Department of Economics and Business Economics

Seeing the huge success of sharing platforms such as Uber, Lyft, and Airbnb, where owners of under-used assets are connected with users willing to pay for the use of these assets, it is not surprising that retailers aim to transfer the basic idea of the sharing economy to their last-mile deliveries. In crowdshipping, the under-used assets are transport capacities of private drivers and the users are the retailers aiming for additional and cost-efficient delivery capacities for their home deliveries. A major drawback of crowdshipping is that retailers can hardly guarantee their promised delivery services when subcontracting individuals. To avoid this problem, different retailers are establishing crowdshipping platforms offering a reward to the employees of their distribution centers for crowdshipping online orders on their way back from work. We investigate the resulting optimization problem for matching crowdshipping supply and demand in this context. We present an efficient exact solution procedure based on Benders decomposition, which maximizes the number of matched shipments while considering the employees’ minimum expected earnings per time unit. This procedure is shown to solve instances of real-world size before a work shift is over and the shipments have to be loaded into the trunks of the employees’ cars. Furthermore, we show the impact of crowdshipping on all main stakeholders and identify critical success factors.