Optimizing an On-Demand Delivery Mode Based on Trucks and Drones

Lu Zhen (), Jiajing Gao (), Shuaian Wang (), Gilbert Laporte () and Xiaohang Yue ()
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Lu Zhen: School of Management, Shanghai University, Shanghai 200444, China
Jiajing Gao: School of Management, Shanghai University, Shanghai 200444, China
Shuaian Wang: Faculty of Business, The Hong Kong Polytechnic University, Kowloon, Hong Kong
Gilbert Laporte: Department of Decision Sciences, HEC Montréal, Montréal, Quebec H3T 2A7, Canada; and School of Management, University of Bath, BA2 7AY Bath, United Kingdom
Xiaohang Yue: Lubar School of Business, University of Wisconsin–Milwaukee, Milwaukee, Wisconsin 53201

Transportation Science, 2025, vol. 59, issue 5, 1008-1031

Abstract: We explore a novel on-demand delivery mode based on cooperation between trucks and drones. A fleet of trucks, each of which carries several drones, travels along a closed-loop route, and the drones are launched from the trucks to pick up (or deliver) ordered parcels from their origin (or to their destination). The fulfillment of an order (i.e., delivering the parcel from its origin to its destination) includes three steps: pick up by a drone, transport by a truck, and delivery by a drone. We investigate how to fulfill all of the orders in one batch in order to minimize the total operational cost. We build a mixed-integer programming (MIP) model for this new on-demand delivery system in a network of multiple routes with transshipment. For drones, the assignment decision regarding the fulfillment stages for the orders and the location decision regarding the launching from and landing onto trucks are optimized by the proposed MIP model. An exact branch-and-price algorithm is designed to efficiently solve the model on large-scale instances. We validate the advantages of our algorithm in terms of computing time and solution quality through experiments on both artificial and real data. We validate the benefits of both implementing this new delivery mode and allowing transshipments among routes using a drone to serve multiple orders in one flying trip and consolidating orders. We also investigate the influences of the number of drones, speed, endurance time, unit penalty cost, and the geographic distribution of orders on the system’s operational cost.

Keywords: cooperative delivery; on demand; trucks and drones; sidekicks; branch-and-price (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ortrsc:v:59:y:2025:i:5:p:1008-1031

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