We consider a service network design problem for the tactical planning of parcel delivery in a city logistics setting. A logistics service provider seeks a repeatable plan to transport commodities from distribution centers on the periphery to inner-city satellites. In a heterogeneous infrastructure, autonomous vehicles in level 4 may only drive in feasible streets but need to be pulled elsewhere by manually operated vehicles in platoons. We formulate an integer program to determine the fleet mix, schedule transportation services, and decide on the routing or outsourcing of commodities. Platooning requires a high level of synchronization between vehicles which demands the time-expanded networks to contain narrow time intervals. Thus, we develop an algorithm based on the dynamic discretization discovery scheme which refines partially time-expanded networks iteratively without having to enumerate the fully time-expanded network a priori. We introduce valid inequalities and provide two enhanced versions of the algorithm that exploit linear relaxations of the problem. Further, we propose heuristic ideas to speed up the search for high-quality solutions. In a computational study, we analyze the efficacy of the algorithm in different versions and observe improvements of computational performance in comparison to a commercial solver. Finally, we solve a case study on a real-world based network to obtain insights into the deployment of a mixed autonomous fleet in an existing heterogeneous infrastructure.

我们在城市物流环境中考虑包裹运输战术规划的服务网络设计问题。物流服务提供商寻求可重复的计划，以将商品从外围的配送中心运输到城市内的卫星。在异构基础设施中，第4层的自动驾驶汽车只能在可行的街道上行驶，但需要由排中的手动驾驶车辆拖到其他地方。我们制定了一个整数程序来确定车队组合，安排运输服务并决定商品的路线安排或外包。排要求车辆之间的高度同步，这要求时间扩展网络包含狭窄的时间间隔。从而，我们开发了一种基于动态离散化发现方案的算法，该算法可以迭代地细化部分时间扩展的网络，而不必先验地枚举完全时间扩展的网络。我们引入了有效的不等式，并提供了利用该问题的线性松弛的算法的两个增强版本。此外，我们提出启发式的想法以加快对高质量解决方案的搜索。在计算研究中，我们分析了该算法在不同版本中的功效，并观察了与商用求解器相比计算性能的提高。最后，我们在一个基于现实世界的网络上解决了一个案例研究，以了解在现有异构基础架构中混合自主舰队的部署情况。