Optimizing the location of products in a warehouse using genetic algorithms

Product allocation problems in warehouse management have recently been researched in large-scale warehouses compared with scheduling and path planning problems already widely studied. In this paper, we explore way to increase efficiency and reduce the cost of warehouse operations through optimization of product storage location. Optimizing a large-scale warehouse, we propose a new product allocation model using cumulative data. Using order frequency and product mass, a Genetic algorithm(GA) determines the location of each product. Furthermore, computer simulations show that products with frequently ordered are moved to optimal locations as well as reduce the total travel time for forklifts approximately 50% and represent the superiority of GA compared with other algorithms.