<![CDATA[General Background: Efficient distribution systems are essential in logistics, especially in geographically complex regions like Indonesia. Specific Background: PT. Sinar Genta Logistik distributes three-wheeled Viar motorcycles using double-deck trucks but has not yet optimized its delivery routes. Knowledge Gap: Although the Ant Colony Optimization (ACO) method has been widely applied in solving distribution problems, its application in routing three-wheeled vehicle shipments with fleet capacity constraints using real industry data is limited. Aims: This study aims to optimize delivery routes to reduce travel distance and improve route allocation by applying the ACO algorithm. Results: Using Python programming on Google Colab, the ACO method reduced the total travel distance from 1,849.8 km to 1,556.5 km—a reduction of 293.3 km or 15.86%. The new routing model reorganized deliveries into six vehicle routes adjusted to truck capacity. Novelty: The research applies ACO specifically for the distribution of Viar three-wheeled vehicles with real-world data, integrating Google Maps-based routing and considering capacity constraints. Implications: The findings offer a practical solution for logistics firms to decrease operational distance and adopt algorithm-based distribution strategies for cost efficiency and timely deliveries. Highlights: ACO reduced total delivery distance by 15.86%. Delivery restructured into six efficient routes. Uses real company data and Python-based ACO. Keywords: Ant Colony Optimization, Vehicle Routing Problem, Logistics, Fleet Capacity, Viar]]>
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