，从而节省宝贵的响应时间来启动CPR。志愿人员计划正在世界各地兴起，但与其设计有关的关键问题仍然没有答案。例如，需要多少志愿者才能确保对生存率的影响，以及何时可以招募多种类型的志愿者，应该优先考虑哪种类型？该项目开发的研究方法来回答这些问题和其他人。相关研究方向涉及宽带连接的潜在影响，使远程护理人员或医生能够建议护理人员或救护人员进行现场治疗，以及如何在紧急服务严重负荷和/或交通拥堵期间优先考虑调度决策。该奖项开发了数学和计算工具，为志愿者计划的设计提供信息。中心原则包括综合利用空间泊松点过程来模拟志愿者的位置与凸优化，以解决有关的问题，志愿者的能力分布在整个城市，以最大限度地提高病人的生存。当救护车严重负载时，救护车调度的相关问题通过使用近似动态规划来解决，利用深度神经网络来近似值函数和策略选择。 仿真优化将成为这项工作的某些方面的基础。在这种情况下，将探讨在搜索中使用有偏梯度估计。 在模拟优化中，大多数梯度估计方法都会导致有偏估计，这在实践中是不令人满意的。 该项目将探索它们的潜在功效;在一些例子中，它们被认为具有很大的价值，研究将更广泛地探索它们的潜力。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

项目成果

Automatic Differentiation for Gradient Estimators in Simulation

• DOI: 10.1109/wsc57314.2022.10015421
• 发表时间: 2022-12
• 期刊: 2022 Winter Simulation Conference (WSC)
• 作者: Matthew T. Ford;S. Henderson;David J. Eckman

Minimizing Multimodular Functions and Allocating Capacity in Bike-Sharing Systems

最小化多模块功能并分配自行车共享系统的容量

• DOI: 10.1287/opre.2022.2320
• 发表时间: 2022
• 期刊: Operations Research
• 影响因子: 2.7
• 作者: Freund, Daniel;Henderson, Shane G.;Shmoys, David B.
• 通讯作者: Shmoys, David B.

Diagnostic Tools for Evaluating and Comparing Simulation-Optimization Algorithms

• DOI: 10.1287/ijoc.2022.1261
• 发表时间: 2023-01
• 期刊: INFORMS J. Comput.
• 作者: David J. Eckman;S. Henderson;S. Shashaani

Reflections on Simulation Optimization

对仿真优化的思考

• 发表时间: 2021
• 期刊: Proceedings of the Winter Simulation Conference
• 作者: Henderson, Shane G.

How should volunteers be dispatched to out-of-hospital cardiac arrest cases?

院外心脏骤停病例如何派遣志愿者？

• DOI: 10.1007/s11134-022-09752-z
• 发表时间: 2022
• 期刊: Queueing Systems
• 影响因子: 1.2
• 作者: Henderson, Shane G.;Berg, Pieter L.;Jagtenberg, Caroline J.;Li, Hemeng
• 通讯作者: Li, Hemeng