An efficient branch-and-cut algorithm for the parallel drone scheduling traveling salesman problem

TL;DR

This paper introduces an efficient branch-and-cut algorithm that optimally solves the parallel drone scheduling traveling salesman problem, handling large instances and providing a benchmark for future research.

Contribution

The paper presents a novel exact algorithm for the problem and introduces new larger benchmark instances for future algorithmic testing.

Findings

Optimal solutions for most existing instances up to 229 customers.

Algorithm successfully solves large instances with up to 783 customers.

Metaheuristics tested show varying performance on new instances.

Abstract

We propose an efficient branch-and-cut algorithm to exactly solve the parallel drone scheduling traveling salesman problem. Our algorithm can find optimal solutions for all but two existing instances with up to 229 customers in a reasonable running time. To make the problem more challenging for future methods, we introduce two new sets of 120 larger instances with the number of customers varying from 318 to 783 and test our algorithm and investigate the performance of state-of-the-art metaheuristics on these instances.