<![CDATA[General Background: Efficient route planning is a fundamental aspect of logistics, directly impacting operational costs, fuel consumption, and customer satisfaction. Specific Background: A logistics company based in Batam has been facing inefficiencies in spare part delivery operations due to suboptimal routing strategies. Knowledge Gap: While various routing solutions exist, few are tailored to accommodate dynamic, real-world constraints such as vehicle capacity and varying delivery points in mid-scale logistics operations. Aim: This study aims to optimize delivery routes using the Ant Colony Optimization (ACO) algorithm by modeling the problem as a Vehicle Routing Problem (VRP) with specific operational constraints. Results: The implementation of ACO significantly reduced total travel distance compared to the company’s existing manual routing approach. As a result, fuel consumption was lowered, delivery times improved, and customer service enhanced. Novelty: Unlike generic routing systems, the proposed ACO-based model dynamically adapts to real operational variables through pheromone-based local and global updates, improving the solution iteratively with each cycle. Implications: This research provides a practical and intelligent decision-support framework for logistics planning, demonstrating that metaheuristic algorithms such as ACO can robustly handle complex delivery challenges and be scaled to broader logistics applications Highlights: Improves route efficiency using ACO in real delivery operations. Reduces distance, fuel usage, and delivery time significantly. Provides a scalable model for intelligent logistics planning. Keywords: Ant Colony Optimization, Vehicle Routing Problem, Logistics Efficiency, Route Optimization, Metaheuristic Algorithm]]>
