Synchronized Deliveries with a Bike and a Self-Driving Robot

Zhao, Yanlu; Cattaruzza, Diego; Kang, Ningxuan; Roberti, Roberto

Abstract

Online e-commerce giants are continuously investigating innovative ways to improve their practices in last-mile deliveries. Inspired by the current practices at JD.com (the largest online retailer by revenue in China), we investigate a delivery problem that we call Traveling Salesman Problem with Bike-and-Robot (TSPBR) where a cargo bike is aided by a self-driving robot to deliver parcels to customers in urban areas. We present two mixed-integer linear programming models and describe a set of valid inequalities to strengthen their linear relaxation. We show that these models can yield optimal solutions of TSPBR instances with up to 60 nodes. To efficiently find heuristic solutions, we also present a genetic algorithm based on a dynamic programming recursion that efficiently explores large neighborhoods. We computationally assess this genetic algorithm on instances provided by JD.com and show that high-quality solutions can be found in a few minutes of computing time. Finally, we provide some managerial insights to assess the impact of deploying the bike-and-robot tandem to deliver parcels in the TSPBR setting.

Citation

Zhao, Y., Cattaruzza, D., Kang, N., & Roberti, R. (2023). Synchronized Deliveries with a Bike and a Self-Driving Robot. Transportation Science, https://doi.org/10.1287/trsc.2023.0169

• Journal Article Type: Article
• Acceptance Date: Oct 27, 2023
• Online Publication Date: Dec 8, 2023
• Publication Date: 2023
• Deposit Date: Nov 13, 2023
• Publicly Available Date: Dec 8, 2023
• Journal: Transportation Science
• Print ISSN: 0041-1655
• Electronic ISSN: 1526-5447
• Publisher: Institute for Operations Research and Management Sciences
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1287/trsc.2023.0169
• Public URL: https://durham-repository.worktribe.com/output/1925903