Crowd-shipping systems with public transport passengers: Operational planning

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. © 2024 The Author(s)