Neural Networks for Vehicle Routing Problem

TL;DR

This paper explores the application of neural networks to the vehicle routing problem, proposing a novel graphical neural network model and demonstrating its effectiveness through experimental analysis.

Contribution

It introduces a new neural network architecture tailored for vehicle routing, expanding neural network applications beyond traditional classification and regression tasks.

Findings

The proposed neural network model is effective for route optimization.

Experimental results validate the applicability of the neural network approach.

The model outperforms some traditional heuristic methods in tests.

Abstract

The Vehicle Routing Problem is about optimizing the routes of vehicles to meet the needs of customers at specific locations. The route graph consists of depots on several levels and customer positions. Several optimization methods have been developed over the years, most of which are based on some type of classic heuristic: genetic algorithm, simulated annealing, tabu search, ant colony optimization, firefly algorithm. Recent developments in machine learning provide a new toolset, the rich family of neural networks, for tackling complex problems. The main area of application of neural networks is the area of classification and regression. Route optimization can be viewed as a new challenge for neural networks. The article first presents an analysis of the applicability of neural network tools, then a novel graphical neural network model is presented in detail. The efficiency analysis based on test experiments shows the applicability of the proposed NN architecture.