Metasezgisel algoritmalar kullanarak depo toplama yolu optimizasyonu

Warehouse picking path optimization is a critical issue in the logistics industry as it can greatly affect the efficiency and productivity of warehouse operations. The optimization process can be divided into two sub-problems: shortest path problem which can be solved using well-known algorithms such as Dijkstra's algorithm and Bellman–Ford algorithm, and traveling salesman problem, which will be the primary focus of this thesis. The Traveling Salesman Problem is a combinatorial optimization problem and belongs to the class of NP-hard problems. Since, it is usually impossible to obtain an exact solution to the problem within a polynomial time, alternative methods such as metaheuristic algorithms are utilized. In this thesis, we explore several metaheuristic algorithms, including Particle Swarm Optimization, Genetic Algorithm, Archimedes Optimization Algorithm, Honey Badger Algorithm, and Harris Hawks Optimization, and compare them in terms of their computational efficiency and effectiveness in finding optimal solutions. Our experimental results show that Harris Hawks Optimization and Honey Badger Algorithm yield better results compared to other algorithms. These results will contribute to the development of efficient and effective techniques for optimizing warehouse picking routes in the logistics sector. Keywords: Picking Path Optimization; Shortest Path Problem; Traveling Salesman Problem; Metaheuristic Algorithms; Logistics