Comparative Analysis of Ant Colony Optimization and Google OR-Tools for Solving the Open Capacitated Vehicle Routing Problem in Logistics
Assem Omar, Youssef Omar, Marwa Solayman, Hesham Mansour
TL;DR
This paper compares Ant Colony Optimization (ACO) and Google OR-Tools for solving the Open Capacitated Vehicle Routing Problem (OCVRP) using a common Python-based implementation and a real-world dataset from Greater Cairo. It evaluates routing quality, computation time, and scalability across small and larger problem instances, finding OR-Tools to deliver faster, more consistent solutions with minimal tuning, while ACO offers customizable routing behavior through parameter settings. The study highlights a practical speed and robustness edge for OR-Tools, with ACO providing flexibility in dynamic or constrained settings. These results guide logistics practitioners in selecting routing strategies and motivate future hybrids and real-time extensions to improve scalability.
Abstract
In modern logistics management systems, route planning requires high efficiency. The Open Capacitated Vehicle Routing Problem (OCVRP) deals with finding optimal delivery routes for a fleet of vehicles serving geographically distributed customers, without requiring the vehicles to return to the depot after deliveries. The present study is comparative in nature and speaks of two algorithms for OCVRP solution: Ant Colony Optimization (ACO), a nature-inspired metaheuristic; and Google OR-Tools, an industry-standard toolkit for optimization. Both implementations were developed in Python and using a custom dataset. Performance appraisal was based on routing efficiency, computation time, and scalability. The results show that ACO allows flexibility in routing parameters while OR-Tools runs much faster with more consistency and requires less input. This could help choose among routing strategies for scalable real-time logistics systems.