Minimizing Total Travel Time for Collaborative Package Delivery with Heterogeneous Drones

2026-02-23

AI Summary

This study addresses the problem of cooperative package delivery using heterogeneous unmanned aerial vehicles (UAVs), with the objective of minimizing the total travel time across all UAVs. Focusing on non-preemptive scheduling scenarios, the problem is formulated as a tree-based combinatorial optimization model. The authors propose the first constant-factor approximation algorithm for this setting, leveraging the primal-dual method. Theoretical analysis establishes that the performance ratio between the optimal non-preemptive schedule and the optimal preemptive schedule is at most 3. Empirical evaluations demonstrate that the proposed algorithm achieves strong scalability and high-quality scheduling outcomes on both synthetic and real-world large-scale datasets.

Technology Category

• Planning, Routing, and Scheduling: Scheduling under Uncertainty
• Search and Optimization: Combinatorial Optimization
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: The sharing economy

Abstract

Given a fleet of drones with different speeds and a set of package delivery requests, the collaborative delivery problem asks for a schedule for the drones to collaboratively carry out all package deliveries, with the objective of minimizing the total travel time of all drones. We show that the best non-preemptive schedule (where a package that is picked up at its source is immediately delivered to its destination by one drone) is within a factor of three of the best preemptive schedule (where several drones can participate in the delivery of a single package). Then, we present a constant-factor approximation algorithm for the problem of computing the best non-preemptive schedule. The algorithm reduces the problem to a tree combination problem and uses a primal-dual approach to solve the latter. We have implemented a version of the algorithm optimized for practical efficiency and report the results of experiments on large-scale instances with synthetic and real-world data, demonstrating that our algorithm is scalable and delivers schedules of excellent quality.

Problem

• collaborative package delivery
• heterogeneous drones
• total travel time minimization
• non-preemptive scheduling
• drone fleet coordination

Innovation

• collaborative drone delivery
• non-preemptive scheduling
• constant-factor approximation
• primal-dual algorithm
• heterogeneous drones