Exact and Heuristic Approaches to Drone Delivery Problems

TL;DR

This paper introduces a new MIP formulation and a hybrid heuristic for the Flying Sidekick Traveling Salesman Problem, significantly improving solution quality and solving previously unsolved instances in drone delivery routing.

Contribution

It presents a novel MIP model with better bounds and a hybrid heuristic combining Tabu Search for large instances, advancing drone delivery optimization methods.

Findings

MIP formulation outperforms previous models in bounds

Capable of solving several previously unsolved instances

Improved over 80% of best known solutions on benchmark data

Abstract

The Flying Sidekick Traveling Salesman Problem (FSTSP) considers a delivery system composed by a truck and a drone. The drone launches from the truck with a single package to deliver to a customer. Each drone must return to the truck to recharge batteries, pick up another package, and launch again to a new customer location. This work proposes a novel Mixed Integer Programming (MIP) formulation and a heuristic approach to address the problem. The proposedMIP formulation yields better linear relaxation bounds than previously proposed formulations for all instances, and was capable of optimally solving several unsolved instances from the literature. A hybrid heuristic based on the General Variable Neighborhood Search metaheuristic combining Tabu Search concepts is employed to obtain high-quality solutions for large-size instances. The efficiency of the algorithm was evaluated on 1415 benchmark instances from the literature, and over 80% of the best known solutions were improved.