Crowdkeeping in Last-Mile Delivery

Abstract

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 storage options such as automated lockers, and this leads to a more efficient and a more profitable system for last-mile deliveries. We present a bilevel 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 data set from Amazon. The fixed-storage and no-storage systems are used as benchmarks to assess the performance of the crowdkeeping system. The results show that the crowdkeeping delivery system has the potential to generate higher profits because of its ability to consolidate deliveries and to eliminate failed deliveries. Funding: Funding provided by the Natural Sciences and Engineering Research Council of Canada [Grants 2022-04979 and 2022-05261], the Canada Research Chair program [Grant CRC-2018-00105], and the China Scholarship Council [Grant 202006190051] is gratefully acknowledged. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0323 .