A Case Study of Vehicle Route Optimization

TL;DR

This paper presents a comprehensive approach to vehicle route optimization that incorporates real-world constraints using a two-stage strategy, a Timeline algorithm, and metaheuristics like GA and ACO, demonstrating effectiveness across multiple instances.

Contribution

It introduces a novel two-stage strategy and Timeline algorithm for rich VRP, integrating constraints with metaheuristics to improve practical route planning solutions.

Findings

Handles all constraints efficiently

Outperforms four state-of-the-art algorithms

Effective on eight problem instances

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.