Optimising Export Logistics Using Genetic Algorithm and Ant Colony Optimization: A Real Case Study from Turkey

In today’s competitive business environment, logistics activities play a pivotal role in achieving cost reductions and securing a competitive edge. Given the significance of exports, imports, warehouse design, inventory management, and demand/supply forecasting, logistics must be regarded as a core strategic function. This study presents a model designed for a manufacturing company based in Manisa, Turkey, which distributes its products to both domestic and international markets. The primary objective is to enhance the efficiency and effectiveness of the company’s international logistics operations, particularly through improvements to existing transportation methods. Using real-world data on export operations to European countries from the past year, we develop a shipment model that integrates two metaheuristic optimisation techniques: The Ant Colony Optimization (ACO) algorithm and the Genetic Algorithm (GA). ACO is employed to optimize routing and scheduling by simulating the foraging behaviour of ants, thereby identifying the most efficient transportation paths. The GA is then utilized to further refine potential solutions through iterative evolutionary processes using this data. The findings demonstrate substantial improvements in both cost reduction and operational efficiency, underscoring the efficacy of combining ACO and GA within a logistics framework. By offering a comprehensive approach to logistics optimisation, this study provides valuable insights for businesses seeking to enhance their competitive position through cost-effective and efficient transportation strategies.