An effective warehouse design: Shelf layout improvement with Pareto analysis and particle swarm algorithm

Warehouse and storage concepts are important to any business because of customer service and cost. Warehouse management activities play a major role in the success of a business in terms of customer service and inventory management. Warehouses have important functions within the business. In this thesis study, the product and raw material warehouse of the enterprise grows in direct proportion to this growth in proportion to the increase in the 5-year production volume of an enterprise. The 5-year prediction values of the increase in production volume were calculated and these prediction values were used in the warehouse improvement process. In this project, the warehouse design was carried out by calculating the capacity, shelf type, logistics flow, required number of personnel and equipment for the product and raw material warehouse, adhering to the predictive values. The warehouse design was tested with two different methods and the accuracy and reliability of the design was sought to be observed. The first method used was Pareto analysis, and the appropriate path was drawn after the data handling process. In the same way, the warehouse design was made with the particle swarm algorithm method. Are there any differences between these two designs? Do the results of the designs support each other? Which method leads to more accurate results when designing or improving the warehouse? Keywords: Facility Design, Warehouse Design, Optimization, ABC Analysis, Particle Swarm Algorithm