English

A Case Study of Vehicle Route Optimization

Neural and Evolutionary Computing 2022-01-13 v1

Abstract

In the last decades, the classical Vehicle Routing Problem (VRP), i.e., assigning a set of orders to vehicles and planning their routes has been intensively researched. As only the assignment of order to vehicles and their routes is already an NP-complete problem, the application of these algorithms in practice often fails to take into account the constraints and restrictions that apply in real-world applications, the so called rich VRP (rVRP) and are limited to single aspects. In this work, we incorporate the main relevant real-world constraints and requirements. We propose a two-stage strategy and a Timeline algorithm for time windows and pause times, and apply a Genetic Algorithm (GA) and Ant Colony Optimization (ACO) individually to the problem to find optimal solutions. Our evaluation of eight different problem instances against four state-of-the-art algorithms shows that our approach handles all given constraints in a reasonable time.

Keywords

Cite

@article{arxiv.2111.09087,
  title  = {A Case Study of Vehicle Route Optimization},
  author = {Veronika Lesch and Maximilian König and Samuel Kounev and Anthony Stein and Christian Krupitzer},
  journal= {arXiv preprint arXiv:2111.09087},
  year   = {2022}
}
R2 v1 2026-06-24T07:42:04.550Z