Abstract Operations research requires models that unambiguously define problems and support the generation and presentation of solution methodology. In the field of dynamic routing, capturing the joint evolution of complex sequential routing decisions and stochastic information is challenging, leading to a situation where rigorous methods have outpaced rigorous models and thus making it difficult for researchers to engage in rigorous science. We provide a modeling framework that strongly connects application with method and that leverages the rich body of route-based planning and optimization. As a generalization of conventional Markov decision processes (MDPs), route-based MDPs augment the state space, action space, and reward structure to include routing information. Accordingly, route-based MDPs make it conceptually easier to connect dynamic routing problems with the route-based methods typically used to solve them – construct and revise routes as new information is learned. We anticipate route-based MDPs will facilitate more scientific rigor in dynamic routing studies, provide researchers with a common modeling language, allow for better inquiry, and improve classification and description of solution methods.

中文翻译：

---

关于建模随机动态车辆路径问题

摘要运筹学需要能够明确定义问题并支持解决方案方法的生成和表示的模型。在动态路由领域，捕获复杂的顺序路由决策和随机信息的联合进化是具有挑战性的，从而导致一种情况，即严格的方法超过了严格的模型，从而使研究人员难以从事严格的科学。我们提供了一个建模框架，该框架将应用程序与方法紧密地联系在一起，并充分利用了基于路线的计划和优化的丰富内容。作为常规马尔可夫决策过程（MDP）的概括，基于路由的MDP扩展了状态空间，操作空间和奖励结构，以包括路由信息。因此，基于路由的MDP在概念上使动态路由问题与通常用于解决问题的基于路由的方法联系起来更加容易-在学习新信息时构造和修改路由。我们预计基于路线的MDP将促进动态路线研究中更加科学的严谨性，为研究人员提供通用的建模语言，允许更好的查询，并改善求解方法的分类和描述。