Same-day delivery and on-demand delivery with driverless delivery robots (DDRs) are becoming new attractive options for more customers looking for grocery or medication delivery, as these delivery methods can customize time demand and meet consumers’ safety expectations. However, meeting these requirements for instant shipping necessarily increases the need for more vans and DDRs for last-mile delivery, thus increasing the economic and ecological costs. To optimize the economic costs and environmental effects of the delivery network, and also to meet customer satisfaction simultaneously, an effective model considering the new constraints of the van-DDR system and an efficient algorithm are needed to obtain the solutions. Therefore, the goals of this study are to establish a model and develop an algorithm for a multi-objective multi-depot two-tier location routing problem with parcel transshipment (MOMD-2T-LRP-PT), where vans and DDRs serve the two tiers, respectively. In this study, we split the MOMD-2T-LRP-PT model into two subproblems: the location-allocation problem and the vehicle routing problem. The two problems are solved sequentially and iteratively with a “k-prototype cluster” and a hybrid artificial immune algorithm (HAIA). We firstly illustrate the effectiveness of the MOMD-2T-LRP-PT model with the ∊-constraint method on a small-scale data set. Then the proposed HAIA algorithm is compared with a nondominated sorting genetic algorithm II (NSGA-II) using different data sets including a real case test. Both the analytic results and the real case application show that the ∊-constraint method can produce the best solution with up to six customers, and the HAIA algorithm produces better-optimized results than NSGA-II in real-life applications. These results imply that the MOMD-2T-LRP-PT model and the proposed HAIA algorithm are promising and effective in optimizing practical E-grocery delivery that can achieve optimization and balance among economic costs, environmental effects, and customer satisfaction.

使用无人驾驶送货机器人 (DDR) 的当日送货和按需送货正在成为更多寻求杂货或药品送货的客户的新选择，因为这些送货方式可以定制时间需求并满足消费者的安全期望。然而，满足即时运输的这些要求必然会增加最后一英里交付对更多货车和 DDR 的需求，从而增加经济和生态成本。为了优化配送网络的经济成本和环境效应，同时满足客户满意度，需要考虑van-DDR系统新约束的有效模型和高效算法来获得解决方案。所以，本研究的目标是为具有包裹转运的多目标多仓库两层位置路由问题 (MOMD-2T-LRP-PT) 建立模型并开发算法，其中货车和 DDR 为两层服务，分别。在本研究中，我们将 MOMD-2T-LRP-PT 模型拆分为两个子问题：位置分配问题和车辆路径问题。用“克-prototype cluster”和混合人工免疫算法（HAIA）。我们首先用 ∊ 约束方法在小规模数据集上说明了 MOMD-2T-LRP-PT 模型的有效性。然后将所提出的 HAIA 算法与使用不同数据集的非支配排序遗传算法 II (NSGA-II) 进行比较，包括真实案例测试。分析结果和实际案例应用都表明，∊约束方法可以产生最多六个客户的最佳解决方案，并且HAIA算法在实际应用中产生比NSGA-II更好的优化结果。这些结果意味着 MOMD-2T-LRP-PT 模型和所提出的 HAIA 算法在优化实际电子杂货交付方面具有前景和有效性，可以实现经济成本、环境影响和客户满意度之间的优化和平衡。