ORBITER：面向智能体的最后一公里配送的冲突感知决策方法
ORBITER: Conflict-Aware Decision-Making for Agentic Last-Mile Delivery
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中文总结 AI 辅助
本文针对最后一公里配送决策的可解释性与可靠性问题，提出ORBITER框架，结合LLM与结构化决策机制，在四城市数据上较最优基线平均提升9.2%，验证了方法的有效性。
中文摘要 AI 辅助
最后一公里配送旨在处理快递员动态到达的订单，同时建模复杂的时空关联。近期基于学习的方法会建模订单间的时空依赖关系以预测快递员服务序列，但未对下一个订单的决策过程进行解释。用语言描述当前配送状态可让大语言模型（LLM）明确推理单个决策背后的空间、时间和行为线索；不过，作为直接预测器，LLM对任务呈现方式较为敏感，常产生不可靠的决策。为应对这些挑战，本文提出ORBITER（面向最后一公里配送下一个订单决策的智能体订单仲裁器）。ORBITER通过决策点建模快递员服务，每个决策点包含快递员的时空状态和可见订单，并暴露局部权衡以用于建模和验证；固定提议者对候选订单进行排序，结构化报告识别出排序不一致之处；LLM利用特定任务工具收集领先候选订单的证据，同时独立评论员会对照证据核查最终决策。本文在四个城市的数据上开展了广泛评估，结果显示ORBITER的性能较现有最优基线平均提升高达9.2%，验证了其有效性。
英文摘要
Last-mile delivery aims to handle dynamically arriving orders with couriers while modeling complex spatial and temporal correlations. Recent learning-based methods model spatiotemporal dependencies among orders to predict courier service sequences, but leave next-order decision making unexplained. Describing the current delivery state in language allows LLMs to reason explicitly about the spatial, temporal, and behavioral cues behind an individual decision. As direct predictors, however, LLMs remain sensitive to task presentation and often produce unreliable decisions. To address these challenges, we introduce ORBITER, an agentic Order Arbiter for next-order decision-making in last-mile delivery. ORBITER models courier service through decision points, each containing the courier's spatiotemporal state and visible orders and exposing local trade-offs for modeling and verification. Fixed proposers rank the candidates, and a structured report identifies where their rankings disagree. The LLM uses task-specific tools to gather evidence on the leading alternatives, while an independent critic checks the resulting decision against that evidence. We conduct extensive evaluations on data in four cities, where ORBITER outperforms existing state-of-the-art baselines by up to 9.2% on average showing its effectiveness.
