An Efficient Ant Colony Algorithm for Multi-Depot Heterogeneous Fleet Green Vehicle Routing Problem

Vehicle routing problem (VRP) is one of the most widely researched topics in the fields of transportation, distribution and logistics, mostly because of its capabilities for potential cost savings and improved service performance leading to better customer satisfaction. Nowadays, the rising concerns about global warming have forced companies to reduce their carbon emissions. In this paper, a framework for multi-objective multi-depot heterogeneous fleet green vehicle routing problem (MDHFGVRP) has been developed. The model maximizes revenue and minimizes costs, time and carbon emissions considering heterogeneous fleet. The heterogeneous fleet consists of different types of vehicles available to each depot. An efficient ant colony optimization algorithm (EACO), a population-based metaheuristic, has been applied to solve the problem. The EACO model is inspired by ant’s behaviors in nature. The proposed EACO model uses a novel approach of applying k-NN Classification with traditional ant colony optimization (ACO), which ensures more efficient solutions with better accuracy. The results obtained through the proposed EACO shows better performance than the traditional methods existing in the literature and provides improved solution quality. Therefore, improved responsiveness and simplicity are achieved through the application of EACO algorithm for solving the MDHFGVRP problem.