Arc-routing for winter road maintenance

Mixed-Integer Programming for the ROADEF/EURO 2020 challenge

In this paper, we present a novel approach for solving a maintenance scheduling problem from the French electricity grid company RTE, as presented in the ROADEF 2020 challenge. Our approach combines constraint generation with a new family of cutting planes to address the nonconvexity of the problem. We provide mathematical proofs and separation algorithms for the cutting planes and study the practical impact of our approach on the challenge instances. This method is applicable to a wide range of problems involving the modeling of uncertainty, and is shown to improve results significantly on re…

用于加速动态道路网络距离查询的稳定树标记

Stable Tree Labelling for Accelerating Distance Queries on Dynamic Road Networks

针对动态道路网络距离查询中标记规模大且难维护的问题，提出基于稳定树层次结构的2-hop标记方法STL及两种高效维护算法，显著提升了查询与更新性能并大幅降低了空间开销。

Distributed Processing of k Shortest Path Queries over Dynamic Road Networks

The problem of identifying the k-shortest paths (KSPs for short) in a dynamic road network is essential to many location-based services. Road networks are dynamic in the sense that the weights of the edges in the corresponding graph constantly change over time, representing evolving traffic conditions. Very often such services have to process numerous KSP queries over large road networks at the same time, thus there is a pressing need to identify distributed solutions for this problem. However, most existing approaches are designed to identify KSPs on a static graph in a sequential manner (i.e…

风景路线现在：有效地解决时间相关的弧形定向问题

Scenic Routes Now: Efficiently Solving the Time-Dependent Arc Orienteering Problem

本文将圆弧定向问题（AOP）推广到行程时间和价值收益均依赖于时间的大型道路网络，简称为两重时间依赖AOP或2 TD-AOP。在其原始定义中，NP难定向问题（OP）要求找到一条从源到目的地的路径，使累积价值最大化，同时不超过成本预算。OP和AOP的变体具有许多实际应用，例如移动众包任务（例如，修理和维护或派遣现场工人），各种物流问题（例如，人群控制或控制野火）以及若干游客引导问题（例如，生成旅行推荐或在主题公园中导航）。在所提出的2 TD-AOP中，旅行时间和价值函数被假定为时间相关的。例如，动态价值模型可以对众包任务中的不同奖励或损害控制任务中的不同紧急级别进行建模。我们讨论了这个新的问题，证明了时间依赖性的好处经验，并提出了一个有效的近似解决方案，快速响应系统的优化。我们的方法是第一个时间依赖的AOP的变体进行评估，在一个大规模的，细粒度的，真实世界的道路网络。我们证明了最优解是不可行的，静态问题的解往往是无效的。我们提出了一个近似的动态规划的解决方案，产生有效的路径，是数量级的速度比任何最佳的解决方案。