Study of Robust Features in Formulating Guidance for Heuristic Algorithms for Solving the Vehicle Routing Problem

TL;DR

This paper investigates the use of explainable AI to identify robust features that can guide heuristic algorithms in solving the Vehicle Routing Problem more effectively.

Contribution

It introduces a unified framework for ranking feature importance across scenarios, enhancing the understanding of feature impacts in VRP solution quality prediction.

Findings

Certain features are consistently strong predictors of solution quality.

Feature importance varies across different scenarios.

A unified ranking framework was successfully developed.

Abstract

The Vehicle Routing Problem (VRP) is a complex optimization problem with numerous real-world applications, mostly solved using metaheuristic algorithms due to its $\mathcal{NP}$-Hard nature. Traditionally, these metaheuristics rely on human-crafted designs developed through empirical studies. However, recent research shows that machine learning methods can be used the structural characteristics of solutions in combinatorial optimization, thereby aiding in designing more efficient algorithms, particularly for solving VRP. Building on this advancement, this study extends the previous research by conducting a sensitivity analysis using multiple classifier models that are capable of predicting the quality of VRP solutions. Hence, by leveraging explainable AI, this research is able to extend the understanding of how these models make decisions. Finally, our findings indicate that while feature importance varies, certain features consistently emerge as strong predictors. Furthermore, we propose a unified framework able of ranking feature impact across different scenarios to illustrate this finding. These insights highlight the potential of feature importance analysis as a foundation for developing a guidance mechanism of metaheuristic algorithms for solving the VRP.