Crowdkeeping in Last-mile Delivery

In order to improve the efficiency of the last-mile delivery system when customers are possibly absent for deliveries, we propose the idea of employing the crowd to work as keepers and to provide storage services for their neighbors. Crowd-keepers have extra flexibility, more availability, and lower costs than fixed-storages such as automated lockers, and this leads to a more efficient and a more profitable system for last-mile deliveries. We present a bi-level program that jointly determines the assignment, routing, and pricing decisions while considering customer preferences, keeper behaviors, and platform operations. We develop an equivalent single-level program, a mixed-integer linear program with subtour elimination constraints, that can be solved to optimality using a row generation algorithm. To improve the efficiency of the solution procedure, we further derive exact best response sets for both customers and keepers, and approximate optimal travel times using linear regression. We present a numerical study using a real-world dataset from Amazon. The fixed-storage and the no-storage systems are used as benchmarks to assess the performance of crowdkeeping system. The results show that the crowdkeeping delivery system has the potential to generate higher profits due to its ability to consolidate deliveries and to eliminate failed deliveries.