面向时间敏感血液样本采集与配送的动态调度
Dynamic Dispatching for Time-Sensitive Blood Sample Collection and Delivery
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中文总结 AI 辅助
针对血液样本配送延迟问题，提出基于马尔可夫决策过程的神经近似动态规划调度框架，通过对偶值函数分解提升按时配送样本量占比，减少对外部快递的依赖。
中文摘要 AI 辅助
医院和诊断实验室依赖快递员从地理分散的采集中心采集血液样本，并在其短暂的有效期内送达分析；延迟配送会导致成本高昂的重新采集，并可能延误诊断。我们研究此类快递车队的实时调度，其中样本请求全天随机到达各中心，中央调度员必须反复决定派遣哪些车辆、每辆车应访问哪些中心，以及是立即采集紧急样本还是将其合并到后续行程中，且需满足严格的配送截止日期和车辆容量限制。与在需求已知前固定路线的静态规划模型、仅响应当前积压的反应式启发式算法不同，我们的方法在权衡立即采集与合并时会预测未来到达情况。我们将该问题建模为马尔可夫决策过程，并开发了用于集中调度的神经近似动态规划框架。该方法引入了对偶值函数分解，通过基于决策后状态训练的神经网络分别表示车辆状态和采集中心状态。这些学习到的估计值通过匹配公式整合，该公式在平衡供应可用性、需求紧迫性和下游机会成本的同时选择调度动作。在真实的大多伦多地区网络上进行的计算实验，将所提策略与短视基线和消融变体进行了比较。结果表明，所提对偶值函数策略比短视基线将按时配送的样本量占比提高了1至9个百分点，在车队、容量、截止日期和路线规划约束严格时收益最大；这些按时配送的收益进而减少了对成本高昂的外部快递员的依赖。
英文摘要
Hospitals and diagnostic laboratories rely on couriers to collect blood samples from geographically dispersed collection centres and deliver them for analysis before their short viability windows expire; late deliveries force costly re-collection and can delay diagnosis. We study the real-time dispatching of such a courier fleet, in which sample requests arrive stochastically at the centres throughout the day and a central dispatcher must repeatedly decide which vehicles to send, which centres each should visit, and whether to collect urgent samples immediately or consolidate them into later trips, subject to hard delivery deadlines and vehicle capacity limits. Unlike static planning models, which fix routes before demand is known, and reactive heuristics, which respond only to the current backlog, our approach anticipates future arrivals when weighing immediate collection against consolidation. We formulate the problem as a Markov decision process and develop a neural approximate dynamic programming framework for centralized dispatch. The method introduces a dual value function decomposition that separately represents vehicle states and collection-centre states through neural networks trained on post-decision states. These learned estimates are integrated through a matching formulation that selects dispatch actions while balancing supply availability, demand urgency, and downstream opportunity cost. Computational experiments on a realistic Greater Toronto Area network compare the proposed policy with myopic baselines and ablation variants. Results show that the proposed dual value function policy raises the share of sample volume delivered on time by 1 to 9 percentage points over myopic baselines, with the largest gains under tight fleet, capacity, deadline, and routing constraints; these on-time gains, in turn, reduce reliance on costly external couriers.
