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Given a picker-to-part warehouse having a simple rectilinear aisle arrangement with north–south storage aisles and east–west travel aisles (or “cross-aisles”), this article investigates the optimal placement of the cross-aisles as a consequence of the probability mass function of the order pick points, as determined by the storage policy. That is, what placement of the cross-aisles will result in a minimal expected path length for the picker? An analytical solution procedure is developed for the optimal placement of a single middle cross-aisle for a given storage policy. A simplifying assumption is made as regards picker routing, but arbitrary non-random storage policies are considered. The solution procedure is generalized to a method for multiple cross-aisles. Some example problems are solved and a simulation study is used to measure the impact of the assumptions made to generate the method. [ABSTRACT FROM PUBLISHER]

The focus of this work is to develop analytical models to estimate worker blocking in narrow-aisle order picking systems. The situation with deterministic pick times has already been reported in the literature but the case with non-deterministic pick times remains an open question. Models that use a non-deterministic pick time for cases where the pick:walk-time ratios are 1:1 and ∞:1 are presented. It is shown that the results obtained using deterministic pick times are in fact sensitive to this assumption. [ABSTRACT FROM AUTHOR]

Order picking is the warehousing process by which products are retrieved from their storage locations in response to customers' orders. Its efficiency can be influenced through the layout of the area and the operating policies. We present a model that minimizes travel distances in the picking area by identifying an appropriate layout structure consisting of one or more blocks of parallel aisles. The model has been developed for one commonly used routing policy, but it is shown to be fairly accurate for some other routing policies as well. [ABSTRACT FROM AUTHOR]

In this paper we present the results of a research project to develop analytical models to determine the throughput of pods of carousels or Vertical Lift Modules (VLMs) with human order-pickers picking batches of orders. The primary advantage of using carousels/VLMs over static shelving is that much of the walking time associated with static shelving is removed since the carousels/VLMs bring the items to the order pickers. Multiple carousels/VLMs are typically grouped together in what are called pods to ensure that the order-picker does not have to wait on the carousel/VLM while it is rotating to retrieve the next item. The carousel and VLM throughput models are presented along with testing that indicates that they perform well under various system configurations. We include an example of how the throughput models can be used as an aid in the storage design process. [ABSTRACT FROM AUTHOR]

In this paper, the problem of pre-positioning a storage/retrieval (S/R) machine in an automated storage/retrieval system (AS/RS) when the machine is idle is addressed. The objective of the pre-positioning strategy is to minimize the maximum response time to service incoming requests into the AS/RS. A model is developed under the operating condition that each machine can serve one or multiple dedicated aisle(s) of the system. The aisles are assumed to exist in the same region of the AS/RS and thus form a valid order storage/retrieval zone. A solution algorithm for determining the optimal dwell point or location is developed. The paper assumes a dynamic environment where orders arrive over time and are to be serviced by the S/R machine. A dwell point or strategic point to pre-position the machine is to be determined whenever the S/R machine becomes idle in anticipation of an incoming service request. A numerical example is provided to demonstrate how the technique can be used in an actual production setting to improve the overall order turnaround time. [ABSTRACT FROM AUTHOR]

This paper is focused on the control problem of determining the dwell point of an idle storage/retrieval (S/R) machine in an automated S/R system to improve the overall system service level as measured by the turnaround time of requests to the system. The emphasis of the paper is on the minimization of the expected response time over all retrieval and storage requests executed by the system. The model presented is developed under the operating condition that each machine serves several dedicated aisles of the system. The aisles are assumed to exist in the same region of the automated storage/retrieval system (AS/RS) and thus form a valid order S/R zone. A solution algorithm for determining the optimal dwell point location is developed. The paper assumes a dynamic environment where orders arrive over time and are to be serviced by the S/R machine. A dwell point or strategic point to pre-position the machine is to be determined whenever the machine becomes idle in anticipation of an incoming service request. A numerical example is provided to demonstrate how the technique can be used in an actual production setting to improve the overall order turnaround time. [ABSTRACT FROM AUTHOR]

In this paper we investigate automated storage/retrieval systems where a due date is specified for each retrieval order. We consider the inclusion of both order retrieval and storage in the same tour when possible. Rules are developed for sequencing and batching orders to tours such that the total tardiness of retrievals per group of orders is minimized. [ABSTRACT FROM AUTHOR]

A classical order picking problem is the case where items have to be picked from both sides of an aisle and the picker cannot reach items on both sides without changing position. Hence the picker must cross the aisle one or more times. An efficient optimal algorithm is developed and shown to yield policies with up to 30% savings in travel time over commonly used policies. It is also shown that, for most practical aisle widths, it is significantly more efficient to pick both sides of the aisle in the same pass (a traversal policy) rather than pick one side and then pick the other side (a return policy) unless the pick densities are greater than 50%. All the algorithms presented here can be implemented in real time on a microcomputer. [ABSTRACT FROM AUTHOR]