An Enhanced Scheduling Approach to Minimize the Vehicles Deployed in an EVRP Scenario Considering Geographical Distribution

Abstract

Electric vehicle routing was at the center of interest for the past decades due to its importance to the environment first, and to the economy second. Several methods were attempted to tackle this dilemma, but it proved difficult and required extensive efforts. In this paper, we propose a 2 stages approach to schedule vehicles using DBSCAN (Density-Based Spatial Clustering of Applications with Noise) followed by genetic algorithm, with the purpose of minimizing the number of vehicles deployed in an EVRP (Electric Vehicle Routing Problem) scenario. By diving the problem into smaller problems, we are aspiring to alleviate some of the problem difficulties. Upon running simulations on Solomon's datasets, the approach proves effective in optimally making the most of an heterogenous fleet while aiming for an optimal cost.