用优化增强的机器学习模型，让救护车调度更高效，响应时间最多快30%。
Optimization-Augmented Machine Learning for Vehicle Operations in Emergency Medical Services
- 结合组合优化与机器学习，自动学习最优派车和调运策略。
- 在旧金山数据上测试，平均响应时间减少最多达30%，且提速超87.9%。
- 适合应急医疗系统、城市交通调度等需实时决策的场景。
为满足法律要求并及时救治患者，急救医疗服务（EMS）系统必须缩短响应时间。为此，我们研究一种集中控制的EMS系统，通过学习在线救护车调度与再部署策略，以最小化系统内救护车的平均响应时间——即接到紧急呼叫时派车，并在服务结束后将其重新部署至待命位置。我们提出一种新型组合优化增强型机器学习流程，可高效学习调度与再部署策略。在此框架下，我们进一步展示了如何解决底层全信息问题以生成训练数据，并设计了一种增强方案，通过缓解状态空间中的分布偏移来提升模型泛化能力。相比依赖训练时增强的现有方法，本方法在保持竞争性能的同时，运行时间节省高达87.9%。为评估其效果，我们基于旧金山911呼叫数据开展数值案例研究。结果表明，在不同资源与需求情景下，所学策略均优于现有在线基准，平均响应时间最高降低30%。
原文摘要 · Abstract (English)
Minimizing response times to meet legal requirements and serve patients in a timely manner is crucial for Emergency Medical Service (EMS) systems. Achieving this goal necessitates optimizing operational decision-making to efficiently manage ambulances. Against this background, we study a centrally controlled EMS system for which we learn an online ambulance dispatching and redeployment policy that aims at minimizing the mean response time of ambulances within the system by dispatching an ambulance upon receiving an emergency call and redeploying it to a waiting location upon the completion of its service. We propose a novel combinatorial optimization-augmented machine learning pipeline that allows to learn efficient policies for ambulance dispatching and redeployment. In this context, we further show how to solve the underlying full-information problem to generate training data and propose an augmentation scheme that improves our pipeline's generalization performance by mitigating a possible distribution mismatch with respect to the considered state space. Compared to existing methods that rely on augmentation during training, our approach offers substantial runtime savings of up to 87.9% while yielding competitive performance. To evaluate the performance of our pipeline against current industry practices, we conduct a numerical case study on the example of San Francisco's 911 call data. Results show that the learned policies outperform the online benchmarks across various resource and demand scenarios, yielding a reduction in mean response time of up to 30%.
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