Class-based Storage With a Finite Number of Items

ABC class-based storage is widely studied in literature and applied practice. It divides all stored items into a limited number of classes according to their demand rates (turnover per unit time). Classes of items with higher turnovers are stored in a region closer to the warehouse depot. In literature, it is commonly shown that the use of more storage classes leads to shorter travel time for storing and retrieving items. A basic assumption in this literature commonly is that the required storage space of items equals their average inventory levels, which is right if an infinite number of items are stored in each storage region. However, if a finite number of items are stored in the warehouse, more storage classes need more space to store the items: more classes lead to fewer items stored per class, which have less opportunity to share space with other items. This paper revisits ABC class-based storage by relaxing the common assumption that the total required storage space of all items is independent of the number of classes. We develop a travel time model and use it for optimizing the number and the boundaries of classes. Our numerical results illustrate that a small number of classes is optimal

Automated and Robotic Warehouses: Developments and Research Opportunities

The first automated, high-bay, warehouses were introduced some 50 years ago. Since then, developments have continued at a rapid pace. Initially, automation was mainly focused on pallet warehouses with bulk storage facilities. A major reason was to increase the storage density, which could be achieved by making the warehouses higher. Later, mini-load warehouses and order picking warehouses were also automated. In this paper we will discuss the different types of automated systems as well as a number of scientific results that are now known about such systems. We will first discuss storage systems for unit loads (bins and pallets). This will be followed by order picking systems from which individual packages can be picked. Finally, we will provide our future expectations of warehouse automation

An Evaluation of Cross-Efficiency Methods, Applied to Measuring Warehouse Performance

In this paper method and practice of cross-efficiency calculation is discussed. The main methods proposed in the literature are tested not on a set of artificial data but on a realistic sample of input-output data of European ware- houses. The empirical results show the limited role which increasing automation investment and larger warehouse size have in increasing productive performance. The reason is the existence of decreasing returns to scale in the industry, resulting in sub-optimal scales and inefficiencies, regardless of the operational performance of the facilities. From the methodological perspective, and based on a multidimensional metric which considers the capability of the various methods to rank warehouses, their ease of implementation, and their robustness to sensitivity analyses, we conclude to the superiority of the classic Sexton et al. (1986) method over recently proposed, more sophisticated methods

An evaluation of cross-efficiency methods: With an application to warehouse performance

Cross-efficiency measurement is an extension of Data Envelopment Analysis that allows for tie-breaking ranking of the Decision Making Units (DMUs) using all the peer evaluations. In this article we examine the theory of cross-efficiency measurement by comparing a selection of methods popular in the literature. These methods are applied to performance measurement of European warehouses. We develop a cross-efficiency method based on a rank-order DEA model to accommodate the ordinal nature of some key variables characterizing warehouse performance. This is one of the first comparisons of methods on a real-life dataset and the first time that a model allowing for qualitative variables is included in such a comparison. Our results show that the choice of model matters, as one obtains statistically different rankings from each one of them. This holds in particular for the multiplicative and game-theoretic methods whose results diverge from the classic method. From a managerial perspective, focused on the applicability of the methods, we evaluate them through a multidimensional metric which considers their capability to rank DMUs, their ease of implementation, and their robustness to sensitivity analyses. We conclude that standard weight-restriction methods, as initiated by Sexton et al. [48], perform as well as recently introduced, more sophisticated alternativesSpanish Ministry of Science and Innovation (Ministerio de Ciencia e Innovación), the State Research Agency (Agencia Estatal de Investigación) and the European Regional Development Fund (Fondo Europeo de Desarrollo Regional) under grants EIN2020-11226

Accidents will happen: do hazard-reducing systems help?

In the summer of 2009, soon after the winners of the annual Safest Warehouse of the Year Awards were being lauded at an industry conference, journalist Marcel te Lindert wondered out loud in his regular column for the Dutch magazine Logistiek, why it was that there were more questions raised about safety issues than there were answers

Forward-reserve storage strategies with order picking: When do they pay off?

Customer order response time and system throughput capacity are key performance measures in warehouses. They depend strongly on the storage strategies deployed. One popular strategy is to split inventory into a bulk storage and a pick stock, or Forward-Reserve (FR) storage. Managers often use a rule of thumb: when the ratio m of average picks per replenishment is larger than a certain factor, it is beneficial to split inventory. However, research that systematically quantifies the benefits is lacking. We quantify the benefits analytically by developing response travel time models for FR storage in an Automated Storage/Retrieval system combined with order picking. We compare performance of FR storage with turnover class-based storage, and find when it pays off. Our findings illustrate that, in FR storage systems where forward and reserve stocks are stored in the same rack, FR storage usually pays off, as long as m is sufficiently larger than 1. The response time savings can go up to 50% when m is larger than 10. We validate these results using real data from a wholesale distributor

Automated and Robotic Warehouses: Developments and Research Opportunities

The first automated, high-bay, warehouses were introduced some 50 years ago. Since then, developments have continued at a rapid pace. Initially, automation was mainly focused on pallet warehouses with bulk storage facilities. A major reason was to increase the storage density, which could be achieved by making the warehouses higher. Later, mini-load warehouses and order picking warehouses were also automated. In this paper we will discuss the different types of automated systems as well as a number of scientific results that are now known about such systems. We will first discuss storage systems for unit loads (bins and pallets). This will be followed by order picking systems from which individual packages can be picked. Finally, we will provide our future expectations of warehouse automation

The impact of order batching and picking area zoning on order picking system performance

This paper proposes an approximation model based on queuing network theory to analyze the impact of order batching and picking area zoning on the mean order throughput time in a pick-and-pass order picking system. The model includes the sorting process needed to sort the batch again by order. Service times at pick zones are assumed to follow general distributions. The first and second moments of service times at zones and the visiting probability of a batch of orders to a pick zone are derived. Based on this information, the mean throughput time of an arbitrary order in the order picking system is obtained. Results from a real application and simulation show that this approximation model provides acceptable accuracy for practical purposes. Furthermore, the proposed method is simple and fast and can be easily applied in the design and selection process of order picking systems.Queueing Stochastic processes Simulation Applied probability