In this paper, we examine a vehicle routing problem with a makespan objective incorporating both stochastic and correlated travel times, which is usually not considered in routing problems. As an alternative to simulation, we develop an approach based on extreme-value theory to estimate the expected makespan (and standard deviation) and show how this approach can be embedded within an existing routing heuristic. We present results that demonstrate the impact of different correlation patterns and levels of correlation on route planning using real-world motivated instances. Depending on the particular objective, cost savings of up to 13.76% can be obtained by considering correlation.

在本文中，我们研究了具有随机和相关行驶时间的制造目标的车辆路径问题，这在路径问题中通常不予考虑。作为模拟的替代方法，我们开发了一种基于极值理论的方法来估计预期的制造时间（和标准偏差），并说明如何将该方法嵌入现有的路由启发式方法中。我们提供的结果证明了使用现实世界中的动机实例，不同的关联模式和关联级别对路线规划的影响。根据特定的目标，考虑相关性可以节省高达13.76％的成本。