## 1 Introduction  
Vehicle Routing Problems (VRPs) are about finding efficient delivery routes for a set of vehicles. The goal is to minimize travel times while dealing with constraints such as vehicle capacity and delivery time windows. The VRP family of problems is faced in real-world situations daily, such as with food and last-mile delivery. Moreover, novel and varied problem instances must be considered with changing road availability at different hours of the day. Metaheuristics are able to approximate the optimal solution to intractable problems such as VRPs [1]. To solve an instance of a VRP using a metaheuristic algorithm, it is first necessary to define the parameter values with which the algorithm will run. These parameters have a crucial effect on the quality of the algorithm's solutions. Moreover, no unique parameter configuration works well for all possible problem instances. Consequently, finding a quality configuration is a difficult and time-consuming task that will be repeatedly executed when new or unseen routing instances are being solved.  
Machine learning algorithms have been applied to design, complement, and enhance metaheuristic algorithms [2]. In this work, we propose a methodology to use machine learning (ML) algorithms to predict suitable parameter configurations for metaheuristic algorithms solving unseen instances. The objective of this work is to avoid the need to execute parameter-tuning processes every time a new problem instance is solved. For this, the ML algorithms will be trained with parameter values obtained by the well-known ParamILS tuner [3] and algorithmically extracted instance features. Here, we analyze the Capacitated VRP with Time Windows (CVRPTW) as a case study. We perform a clustering-based feature extraction focused on understanding client distribution characteristics using a set of benchmark instances. Moreover, we use a state-of-the-art metaheuristic to solve hard problem instances of the CVRPTW named Hybrid Genetic Search [4]. We evaluate the application of k-nearest neighbours and Artificial Neural Networks for the feature-based prediction of high-quality parameter configurations. To analyze which CVRPTW features are most relevant to the prediction of parameter configurations, we present an analysis using Explainable AI methods. The main contributions of this work are:  
* The proposal of a methodology that uses a machine learning approach to suggest suitable parameter values for unseen instances.
* The definition of a set of clustering-based CVRPTW features to describe instances.
* The assessment of our proposal applied to the CVRPTW using current state-of-the-art algorithms.
* The analysis of the most relevant CVRPTW features in the prediction step using Explainable AI techniques.  
It is important to mention that the objective of our work is not to propose a new machine learning technique. Rather, the main idea is to propose and evaluate a simple and novel methodology to predict suitable parameter values for unseen instances of a vehicle routing problem. The structure of the article is defined as follows: Section 2 presents details of the CVRPTW and previous work in the literature where machine learning techniques are used for metaheuristic algorithms. Section 3 describes our methodology to predict suitable parameter values using ML. Section 4 details the experimental setup applied in our study. Results are discussed in Section 5. Our main conclusions and paths for future work are presented in Section 6.