Class‐Based Storage with a Finite Number of Items: Using More Classes is not Always Better

Abstract

C lass-based storage is widely studied in the literature and applied in practice. It divides all stored items into a number of classes according to their turnover. A class of items with higher turnover is allocated to a region closer to the warehouse depot. In the literature, it has been shown that the use of more storage classes leads to a shorter travel time for storing and retrieving items. A basic assumption in this literature is that the required storage space for all items equals their average inventory level, which is valid only if an infinite number of items can be stored in each storage region. This study revisits class-based storage by considering each storage space to contain only a finite number of items. We develop a travel time model and an algorithm that can be used for determining the optimal number and boundaries of storage classes in warehouses. Different from the conventional research, our findings illustrate that commonly a small number of classes is optimal. In addition, we find the travel time is fairly insensitive to the number of storage classes in a wide range around the optimum. This suggests that a manager can select a near-optimal number of storage classes in an easy way and need not be worried about the impact of storage-class reconfigurations. We validate our findings for various cases, including different ABC-demand curves, space-sharing factors, number of items, storage rack shapes, discrete storage locations, and stochastic item demand.

Study on a storage location strategy based on clustering and association algorithms

“…In the general case, a bowl-shaped relationship exists between the time required to access the goods in the storage system and the number of classification categories (Yu et al 2015). It has been theoretically revealed that regardless of the quantity of goods, the optimal storage efficiency can always be achieved by classifying the goods into 3-5 categories.…”

Optimal two-class-based storage in a live-cube compact storage system

“…He also determines the mean and variance of the travel times for AS/R systems for the two-class NSIT case. Recently, Yu et al (2015) determined optimal zone boundaries and showed that an optimal (small) number of zones exist, if space requirements are taken into account. Stadtler (1996), Sari et al (2005), Gue and Kim (2007), De Koster et al (2008), de Koster (2009), andZaerpour et al (2015b) study compact storage systems using different depth handling systems.…”

Analytical Optimization for the Warehouse Sizing Problem Under Class-Based Storage Policy

“…Additionally, function (10) implies that regardless of the values of w 1 , w 2 , w 3 , t A , t B , t C , a and A, the global optimum of E(D) never is going to be achieved in Case 2, Case 3, Case 5, Case 6, Case 7 or Case 8. In consequences, we can ignore those cases in the optimization process.…”

“…In this way, class-based storage policies have a lower expected travel time to pick and retrieve loads when compared to randomize policies. Yu et al [10] showed that even if the storage classes decrease the expected travel time, a small number of classes is optimal when a discrete number of items for each class is considered. Their results are robust to different kinds of ABC-demand profiles and warehouse shape.…”

“…In this paper, we gain insight into the warehouse dimensioning problem [6] under the most common storage assignment policy, the ABC classification [10] . Francis [3] developed the first attempt in warehouse dimensioning in a very simplified context and concluded for this context that a rectangular warehouse with one P&D should position it in the middle of its lower side.…”