Capacitated Vehicle Routing Problem using Ant Colony Optimization and PuLP Library

Routing determines the best path for resources or data to travel, essential in sectors like networks, telecommunications and transportation. Finding the most effective routes for a fleet of vehicles to provide goods or services while taking into account variables like vehicle capacity and speed is the goal of the Vehicle Routing Problem (VRP).Solving VRP optimizes resource use, reduces costs, and boosts operational efficiency. The VRP impacts delivery schedules, vehicle maintenance and driver workloads. Optimal VRP solutions enhance delivery reliability and timeliness, crucial for customer satisfaction and competitive advantage. The Capacitated Vehicle Routing Problem (CVRP) addresses real-world challenges by ensuring efficient load balancing and preventing overloading or underutilization. CVRP solutions improve fuel economy, reduce carbon emissions and create a more cost-effective, sustainable transportation system. In order to find the best route for a fleet of capacitated vehicles, we compare the PuLP library with Ant Colony Optimization (ACO) in this study. Our study aims to evaluate the efficiency of these two methodologies in solving the capacitated vehicle routing problem (CVRP), focusing on their performance in identifying the most efficient routes under given constraints. Based on the findings, the methodology that demonstrates superior performance will be implemented in the development of a user-facing system to optimize vehicle routing for practical applications.