Using ant colony and simulated annealing algorithm methods in load transport optimization

Population growth, which continues day by day, increases personal and social needs and brings many problems with it. Logistics plays an important role in meeting the increasing needs. Logistics is the process that takes a product from the point of production to the point of consumption. It is of great importance that a prepared product reaches the desired place on time. In this study, a solution to the Traveling Salesman Problem (TSP) is sought on a sample application. In the study, there is the shortest route to the Organized Industrial Zones (OIZ) in 81 provinces, one from each province, and return to the starting point. Analysis in road transport is done with Ant Colony Optimization (ACO) and Simulated Annealing Algorithm (SAA). The results of the analysis are compared with the Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Linear Optimization (LO) and Tabu Search (TS) algorithms made in different studies. As a result of the analysis made with ACO for 81 OIZs, the shortest road distance is 10198 km, and as a result of the analysis made with SAA, the shortest road distance is 10133 km. When the results and previous analyses are compared, it is observed that SAA and ACO results are better than TS, GA and PSO results, and worse than LO results.