Class‐Based Storage with a Finite Number of Items: Using More Classes is not Always Better. Issue 8 (26th January 2015)

• Record Type: Journal Article
• Authors: Yu, Yugang; de Koster, René B.M.; Guo, Xiaolong
• Is Part Of: Production and operations management. Volume 24:Issue 8(2015:Aug.)
• Page Start: 1235
• Page End: 1247
• Publication Date: 2015-01-26
• Subjects: Production management -- Periodicals; 658.505
• DOI: 10.1111/poms.12334
• Languages: English
• ISSNs: 1059-1478

Abstract

<abstract abstract-type="main" id="poms12334-abs-0001"> <title> <x xml:space="preserve">Abstract</x> </title> <p>Class‐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 <italic>required storage space</italic> for all items <italic>equals</italic> their <italic>average inventory level</italic>, 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 <italic>small</italic> 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.</p> </abstract>