Comparative Analysis of Ant Colony Optimization and Google OR-Tools for Solving the Open Capacitated Vehicle Routing Problem in Logistics

• URL: http://arxiv.org/abs/2509.26216v1
• Date: Tue, 30 Sep 2025 13:18:14 GMT
• Title: Comparative Analysis of Ant Colony Optimization and Google OR-Tools for Solving the Open Capacitated Vehicle Routing Problem in Logistics
• Authors: Assem Omar, Youssef Omar, Marwa Solayman, Hesham Mansour,
• Abstract summary: The Open Capacitated Vehicle Routing Problem (OCVRP) deals with finding optimal delivery routes for a fleet of vehicles serving geographically distributed customers.<n>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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Relatron: Automating Relational Machine Learning over Relational Databases [50.94254514286021]
  We present a study that unifies RDL and DFS in a shared design space and conducts architecture-centric searches across diverse RDB tasks.<n>Our analysis yields three key findings: (1) RDL does not consistently outperform DFS, with performance being highly task-dependent; (2) no single architecture dominates across tasks, underscoring the need for task-aware model selection; and accuracy is an unreliable guide for choice architecture.
  arXiv  Detail & Related papers  (2026-02-26T02:45:22Z)
• FastLane: Efficient Routed Systems for Late-Interaction Retrieval [58.060096779432094]
  FastLane is a novel retrieval framework that dynamically routes queries to their most informative representations.<n>By bridging late-interaction models with Approximate Nearest Neighbor Search (ANNS), FastLane enables scalable, low-latency retrieval.
  arXiv  Detail & Related papers  (2026-01-10T02:22:01Z)
• Beyond Shortest Path: Agentic Vehicular Routing with Semantic Context [0.3767731868757604]
  This paper introduces and evaluates PAVe, a hybrid agentic assistant designed to augment classical pathfinding algorithms with contextual reasoning.<n>In a benchmark of realistic urban scenarios, PAVe successfully used complex user intent into appropriate route modifications.<n>We conclude that combining classical routing algorithms with an LLM-based semantic reasoning layer is a robust and effective approach for creating personalized, adaptive, and scalable solutions for urban mobility optimization.
  arXiv  Detail & Related papers  (2025-11-06T15:37:11Z)
• Accelerating Vehicle Routing via AI-Initialized Genetic Algorithms [53.75036695728983]
  Vehicle Routing Problems (VRP) are a fundamental NP-hard challenge in Evolutionary optimization.<n>We introduce an optimization framework where a reinforcement learning agent is trained on prior instances and quickly generates initial solutions.<n>This framework consistently outperforms current state-of-the-art solvers across various time budgets.
  arXiv  Detail & Related papers  (2025-04-08T15:21:01Z)
• SCoTT: Strategic Chain-of-Thought Tasking for Wireless-Aware Robot Navigation in Digital Twins [78.53885607559958]
  We propose SCoTT, a wireless-aware path planning framework.<n>We show that SCoTT achieves path gains within 2% of DP-WA* while consistently generating shorter trajectories.<n>We also show the practical viability of our approach by deploying SCoTT as a ROS node within Gazebo simulations.
  arXiv  Detail & Related papers  (2024-11-27T10:45:49Z)
• Multi-Agent Environments for Vehicle Routing Problems [1.0179489519625304]
  We propose a library composed of multi-agent environments that simulates classic vehicle routing problems. The library, built on PyTorch, provides a flexible modular architecture design that allows easy customization and incorporation of new routing problems.
  arXiv  Detail & Related papers  (2024-11-21T18:46:23Z)
• Learn to Solve Vehicle Routing Problems ASAP: A Neural Optimization Approach for Time-Constrained Vehicle Routing Problems with Finite Vehicle Fleet [0.0]
  We propose an NCO approach to solve a time-constrained capacitated VRP with a finite vehicle fleet size. The method is able to find adequate and cost-efficient solutions, showing both flexibility and robust generalizations.
  arXiv  Detail & Related papers  (2024-11-07T15:16:36Z)
• Roulette-Wheel Selection-Based PSO Algorithm for Solving the Vehicle Routing Problem with Time Windows [58.891409372784516]
  This paper presents a novel form of the PSO methodology that uses the Roulette Wheel Method (RWPSO) Experiments using the Solomon VRPTW benchmark datasets on the RWPSO demonstrate that RWPSO is competitive with other state-of-the-art algorithms from the literature.
  arXiv  Detail & Related papers  (2023-06-04T09:18:02Z)
• Combinatorial Optimization enriched Machine Learning to solve the Dynamic Vehicle Routing Problem with Time Windows [5.4807970361321585]
  We propose a novel machine learning pipeline that incorporates an optimization layer. We apply this pipeline to a dynamic vehicle routing problem with waves, which was recently promoted in the EURO Meets NeurIPS Competition at NeurIPS 2022. Our methodology ranked first in this competition, outperforming all other approaches in solving the proposed dynamic vehicle routing problem.
  arXiv  Detail & Related papers  (2023-04-03T08:23:09Z)
• Preference-Aware Delivery Planning for Last-Mile Logistics [3.04585143845864]
  We propose a novel hierarchical route with learnable parameters that combines the strength of both the optimization and machine learning approaches. By using a real-world delivery dataset provided by the Amazon Last Mile Research Challenge, we demonstrate the importance of having both the optimization and the machine learning components.
  arXiv  Detail & Related papers  (2023-03-08T02:10:59Z)
• Integrated Decision and Control: Towards Interpretable and Efficient Driving Intelligence [13.589285628074542]
  We present an interpretable and efficient decision and control framework for automated vehicles. It decomposes the driving task into multi-path planning and optimal tracking that are structured hierarchically. Results show that our method has better online computing efficiency and driving performance including traffic efficiency and safety.
  arXiv  Detail & Related papers  (2021-03-18T14:43:31Z)
• Reinforcement Learning Based Vehicle-cell Association Algorithm for Highly Mobile Millimeter Wave Communication [53.47785498477648]
  This paper investigates the problem of vehicle-cell association in millimeter wave (mmWave) communication networks. We first formulate the user state (VU) problem as a discrete non-vehicle association optimization problem. The proposed solution achieves up to 15% gains in terms sum of user complexity and 20% reduction in VUE compared to several baseline designs.
  arXiv  Detail & Related papers  (2020-01-22T08:51:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.