This study designs a crowdshipping (CS) delivery system with public transport (PT) passengers at the operational decision-making level. In this system, parcel lockers (PLs) are positioned in PT stations, through which small and light parcels are allocated to passengers for delivery to their final delivery addresses (i.e., performing the last-mile delivery). A probabilistic mathematical model is formulated with behavioural constraints to estimate the probabilities of accepting CS tasks by passengers. The probability is estimated based on a logit function, sensitive to the parcel’s weight, reimbursement amount, and the walking detour required to deliver the parcel to its final destination. The logit model is constructed based on survey data collected from the Greater Sydney (GS) area, Australia. The mathematical model optimises the allocation of delivery tasks to the CS system and PLs, subsequently, incentivising CS-allocated tasks for participating passengers. Furthermore, the model performs the routing of vehicles to deliver non-allocated parcels, including heavy parcels. A heuristic solution algorithm is then proposed to optimise decisions related to allocation, routing, and incentivisation, which was tested on a real case study. By conducting sensitivity analysis on various model parameters, results show that for a small carrier, utilising a PT-based CS system could minimise daily delivery costs by up to 36%, depending on passengers’ rate of familiarity with the CS initiative and the number of PT stations equipped with PLs. Vehicle delivery cost in the CS-integrated delivery system is also reduced between 50% and 65%, in comparison to the conventional vehicle-only system. Our study reveals that a CS system should offer higher incentives at the beginning, and as CS familiarity grows, figures could be reduced depending on other market and operational conditions. Furthermore, simulated experiments suggest that denser PL networks enable carriers to reduce incentives even at earlier stages with lower familiarity rates.

中文翻译：

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公共交通乘客的众运系统：运营规划


本研究设计了一个众包运输 （CS） 交付系统，公共交通 （PT） 乘客处于运营决策层面。在该系统中，包裹储物柜 （PL） 位于 PT 站点，通过该储物柜将小型和轻型包裹分配给乘客，以便运送到他们的最终送货地址（即执行最后一英里交付）。使用行为约束制定一个概率数学模型，以估计乘客接受 CS 任务的概率。概率是根据 logit 函数估计的，该函数对包裹的重量、报销金额以及将包裹运送到最终目的地所需的步行绕道很敏感。Logit 模型是根据从澳大利亚大悉尼 （GS） 地区收集的调查数据构建的。该数学模型优化了向 CS 系统和 PL 分配交付任务，随后激励 CS 为参与乘客分配任务。此外，该模型还执行车辆的路线规划，以递送未分配的包裹，包括重型包裹。然后提出了一种启发式解决方案算法来优化与分配、路由和激励相关的决策，并在真实案例研究中进行了测试。通过对各种模型参数进行敏感性分析，结果表明，对于小型承运人来说，使用基于 PT 的 CS 系统可以将日常交付成本降低多达 36%，具体取决于乘客对 CS 计划的熟悉程度和配备 PL 的 PT 站的数量。CS 集成交付系统中的车辆交付成本也降低了 50% 至 65%。 与传统的纯车辆系统相比。 我们的研究表明，CS 系统在开始时应该提供更高的激励措施，随着 CS 熟悉度的提高，数字可能会根据其他市场和运营条件而减少。此外，模拟实验表明，更密集的 PL 网络使运营商能够在熟悉率较低的早期阶段减少激励。