用大模型评估配送路线，提升最后一公里效率
Optimizing delivery for quick commerce factoring qualitative assessment of generated routes
- 用大模型分析路线是否符合政策规则
- 开源模型识别问题准确率达79%，闭源达86%
- 适合物流优化与智能调度研究者
印度电商市场预计快速增长，最后一公里配送占运营成本近一半。尽管基于车辆路径问题（VRP）的求解器广泛用于配送规划，但在实际场景中受限于地址不规范、地图信息不全及距离估算计算约束。本文提出一个框架，利用大语言模型（LLMs）对VRP生成的路线进行基于政策标准的批判性评估，帮助物流运营商判断并优先选择更优方案。通过生成、标注和评估400个案例验证方法有效性。结果显示，开源LLM识别路由问题准确率为79%，而专有推理模型最高可达86%。研究表明，基于LLM的路线评估可作为超越传统距离与时间指标的有效且可扩展的评价层，对提升配送成本效益、可靠性和可持续性具有重要意义，尤其适用于印度等发展中国家。
原文摘要 · Abstract (English)
Indias e-commerce market is projected to grow rapidly, with last-mile delivery accounting for nearly half of operational expenses. Although vehicle routing problem (VRP) based solvers are widely used for delivery planning, their effectiveness in real-world scenarios is limited due to unstructured addresses, incomplete maps, and computational constraints in distance estimation. This study proposes a framework that employs large language models (LLMs) to critique VRP-generated routes against policy-based criteria, allowing logistics operators to evaluate and prioritise more efficient delivery plans. As a illustration of our approach we generate, annotate and evaluated 400 cases using large language models. Our study found that open-source LLMs identified routing issues with 79% accuracy, while proprietary reasoning models achieved reach upto 86%. The results demonstrate that LLM-based evaluation of VRP-generated routes can be an effective and scalable layer of evaluation which goes beyond beyond conventional distance and time based metrics. This has implications for improving cost efficiency, delivery reliability, and sustainability in last-mile logistics, especially for developing countries like India.
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