Travel Time Prediction in Supply Chain Management Using Machine Learning
Travel Time Prediction in Supply Chain Management Using Machine Learning
利用机器学习进行供应链管理中的运输时间预测
Abstract: The purpose of this research is to find data and methods using machine learning and deep learning to correctly predict the estimated travel time for transportation and logistics in a supply chain system. 摘要： 本研究旨在探索利用机器学习和深度学习的数据与方法，以准确预测供应链系统中运输与物流的预计行程时间。
The supply chain ecosystem is very complex and heavily relies on the transportation and logistics of raw materials and finished goods. Accurate travel time estimation is critical because it helps supply chain members to improve logistics consistency and performance. 供应链生态系统非常复杂，且高度依赖原材料和成品的运输与物流。准确的行程时间估算至关重要，因为它有助于供应链各方提高物流的一致性和绩效。
This helps in planning, demand forecasting, lead time management and assembly planning. The logistics on the delivery side of the customer also plays a crucial role in customer satisfaction and voice of customer. 这有助于规划、需求预测、提前期管理和装配计划。面向客户的交付端物流在客户满意度和客户反馈方面也起着至关重要的作用。
With the collection of huge historical data and using novel techniques, the research builds an accurate model to predict travel time of inventory. 通过收集海量历史数据并运用创新技术，本研究构建了一个能够准确预测库存运输时间的模型。
Paper Details:
- Authors: Balaji Venkateswaran
- Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
- arXiv ID: 2609.38190
- Submission Date: 5 Sep 2026
论文详情：
- 作者： Balaji Venkateswaran
- 学科： 机器学习 (cs.LG)；人工智能 (cs.AI)
- arXiv ID： 2609.38190
- 提交日期： 2026年9月5日