## 2 Related Works  
A significant amount of research has been conducted for solving VRP. In many classical approaches, the focus is on vehicle path planning and directing vehicles to their destination as soon as possible with the consideration of static conditions, mostly considering distance. Early approaches, like the use of [PERSON]'s algorithm [7], focused on finding the shortest path between nodes in a static environment, lacking consideration for factors such as congestion and speed limits. However, those methods became inadequate as modern transportation networks rapidly developed. The traditional objective of the classical VRP was to minimize a cost function [3], which was considered to be the total distance traveled by all vehicles. Common applications like Google Map and Waze [8] also emerged, aiming to find the shortest path between nodes. Yet, their reliance on human input makes them prone to biases and inaccuracies. In response to these challenges, a Distributed Intelligent Traffic System (DITS) based on ant colony optimization was introduced [9]. Inspired by natural ant behavior in finding food resources, this approach ought to enhance traffic conditions. The number of VRPs solution methods introduced in the academic literature has grown rapidly over the past few decades. Several notable approaches have been extensively reviewed in the literature as highlighted by [10; 11; 12; 13]; these reviews provide valuable insights into commonly employed methodologies. [PERSON] and [PERSON] [14] introduced an early approach to address the Urban Transit Routing Problem (UTRP), utilizing a particle swarm optimization algorithm to optimize route networks while balancing service quality for passengers and operator costs. Their work demonstrated competitive results using [PERSON]'s benchmark problem. According to [15], the study of VRPs is growing fast in operations research, increasing by 6% each year. This rapid growth creates a problem for both researchers and practitioners because it is hard to keep up with the latest developments. The large amount of literature makes it difficult to fully understand the newest types of VRPs and how to solve them. Hence, there is a significant demand to systematically organize and summarize information to gain a clearer understanding of the field's progression. This need arises from the fact that real-world problems involve complex constraints, and advanced algorithms are required to solve VRPs in complicated and constantly changing environments. The evolution of machine learning (ML) has prompted researchers to investigate Artificial Intelligence-based (AI-based) models for tackling issues related to traffic congestion [16]. To effectively address the challenges posed by VRPs, [PERSON] et al. [17] conducted a thorough review of hybrid methods that combine analytical techniques with ML tools. The use of cutting-edge ML and DL algorithms to solve dynamic VRP is highlighted in [18]. The goal of this research is to continuously improve VRP solving by applying cutting-edge ML and DL algorithms. Conversely, [PERSON] and [PERSON] [19] provide a comprehensive review of the Green Vehicle Routing Problem (GVRP). This variant of VRP aims to minimize greenhouse gas emissions in vehicle routing by considering Alternative Fuel Vehicles (AFVs) alongside conventional fossil fuel vehicles in fleets. The overarching goal is to achieve environmental sustainability in transportation and logistics. Furthermore, [PERSON] et al. [20] explore the vehicle routing problem as a discrete combinatorial optimization challenge. This study highlights the existing models and algorithms designed for resolving VRPs. Notably, the exploration extends to Learning-Based Optimization (LBO) algorithms as potential solutions for addressing the complexities of VRPs. The increasing popularity of DL and RL techniques marks a departure from traditional navigation methods. These traditional approaches heavily depend on human input and tend to overlook contemporary infrastructure limitations.