Matheuristic algorithms for the parallel drone scheduling traveling salesman problem

Mauro Dell’Amico, Roberto Montemanni, Stefano Novellani

Research output: Contribution to journalArticlepeer-review

57 Scopus citations

Abstract

In a near future drones are likely to become a viable way of distributing parcels in a urban environment. In this paper we consider the parallel drone scheduling traveling salesman problem, where a set of customers requiring a delivery is split between a truck and a fleet of drones, with the aim of minimizing the total time required to service all the customers. We present a set of matheuristic methods for the problem. The new approaches are validated via an experimental campaign on two sets of benchmarks available in the literature. It is shown that the approaches we propose perform very well on small/medium size instances. Solving a mixed integer linear programming model to optimality leads to the first optimality proof for all the instances with 20 customers considered, while the heuristics are shown to be fast and effective on the same dataset. When considering larger instances with 48 to 229 customers, the results are competitive with state-of-the-art methods and lead to 28 new best known solutions out of the 90 instances considered.

Original languageEnglish
Pages (from-to)211-226
Number of pages16
JournalAnnals of Operations Research
Volume289
Issue number2
DOIs
StatePublished - 1 Jun 2020
Externally publishedYes

Keywords

  • Drone-assisted deliveries
  • Heuristic algorithms
  • Matheuristics
  • Mixed integer linear programming
  • Traveling salesman problem

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