Matching Models for Crowd-Shipping Considering Shipper’s Acceptance Uncertainty

Crowd-shipping systems, which use occasional drivers to deliver parcels with compensations, offer greater flexibility and cost-effectiveness than the conventional company-owned vehicle shipping system. This paper investigates a dynamic crowd-shipping system that uses in-store customers as crowd-shippers to deliver online orders on their way home under the condition that the crowd-shippers’ acceptances are uncertain. Optimal matching results between online orders and crowd-shippers and optimal compensation schemes should be determined to minimize the total costs of the crowd-shipping system. To this aim, we formulate this problem as a two-stage optimization model that determines matching results and compensation schemes sequentially. To evaluate the proposed optimization model, we conduct a series of computational experiments. Results show that the average delivery cost is reduced by 7.30 %, compared to the conventional shipping system.