Rack Optimization for Multi-Deep Storage Systems With Class Based Storage and Product Information



Efficient storage and retrieval of unit loads are crucial in modern warehouse management. Multi-deep storage systems have become increasingly popular due to their higher space utilization compared to single-deep systems. However, blocking loads during retrieval operations can lead to longer lead times and decreased efficiency. In this study, we investigate how the use of product-specific information can improve the performance of multi-deep storage systems. Specifically, we propose two approaches to minimize blocking loads: first, selecting the load with the fewest reshufflings for retrieval when the number of loads per product is known; second, using class-based storage to store high-turnover products closer to the input/output points. To evaluate these approaches, we develop a throughput model that considers the impact of various factors, including the number of loads, the dimensions of the rack, and the dimensions of the classes. Our results show that incorporating product-specific information can significantly reduce the number of reshufflings required during retrieval operations, thus improving warehouse efficiency. Our proposed approaches have the potential to optimize the design of multi-deep storage systems and minimize blocking loads, which can ultimately improve throughput.

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