语言模型可以用于现实世界的城市交付路线优化吗?
Yang Liu1, Fanyou Wu1, Zhiyuan Liu2
1State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University, Beijing 100084, China.
Innovation (Cambridge (Mass.))
|October 23, 2023
概括
语言模型现在可以通过从司机的历史经验和隐含行为中学习来优化交付路线. 这种新的方法将人类驾驶模式与优化模型相结合,以提高效率.
科学领域:
- 人工智能的人工智能
- 运营研究 运营研究
- 物流和供应链管理的物流和供应链管理.
背景情况:
- 语言模型 (LMs) 已经进行了先进的跨学科研究,包括蛋白质设计和分子动力学.
- 现有的交付路线优化方法往往无法捕捉复杂操作环境的隐含知识.
- 人类行为,当可映射到序列时,可以被LM学习,提供新的应用途径.
研究的目的:
- 提出一种新的方法,使用语言模型,根据司机的历史经验优化交付路线.
- 通过从经验丰富的驾驶员行为中学习,将交付环境的隐性知识纳入路线优化中.
- 展示语言模型和运营研究的融合,以改善交付物流.
主要方法:
- 模拟现实世界的交付路线到自然语言句子,以利用LM功能.
- 使用无监督学习来导出驾驶行为的矢量表示和推断交付链.
- 开发了一个混合框架,将LMs用于区域间交付推断和旅行销售员问题 (TSP) 用于区域内优化.
主要成果:
- 提出的基于LM的方法在数值实验中显著超过纯优化方法.
- 亚马逊配送服务的现实数据验证了该模型的有效性,效率和可扩展性.
- 该模型成功地学习并保存了司机在优化交付路线中的隐性行为模式.
结论:
- 语言模型可以有效地学习和优化复杂的现实世界的顺序任务,超出传统的语言处理.
- 混合框架为优化交付路线提供了一种多功能解决方案,集成隐含的人类知识.
- 这种方法作为一种基础,可以将LM应用到使用顺序数据的多样化跨学科问题的基础.
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