The refueling station location problem with routing (RSLP-R) is defined as a maximal coverage problem that locates alternative fuel refueling stations (AFSs) on a road network to maximize the refueled alternative fuel vehicle flows by considering the limited range of vehicles and the willingness of drivers to deviate from their paths for refueling. In this study, we introduce the robust counterpart of RSLP-R using a decision-dependent polyhedral uncertainty set. We model the flow uncertainty set using a hybrid model that comprises a hose model and individual flow bounds. To take into account the fact that vehicle flows are affected by AFS deployment decisions in their neighborhoods, we incorporate the decision-dependency notion into the flow uncertainty set. We propose two linear mixed integer programming formulations and a Benders reformulation. Our computational experiments on instances based on the road network of Belgium confirm the effectiveness of the reformulation in solving larger instances. We also report the results of experiments to assess the value of incorporating uncertainty and decision-dependency into the problem.

带路径的加油站定位问题 (RSLP-R) 被定义为最大覆盖问题，通过考虑车辆的有限范围和意愿，在道路网络上定位替代燃料加油站 (AFS) 以最大化加注替代燃料的车辆流量司机偏离他们加油的路径。在这项研究中，我们使用决策相关的多面体不确定性集介绍了 RSLP-R 的鲁棒对应物。我们使用包含软管模型和单个流量边界的混合模型对流量不确定性集进行建模。考虑到车辆流量受其邻域中 AFS 部署决策的影响，我们将决策依赖性概念纳入流量不确定性集中。我们提出了两个线性混合整数规划公式和一个 Benders 重构公式。我们基于比利时道路网络的实例计算实验证实了重新制定解决更大实例的有效性。我们还报告了实验结果，以评估将不确定性和决策依赖性纳入问题的价值。