Abstract The Static and Stochastic Vehicle Routing Problem with Random Requests (SS-VRP-R) describes realistic operational contexts in which a fleet of vehicles has to serve customer requests appearing dynamically. Based on a probabilistic knowledge about the appearance of requests, the SS-VRP-R seeks a priori sequences of vehicle relocations, optimizing the expected responsiveness to the requests. In this paper, an existing computational framework, based on recourse strategies, is adapted to meet the objectives of the SS-VRP-R. The resulting models are applied to a real case study of the management of police units in Brussels. In this context, the expected average response time is minimized. To cope with the reality of the urban context, a time-dependent variant is also studied (TD-SS-VRP-R) in which the travel time between two locations is a function that depends on the departure time at the first location. Experiments confirm the contribution and the adaptability of the recourse strategies to a real-life, complex operational context. Provided an adequate solution method, simulation-based results show the high quality of the a priori solutions designed, even when compared to those designed by field experts. Finally, the experiments provide evidence that there is no potential gain in considering time-dependency in such an operational context.

中文翻译：

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具有随机请求的时间相关随机车辆路由问题：在布鲁塞尔在线警察巡逻管理中的应用

摘要 带有随机请求的静态和随机车辆路由问题 (SS-VRP-R) 描述了现实的操作环境，其中车队必须为动态出现的客户请求提供服务。基于关于请求出现的概率知识，SS-VRP-R 寻找车辆重新定位的先验序列，优化对请求的预期响应。在本文中，基于资源策略的现有计算框架适用于满足 SS-VRP-R 的目标。所得模型应用于布鲁塞尔警察部队管理的真实案例研究。在这种情况下，预期的平均响应时间被最小化。为了应对城市环境的现实，还研究了与时间相关的变体 (TD-SS-VRP-R)，其中两个位置之间的旅行时间是一个取决于第一个位置的出发时间的函数。实验证实了追索策略对现实生活中复杂操作环境的贡献和适应性。提供适当的解决方案方法，基于仿真的结果显示设计的先验解决方案的高质量，即使与现场专家设计的解决方案相比也是如此。最后，实验提供的证据表明，在这种操作环境中考虑时间依赖性没有潜在的收益。基于仿真的结果表明，即使与现场专家设计的解决方案相比，所设计的先验解决方案也具有很高的质量。最后，实验提供的证据表明，在这种操作环境中考虑时间依赖性没有潜在的收益。基于仿真的结果表明，即使与现场专家设计的解决方案相比，所设计的先验解决方案也具有很高的质量。最后，实验提供的证据表明，在这种操作环境中考虑时间依赖性没有潜在的收益。