Integrating Metaheuristics and Machine Learning for Enhanced Vehicle Routing: A Comparative Study of Hyperheuristic and VAE-Based Approaches

Abstract

This study addresses the optimization of the Vehicle Routing Problem (VRP) with prioritized customers by introducing and comparing two advanced solution approaches: a metaheuristic-based hyperheuristic framework and a Variational Autoencoder (VAE)-based hyperheuristic. The VRP with prioritized customers introduces additional complexity by requiring efficient routing while ensuring high-priority customers receive service within strict constraints. To tackle this challenge, the proposed metaheuristic-based hyperheuristic dynamically selects and adapts low-level heuristics using Simulated Annealing (SA) and Ant Colony Optimization (ACO), enhancing search efficiency and solution quality. In contrast, the VAE-based approach leverages deep learning to model historical routing patterns and autonomously generate new heuristics tailored to problem-specific characteristics. Through extensive computational experiments on benchmark VRP instances, our results reveal that both approaches significantly enhance routing efficiency, with the VAE-based method demonstrating superior generalization across varying problem structures. Specifically, the VAE-based approach reduces total travel costs by an average of (Formula presented.) and improves customer priority satisfaction by (Formula presented.) compared to traditional hyperheuristic methods. Moreover, a comparative analysis with recent state-of-the-art algorithms highlights the competitive performance of our approaches in balancing computational efficiency and solution quality. These findings underscore the potential of integrating metaheuristics with machine learning in complex routing problems and provide valuable insights for real-world logistics and transportation planning. Future research will explore the generalization of these methodologies to dynamic and large-scale routing scenarios.