Stochastic dynamic vehicle routing problems have become an essential part of logistics and mobility services. In such problems, a sequence of vehicle routing decisions has to be made in reaction and anticipation of newly revealed stochastic information. To this end, a variety of computational operations research methods has emerged in the literature, increasingly integrating potential future information in their decision making. The integration of information models into decision models via computational methods is known as prescriptive analytics, the most recent advance of business analytics. In this paper, we explore the existing work and future potential of prescriptive analytics for stochastic dynamic vehicle routing. We identify the characteristics of decision models and information models unique in stochastic dynamic vehicle routing and analyze how different methodology meets the characteristics' requirements. We use the insights to derive recommendations about promising methodology when approaching specific stochastic dynamic vehicle routing problems.

随机动态车辆路径问题已成为物流和移动服务的重要组成部分。在此类问题中，必须做出一系列车辆路线决策以对新显示的随机信息做出反应和预期。为此，文献中出现了各种计算运筹学方法，越来越多地将潜在的未来信息整合到他们的决策中。通过计算方法将信息模型集成到决策模型中被称为规范分析，这是业务分析的最新进展。在本文中，我们探讨了随机动态车辆路线规划的规范分析的现有工作和未来潜力。我们确定了随机动态车辆路线规划中独特的决策模型和信息模型的特征，并分析了不同的方法如何满足特征的要求。在处理特定的随机动态车辆路径问题时，我们使用这些见解来推导出有关有前途的方法的建议。