Some of today's most significant challenges in urban environments concern individual mobility and rapid parcel delivery. With the surge of e-commerce and the ever-increasing volume of goods to be handled, new logistic solutions are in high demand. The share-a-ride problem (SARP) was proposed as one such solution, combining people and parcel transportation in taxis. This is an NP-hard problem and thus obtaining optimal solutions can be computationally costly. In this paper, we work with a variation of SARP for ride-hailing systems, which can be formulated as a multi-depot open generalised vehicle routing problem with time windows. We present and solve a mixed-integer linear programming (MILP) formulation for this problem that bundles requests together, and we compare its results to a previously proposed two-stage method. The latter solves the so-called freight insertion problem (FIP) in the second stage, for which we consider two versions, and the problem consists of inserting parcels into predefined passenger routes obtained in the first stage. We tested the methods in three sets of instances. The developed bundle-based approach outperformed both FIP versions in solution quality and in the service of parcels. Our method also compares favourably when it comes to reducing the amount of deadheading distance.

翻译：当今城市环境中最重大的挑战之一涉及个人出行与快速包裹配送。随着电子商务的蓬勃发展以及需处理货物量的持续增长，新型物流解决方案的需求日益迫切。共享乘车问题（SARP）作为此类解决方案被提出，它将出租车中的人员运输与包裹运输相结合。这是一个NP难问题，因此获得最优解的计算成本可能很高。本文针对网约车系统中的SARP变体展开研究，该变体可建模为带时间窗的多车场开放式广义车辆路径问题。我们提出并求解了该问题的混合整数线性规划（MILP）模型，该模型对请求进行捆绑处理，并将其结果与先前提出的两阶段方法进行对比。后者在第二阶段求解所谓的货运插入问题（FIP），我们考虑了该问题的两种版本，其任务是将包裹插入第一阶段预定义的乘客路径中。我们在三组实例上测试了这些方法。所开发的基于捆绑的方法在解的质量和包裹服务率方面均优于两种FIP版本。我们的方法在减少空驶距离方面同样具有优势。