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Minimizing Total Travel Time for Collaborative Package Delivery with Heterogeneous Drones

Authors Thomas Erlebach , Kelin Luo , Wen Zhang

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LIPIcs.ESA.2026.17.pdf
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Subject Classification

ACM Subject Classification
  • Theory of computation → Design and analysis of algorithms
Keywords
  • Approximation algorithms
  • Primal-dual method
  • Heterogeneous pickup and delivery problem
  • Algorithm engineering

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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.

Cite As Get BibTex

Thomas Erlebach, Kelin Luo, and Wen Zhang. Minimizing Total Travel Time for Collaborative Package Delivery with Heterogeneous Drones. In 34th Annual European Symposium on Algorithms (ESA 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 388, pp. 17:1-17:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/LIPIcs.ESA.2026.17

Author Details

Thomas Erlebach
  • Department of Computer Science, Durham University, UK
Kelin Luo
  • Department of Computer Science and Engineering, University at Buffalo, NY, USA
Wen Zhang
  • Department of Computer Science and Engineering, University at Buffalo, NY, USA

Acknowledgements

This research was supported by data provided by Meituan.

Supplementary Materials

References

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