AI 中文总结
研究带时间窗的时间依赖车辆路径问题，通过将单车变体进展用于多车设置，重新实现分支定价算法，并探索整合数据挖掘和机器学习引导列生成，以开发高性能可解释求解器，推进该问题精确求解技术水平。
AI 中文摘要
在城市环境中，出行时间随时间和交通状况大幅变化。带时间窗的时间依赖车辆路径问题（TDVRPTW）扩展了经典VRPTW，使出行时间依赖出发时间，目标是在严格时间窗内为最多k条服务客户的车辆路径最小化总出行时间。目前该现实但研究不足问题的精确求解依赖Dabia等人（2013）的分支定价法，其中列生成方案将问题分解为集合划分主问题和用动态规划求解的资源受限最短路径定价问题。本研究在MAMUT项目中进行，旨在推进TDVRPTW精确且可解释求解的技术水平。我们提议通过将单车变体（TDTSPTW）的最新进展转移到多车设置来重新实现并审视开创性的分支定价算法，特别是A*的精确随时扩展和用于定价子问题的动态规划引导的大邻域搜索。我们还探索整合数据挖掘和机器学习，通过利用先前计算路径的知识来引导列生成。目标是开发一个高性能且可解释的求解器，满足现实世界城市交通需求同时保证解质量。
英文摘要
In urban contexts, travel times vary strongly with the time of day and traffic conditions. The Time-Dependent Vehicle Routing Problem with Time Windows (TDVRPTW) extends the classical VRPTW by making travel times depend on departure time, with the objective of minimizing the total travel time of at most $k$ vehicle routes serving customers within strict time windows. Exact resolution of this realistic yet understudied problem currently relies on the Branch \& Price approach of Dabia et al. (2013), in which a Column Generation scheme decomposes the problem into a set-partitioning master problem and a resource-constrained shortest-path pricing problem solved by dynamic programming. This work, conducted within the MAMUT project (Machine Learning and Matheuristics for Urban Transport), aims to advance the state of the art for the exact and explainable resolution of the TDVRPTW. We propose to reimplement and revisit the pioneering Branch \& Price algorithm by transferring recent advances made on the single-vehicle variant (TDTSPTW) to the multi-vehicle setting, notably an exact anytime extension of A* and a Large Neighborhood Search guided by dynamic programming for the pricing sub-problem. We further explore the integration of data mining and machine learning to guide column generation by exploiting knowledge from previously computed routes. The goal is a high-performance and interpretable solver that meets the requirements of real-world urban transport while preserving guarantees on solution quality.
Journal ref26{è}me congr{è}s annuel de la Soci{é}t{é} Fran{\c c}aise de Recherche Op{é}rationnelle et d'Aide {à} la D{é}cision, {É}cole nationale des ponts et chauss{é}es; CERMICS; LVMT; Institut Polytechnique de Paris, Feb 2025, Champs Sur Marne, France. pp.731
