Costs Optimization of the Search for Goods and Accompanied Route Planning based on Algorithms of Traveling Salesman Problem

The optimization of goods retrieval and route planning within industrial warehouses is of paramount importance in today's rapidly evolving supply chain landscape.With the rise of e-commerce and customer expectations for swift deliveries, warehouse managers face increasing pressure to streamline their operations efficiently.This paper delves into the pivotal challenge of solving the Traveling Salesman Problem (TSP) in the context of goods retrieval from storage locations, with a focus on computational efficiency and solution quality.As the warehousing industry undergoes transformative shifts, the need for precise, time-effective retrieval of goods becomes evident.Authors investigate and compare four distinct algorithms: the brute force factorial method, nearest neighbor, insertion nearest neighbor, and simulated annealing, seeking to identify the most suitable approach for optimizing warehouse operations.The findings from this study not only shed light on the algorithms' performance but also provide valuable insights for warehouse managers aiming to strike a balance between computational efficiency and the quality of goods retrieval and route planning.