Optimizing Spare Part Delivery Routes Using Ant Colony Optimization

Abstract

General Background: Efficient distribution routing is fundamental in logistics, directly influencing operational costs, fuel consumption, and customer satisfaction. Specific Background: PT Threelog Kencana Mandiri in Batam currently experiences inefficiencies due to suboptimal routing strategies for spare part deliveries. Knowledge Gap: Despite various routing solutions, few are tailored for dynamic, real-world constraints like vehicle capacity and varying delivery points in mid-scale operations. Aim: This study aims to optimize PT Threelog’s delivery routes using the Ant Colony Optimization (ACO) algorithm by modeling the problem as a Vehicle Routing Problem (VRP) with specific constraints. Results: The implementation of ACO significantly reduced total travel distance compared to the company’s existing manual routing, resulting in lower fuel usage, faster delivery times, and enhanced customer service. Novelty: Unlike generic routing systems, the proposed ACO-based model dynamically adapts to real operational variables through pheromone-based local and global updates, improving solution quality iteratively. Implications: This research offers a practical, intelligent decision-support framework for logistics firms, proving that metaheuristic algorithms like ACO can robustly handle complex, real-world delivery challenges and scale to broader 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