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An automated storage and retrieval system with multiple in-the-aisle pick positions (MIAPP-AS/RS) is a case-level order fulfillment technology that enables order picking via multiple pick positions (outputs) located in the aisle. This article develops expected travel time models for different operating policies and different physical configurations. These models can be used to analyze MIAPP-AS/RS throughput performance during peak and non-peak hours. Moreover, closed-form approximations are derived for the case of an infinite number of pick positions, which enable the optimal shape configuration that minimizes expected travel times to be derived. The expected travel time models are compared with a simulation model of a discrete rack, and the results validate that the proposed models provide good estimates. Finally, a numerical experiment is conducted to illustrate the trade-offs between performance of operating policies and design configurations. It is found that MIAPP-AS/RS with a dual picking floor and input point is a robust configuration due to the single command operating policy having a comparable throughput performance to a dual-command operating policy. [ABSTRACT FROM PUBLISHER]

The performance of a warehouse is impacted by how it is configured, yet there is no optimization model in the literature to answer the question of how to best configure the warehouse in terms of warehouse shape and the configuration of the dock doors. Moreover, the building blocks for such a model (put-away, replenishment, and order picking models that can be combined in an optimization model) are either not available (in the case of replenishment) or built on a set of inconsistent assumptions (in the case of put-away and order picking). Therefore, this article lays the foundation for more sophisticated warehouse configuration optimization models by developing the first analytical model for replenishment operation performance and extending put-away and order picking performance models. These new models are used to address a question motivated by industry: the optimal configuration of a case-picking warehouse in terms of the shape of the facility and whether the facility is configured with dock doors on one or both sides. An example is presented to demonstrate the use of the proposed models in answering such a question, quantifying the benefit of using an integrated approach to warehouse configuration. [ABSTRACT FROM PUBLISHER]

This article addresses a new class of picker-to-goods order picking systems, in which item retrieval is decoupled from order assembly by a pick-to-buffer technology. This decoupling of item retrieval and order assembly dramatically changes the dynamics of the pick process. Picking to buffers means that the picker is limited by the number of available buffers, rather than by the number of items or orders that may be transported simultaneously, so that order assembly must be carefully synchronized, in real-time, so the picker is not blocked by already filled buffers. This synchronization requirement forces consideration of the stochastic nature of the picking process in designing such systems, in particular, estimating the variance of pick times. This is in sharp contrast with traditional picker-to-goods systems, where estimates of mean pick times are sufficient for evaluating design performance. In this article, models for both expected value and variance of picker travel time are proposed and evaluated. The approximate analytic results are compared with results from detailed simulation and are sufficiently accurate to support design. The real-time synchronization of order assembly with picking is a topic of further research. [ABSTRACT FROM PUBLISHER]

A recent trend in the layout design of unit-load warehouses is the application of layouts without conventional parallel pick aisles and straight middle aisles. Two examples for such designs are flying-V and fishbone designs for single- and dual-command operations. In this study, it is shown that the multi-item order picking problem can be solved in polynomial time for both fishbone and flying-V layouts. These two designs are compared with the traditional parallel-aisle design under the case of multi-item pick lists. Simple heuristics are proposed for fishbone layouts that are inspired by those put forward for parallel-aisle warehouses and it is experimentally shown that a modification of the aisle-by-aisle heuristic produces good results compared with other modified S-shape and largest gap heuristics when items have uniform demand. Computational experiments are performed in order to compare the performances of fishbone and traditional layouts under optimal routing and it is shown that a fishbone design can obtain improvements of around 20% over parallel-aisle design in single-command operations but can perform as high as around 30% worse than an equivalent parallel-aisle layout as the size of the pick list increases. The sensitivity of the results to varying demand skewness levels when volume-based storage is applied is tested and it is shown that unlike the single- and dual-command cases, a fishbone design performs better compared to a traditional design under highly skewed demand as opposed to uniform demand. [ABSTRACT FROM AUTHOR]

This article considers a two-block warehouse with low-level aisles having dedicated pickers who follow a return routing policy within the aisle. Travel distance models are formulated assuming a given product density distribution in aisles for multi-item picks. An analytical recursive procedure is developed to find optimaln-class partitions of each warehouse aisle. A range on the number of classes and number of picks for which class-based storage is an attractive option over the random storage policy is determined through a computational study. The article also presents an iterative hierarchical framework to simultaneously obtain pick list size, number of aisles, and class storage boundaries. A sensitivity analysis of optimal pick travel distance against important warehouse design parameters is presented. [ABSTRACT FROM PUBLISHER]

This note develops analytical picker blocking models to simply and accurately assess picker blocking in parallel-aisle order picking systems when multiple picks occur at a pick point. The Markov chain--based models characterize the two bounding walking speeds for modeling picker movement: unit walk time and instantaneous walk time. The unit walk time model has a state-space transition matrix that is reduced by a factor of 16 for both narrow-aisle and wide-aisle systems. Additionally, the model improves upon the existing literature by providing a closed-form expression for the narrow-aisle system with instantaneous walk time. Experimental results are provided to demonstrate how picker blocking is influenced by pick density in a variety of scenarios under varying assumptions regarding the maximum number of picks at a pick point. These results broaden those previously presented in the literature, as well as demonstrate the improved efficiency of the proposed model.1 [ABSTRACT FROM PUBLISHER]

This article discusses an order batching formulation and heuristic solution procedure suitable for a large-scale order picking situation in parallel-aisle picking systems. Order batching can decrease the total travel distance of pickers not only through reducing the number of trips but also by shortening the length of each trip. In practice, some order picking systems retrieve 500–2000 orders per hour and include ten or more aisles. The proposed heuristic produces near-optimal solutions with run times of roughly 70 s in a ten-aisle system. The quality of the solutions is demonstrated by comparing with a lower bound developed as a linear programming relaxation of the batching formulation developed in this article. A simulation study indicates that the proposed heuristic outperforms existing methods described in the literature or used in practice. In addition, the resulting order picking operations are relatively robust to picker blocking. [ABSTRACT FROM PUBLISHER]

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]

This article investigates the effect of assigning the most-active items to the best locations in unit-load warehouses with non-traditional aisles. Specifically, the performance of flying-V and fishbone designs are investigated when products exhibit different velocity profiles. Both single- and dual-command operations are considered for a warehouse where receiving and shipping are located at the midpoint of one side of the warehouse. For dual-command operations, a fishbone design shows similar reductions in travel distances for both random and turnover-based storage policies. The fishbone designs that provide the best performance have a diagonal cross aisle that extends to the upper corners of the picking space and are approximately half as tall as they are wide. In general, warehouse design parameters that perform best under random storage also perform well under turnover-based storage. [ABSTRACT FROM AUTHOR]

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]

In designing an Order Picking System (OPS) with multiple pickers, the designing (or selection) of several parameters (e.g., width of aisles, storage system and picking strategy) is dependent on the blocking that occurs between pickers. In this paper analytical models to estimate blocking in an OPS that has picking aisles wide enough to allow pickers to pass other pickers in the aisle are developed. In such OPSs, pickers can experience blocking at a pick face when two or more pickers need to pick at the same pick face. The developed models are compared with simulation, with results indicating that the proposed models are sufficiently accurate. Test results suggest that when pickers pick one SKU at a pick face, blocking is less in a wide-aisle OPS compared to that in a narrow-aisle OPS. However, when pickers pick more than one SKU at a pick face, blocking increases monotonically with an increase in the number of SKUs picked. The last result is significant since it highlights the importance of the proposed model that considers the variation in the time the picker is stopped to pick. [Supplementary materials are available for this article. Go to the publisher's online edition of IIE Transactions for the following free supplemental resources: (i) Appendix A, which describes the procedure to obtain a closed-form expression for b1(2); (ii) Appendix B, which describes the derivations of the distributions for the case when pickers pick one SKU and pick:walk time ratio is ∞:1; and (iii) Appendix C, which describes the derivations of the distributions for the case when pickers may pick more than one SKU and pick:walk time ratio is ∞:1.] [ABSTRACT FROM AUTHOR]

One of the challenging questions that online retailers are currently facing is how to organize the logistic fulfillment processes both during and after a transaction has taken place. As new information technologies become available that allow picking information to be conveyed in real time and with the ongoing need to create greater responsiveness to customers, there is increasing interest in applying dynamic picking in the warehouses of online retailers. In a Dynamic Picking System (DPS), a worker picks orders that arrive in real time during the picking operations and the picking information can dynamically change in a picking cycle. Models to describe and analyze such systems via stochastic polling theory are presented and closed-form expressions for the order line waiting times in a DPS are derived. These analytical results are verified by simulation. It is shown that the application of polling-based picking can generally lead to shorter order throughput times and higher on-time service completion ratios than traditional batch picking systems using optimal batch sizes. It is demonstrated that the proposed analysis method can be applied to minimize warehouse cost and improve service. [ABSTRACT FROM AUTHOR]

An approximation method based on G/G/m queuing network modeling using Whitt's queuing network analyzer to analyze pick-and-pass order picking systems is presented. The objective of this approximation method is to provide an instrument for obtaining rapid performance estimates (such as order lead time and station utilization) of an order picking system. The pick-and-pass system is decomposed into conveyor segments and pick stations. Conveyor segments have a constant processing time, whereas the service times at a pick station depend on the number of order lines in the order to be picked at the station, the storage policy at the station and the work practices. The proposed approximation method appears to be sufficiently accurate for practical purposes. It can be used to rapidly evaluate the effects of the storage methods in pick stations, the number of order pickers at stations, the size of pick stations, the arrival process of customer orders and the impact of batching and splitting orders on system performance. [ABSTRACT FROM AUTHOR]

The forward pick area of a distribution center is a cache of conveniently located products from which order pickers can quickly draw, but which must be replenished from bulk or reserve storage. The quantities stored forward determine the amount of work required to sustain the forward pick area. Two stocking strategies that are commonly used in industry are analyzed and compared with the optimal stocking strategy for small parts. [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]

The cost and service performance of an order fulfillment center are determined partly by how workers are organized into an order picking system. One common approach is batch picking, in which workers circumnavigate a picking area with other workers, gathering items on a pick list. In some systems with high space utilization, narrow aisles prohibit workers from passing one another when in the same aisle, and this leads to congestion. We build analytical and simulation models of these systems to investigate their behavior under different levels of activity. Among other things, our results suggest that when the system is busier and pick density is high (that is, when workers stop often to make picks) congestion is less of a problem and workers are more productive. [ABSTRACT FROM AUTHOR]

This paper describes an approach to determine a layout for the order picking area in warehouses, so that the average travel distance for the order pickers is minimized. We give analytical formulas that can be used to calculate the average length of an order picking route under two different routing policies. The optimal layout can be determined by using these formulas as the objective function in a nonlinear programming model. The optimal number of aisles in an order picking area appears to depend strongly on the required storage space and the pick list size. [ABSTRACT FROM AUTHOR]

We introduce and develop models for very high density physical goods storage systems, which are characterized by sometimes having to move interfering items in order to gain access to desired items. We describe a simple, but effective, algorithm to densely fill rectangular storage spaces, subject to a constraint on the number of interfering items. We also prove an upper bound on the storage density for any rectangular space, including traditional warehouses. [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]

The e-commerce revolution has raised awareness about the need for more efficient order fulfillment systems. Our research provides a design aid for companies engaged in order fulfillment system design, emphasizing the decision of whether or not to automate the sorting process. We have developed a descriptive model for this problem based on demand levels, labor rates, order sizes, and other factors. This model is incorporated into a simple cost-based optimization model to recommend a solution. To demonstrate the performance of the recommended system with stochastic factors, we have simulated the output of our model for several test problems. In the event that the model recommends a manual sorting system, we have provided an analytical model to aid in the design and operation of such systems by determining the optimal batching level. [ABSTRACT FROM AUTHOR]

Order picking is found at the interface of production and distribution networks within supply chain systems. In this study the order picking function is modeled to explore its role with respect to inventory deployment and centralized distribution planning, in the presence of production economies of scale and deterministic demand. The results of 5400 numerical experiments suggest that, for certain system cost structures, there may exist an inherent tension between commercially independent stages, owed to the consequences of decentralized planning. Increasing total system cost due to pull style planning over centralized planning appeared to be most heavily influenced by the proportion of costs shared by various system stages around the order picker, and this gap averaged over 30% in worst-case scenarios. [ABSTRACT FROM AUTHOR]

We study an end-of-aisle order picking system with inbound and outbound buffer positions. The system is referred to as a miniload system with a horse-shoe front-end configuration, and is modeled as a two-stage cyclic queueing system with limited capacity. We analyze the system by utilizing known queueing results. Closed form expressions are developed for system performance measures, including the steady-state probability and system throughput. To provide some decision criteria in the design of buffer capacity, we examine the effect of the buffer size on system throughput. We give examples to illustrate how the results developed can be applied to solving real world problems. It is also shown that under heavy retrieval system demand, the system naturally achieves its optimal operating condition in the long run and it always performs dual command cycles. [ABSTRACT FROM AUTHOR]

In this paper the problem of finding efficient orderpicking routes is studied for both conventional warehouses, where pickers have a central depot for picking up and depositing carts and pick lists, and modern warehouses, where orderpicking trucks can pick up and deposit pallets at the head of every aisle without returning to the depot. Such environments can be found in many warehouses where paperless picking is performed from pallet locations with pickers having mobile terminals receiving instructions one by one. In order to find orderpicking routes with a minimal length in both the situations of a central depot or decentralized depositing, we extend the well-known polynomial algorithm of Ratliff and Rosenthal [l] that considered warehouses with a central depot. In practice, the problem is mainly solved by using the so-called S-shape heuristic in which orderpickers move in a S-shape curve along the pick locations. The performance of the new algorithm and the S-shape heuristic are compared in three realistic orderpicking systems: (1) narrow-aisle high-bay pallet warehouse; (2) picking in shelf area with decentralized depositing of picked items; and (3) conventional orderpicking from wide-aisle pallet locations. The new algorithm gives a reduction in travel time per route of between 7 and 34%. It turns out that the reduction in travel time strongly depends on the lay-out and operation of the warehouse. [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, 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 considers a two-level order accumulation/sortation system and develops an algorithm to optimally assign orders to lanes based on the arrival sequence of items to the sortation system. With the use of the algorithm we show significant throughput increases, where throughput is based on the time to sort a complete order pick-wave. Examples illustrate the effectiveness of the algorithm on small and large problems. [ABSTRACT FROM AUTHOR]

In a dynamic environment, such as high-technology industry, a forward picking area requires an intelligent approach to ongoing rewarehousing (reassignment of stock items to locations). The items go through life cycles and product mix changes. Some items are common across multiple families in the product mix. Several of the items also tend to be picked in conjunction with other items, so their locations in the picking face should be made considering correlated assignments. This problem is not limited to high-technology industries, but also occurs in any setting where there are seasonalities or promotional programs, such as in retail distribution. This paper describes an approach, conceptual framework, and heuristic Dynamic Stock Location Assignment Algorithm (SLAA) for dynamic reconfiguration of the order picking system. Previous relevant research is reviewed. This paper also provides a sensitivity analysis of the algorithm and shows that it performs consistently better than the cube per order index rule at minimizing order processing time in dynamic environments with changing popularity and correlation of demand. [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]