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STATUS: In Progress YEAR: 2026 TOPIC AREA: Connected and autonomous systems E-commerce and urban freight Vehicles and infrastructure CENTER: NCST

An Efficient Algorithm for Solving Collaborative Truck-Drone Parcel Delivery System Considering...

Project Summary


An Efficient Algorithm for Solving Collaborative Truck-Drone Parcel Delivery System Considering En-Route Launching and Recovery Points


Project Number: USC-DOT-1121
Funding Source: USDOT
Total Cost: $74,600
Performance Period: January 2026 - August 2026

Project Description:

The logistics industry faces significant challenges in keeping up with evolving demand and supply conditions, especially in urban areas. Traffic congestion during peak hours makes on-time delivery hard. Moreover, time-sensitive products, such as emergency blood and medicine, must be delivered to the customer at the desired time. Drones are a viable solution to urban logistics problems, as they offer several benefits for package delivery. Drones are resilient to traffic delays since they function independently of road infrastructure, unlike conventional vehicles. However, drones have capacity and other constraints; therefore, collaborating with a drone and a truck can make the delivery system more efficient. Although there has been significant research interest in developing truck-drone routing algorithms, a gap remains in developing models that allow for en-route drone launching points and recovery points. The prior research on truck-drone routing assumes that the truck can only reconnect with a drone at a customer location. This project will expand on the prior work to develop optimization models and algorithms to allow with en-route meet points. This added dimension has the potential to reduce truck vehicle miles and subsequently congestion. The solution framework will employ a dynamic programming-based algorithm for the initial solution and a synchronized drone dispatch algorithm to determine the launching and recovery points along the truck route. The proposed algorithm will be able to provide solutions for real-world large instances. 

P.I. NAME & ADDRESS

Maged Dessouky
Dean's Professor and Chair, Daniel J. Epstein Department of Industrial and Systems Engineering
3715 McClintock Ave.
Ethel Percy Andrus Gerontology Center (GER) 206ALos Angeles, CA 90089-0193
United States
[email protected]

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