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
9/30/2025
cs.CLcs.AI

No confident code implementation found for this paper. 4 possible matches can be shown below.

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 4 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Learn to Solve Vehicle Routing Problems ASAP: A Neural Optimization Approach for Time-Constrained Vehicle Routing Problems with Finite Vehicle Fleet

81% match2024

Learning for routing: A guided review of recent developments and future directions

79% match2025

Demand Selection for VRP with Emission Quota

79% match2025

Accelerating Vehicle Routing via AI-Initialized Genetic Algorithms

78% match2025

Enhancing CVRP Solver through LLM-driven Automatic Heuristic Design

78% match2026

Preference-Aware Delivery Planning for Last-Mile Logistics

77% match2023

Cite this paper

@article{omar2025comparative,
  title  = {Comparative Analysis of Ant Colony Optimization and Google OR-Tools for Solving the Open Capacitated Vehicle Routing Problem in Logistics},
  author = {Assem Omar and Youssef Omar and Marwa Solayman and Hesham Mansour},
  year   = {2025},
  eprint = {2509.26216},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2509.26216v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2509.26216.svg)](https://codeofpaper.com/paper/2509.26216)

Discussion