Maximizing the travel time efficiency of the automated storage and retrieval system of company X

This thesis addresses the challenge of maximizing travel time efficiency for shuttles in a multi-deep Automated Storage and Retrieval System (AS/RS). As the demand for high-density storage solutions grows, optimizing the movement of shuttles becomes crucial to reducing operational costs and improving system performance and efficiency. A mathematical optimization model was developed to minimize the total travel time of shuttles while considering factors such as item retrieval frequency, composition, and height. To accurately assess the performance of the system, a simulation model was created in Python. To further refine the solutions, two metaheuristic approaches—Simulated Annealing (SA) and Variable Neighborhood Search (VNS)—were implemented. These methods effectively explore the solution space, aiming to find near-optimal results within a reasonable computational time. Through rigorous testing and analysis, the proposed model demonstrated significant improvements in shuttle travel time efficiency, making it a valuable contribution to the field of automated warehousing systems.