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Order picking in warehouses is a labour- and time-intensive logistical process that significantly impacts the efficiency of supply chains. Although technical progress facilitates the automation of specific order picking tasks, human workers remain the primary actors of order picking. Owing to high operating costs associated with manual order picking, its design and management have been increasingly researched for decades. Because manual order picking systems are socio-technical systems, human factors and workers' interaction with technology are essential for operational success. As innovative technologies become increasingly utilised, such as augmented reality or exoskeletons, warehouse managers need to consider the effects of supportive and substitutive technologies on operational outcomes. However, the potentials and obstacles of using technologies in manual order picking require further investigations. Therefore, this study analyses literature content on supportive and substitutive technologies in manual warehouse order picking and investigates the existing state of research in this field. Text mining is employed to enhance the insights regarding the content analysis. Additionally, future research opportunities on the integration of supportive and substitutive technologies are proposed for manual order picking improvement and development of sustainable and human-centered logistics systems, according to the Industry 5.0 vision. [ABSTRACT FROM AUTHOR]

In the e-commerce era, efficient order fulfillment processes in distribution centers have become a key success factor. One novel technology to streamline these processes is robot-assisted order picking. In these systems, human order pickers are supported by autonomous mobile robots (AMRs), which carry bins for collecting picking orders, autonomously move through the warehouse, and wait in front of a shelf containing a requested stock keeping unit (SKU). Once a picker has approached a waiting AMR and placed the requested SKU into the respective bin, AMR and picker may separate and move toward other picking positions. In this way, pickers continuously move between different waiting AMRs without having to return to the depot. This paper treats the coordination of multiple AMRs and multiple pickers to minimize the makespan. We present a heuristic method for the deterministic case that can handle the requirements of large e-commerce fulfillment centers and successfully solves instances with more than one thousand picking positions. Based on the obtained solutions, the performance of our picking system is compared with the traditional warehouse setup without AMR support and to another work policy using fixed pairings of picker and AMR per order. We find that largely improved makespans can be expected. In addition, we analyze the effects of stochastic picking times, speed differences between AMRs and pickers, and a zoning strategy. The ripple effect caused by stochastic picking times, in which a single delay may cascade through a tightly synchronized schedule and deteriorate picking performance, can be effectively mitigated by separating the workforce into smaller subgroups. Another important finding is that pickers and AMR should have approximately the same travel speed because slower AMRs deteriorate system performance. Finally, zoning slightly decreases the flexibility of the system and should be used if dictated by organizational reasons. History: This paper has been accepted for the Transportation Science Special Issue on Emerging Topics in Transportation Science and Logistics. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.1207. [ABSTRACT FROM AUTHOR]

The problem of order and rack assignment to picking stations (ORAPS) is a key joint optimization problem in the order picking process of the robotic mobile fulfillment system (RMFS). Given a set of customer orders and a set of mobile racks, the goal of this problem is to simultaneously assign orders and racks to multiple picking stations so that the racks can provide products to meet the demand of orders by the minimal number of visits to all picking stations. In this article, we build a mathematical model for the ORAPS, which considers all orders assignment with the allocable capacity interval of the picking station and the rack product capacity limitation. To solve the ORAPS problem, we propose a two-stage hybrid heuristic algorithm (TS-HHA) consisting of the reducing stage and the assigning stage. In the reducing stage, a scheme of dynamic programming (DP) is introduced to find a critical rack set, which can focus attention on the most promising racks and increase the speed of problem-solving. In the assigning stage, we propose an optimization strategy that combines a constructive heuristic algorithm and adaptive neighborhood search (CH-ANS). It can generate a high-quality simultaneous assignment scheme and further improve its quality effectively. The computational results show that our proposed algorithm performs better than its competitors on both simulation and practical instances of the ORAPS problem. Note to Practitioners—This article investigates the order and rack assignment to picking stations (ORAPS) problem in the robotic mobile fulfillment system (RMFS) originated from a renowned Chinese logistics platform. We build a more comprehensive and reasonable mathematical programming model for this ORAPS problem and propose a two-stage hybrid heuristic algorithm (TS-HHA) strategy, which can simultaneously assign orders and racks to multiple picking stations so that all customer orders can be processed. The experimental results show that our TS-HHA performs at least 35% better than its competitors on all simulation and practical instances set. We believe that this research work can serve as a kind of generic framework for practical ORAPS problems and be very helpful for operating the RMFS efficiently. [ABSTRACT FROM AUTHOR]

Warehouse order picking activities are among the ones that impact the most the bottom lines of warehouses. They are known to often account for more than half of the total warehousing costs. New practices and innovations generate new challenges for managers and open new research avenues. Many practical constraints arising in real-life have often been neglected in the scientific literature. We introduce, model and solve a rich order picking problem under weight, fragility and category constraints, motivated by our observation of a real-life application arising in the grocery retail industry. This difficult warehousing problem combines complex picking and routing decisions under the objective ofminimising the distance travelled. We first provide a full description of the warehouse design which enables us to algebraically compute the distances between all pairs of products. We then propose two distinct mathematical models to formulate the problem. We develop five heuristic methods, including extensions of the classical largest gap,mid-point, S-shape and combined heuristics. The fifth one is an implementation of the powerful adaptive large neighbourhood search algorithm specifically designed for the problem at hand. We then implement a branch-and-cut algorithm and cutting planes to solve the two formulations. The performance of the proposed solution methods is assessed on a newly generated and realistic test bed containing up to 100 pickups and 7 aisles. We compare the bounds provided by the two formulations. Our in-depth analysis shows which formulation tends to perform better. Extensive computational experiments confirm the efficiency of the ALNS metaheuristic and derive some important insights for managing order picking in this kind of warehouses. [ABSTRACT FROM AUTHOR]

Humans are at the heart of crucial processes in warehouses. Besides the common economic goal of minimising cycle times, we therefore add in this paper the human well-being goal of minimising workers' discomfort in the context of order picking. We propose amethodology for identifying the most suitable storage location solutions with respect to both goals. The first step in our methodology is to build data-driven empirical models for estimating cycle times and workers' discomfort. The second step of the methodology entails the use of these empirically grounded models to formulate a bi-objective assignment problem for assigning products to storage locations. The developedmethodology is subsequently tested on two actual warehouses. The results of these practical tests show that clear trade-offs exist and that optimising only for discomfort can be costly in terms of cycle time. Based on the results, we provide practical guidelines for taking storage assignment decisions that simultaneously address discomfort and travel distance considerations. [ABSTRACT FROM AUTHOR]

To reduce unproductive picker walking in traditional picker-to-parts warehousing systems, automated guided vehicles (AGVs) are used to support human order pickers. In an AGV-assisted order-picking system, each human order picker is accompanied by an AGV during the order-picking process. AGVs receive the picked items and, once a picking order is complete, autonomously bring the collected items to the shipping area. Meanwhile, a new AGV is requested to meet the picker at the first storage position of the next picking order. Thus, the picker does not have to return to a central depot and continuously picks order after order. This paper addresses both the routing of an AGV-assisted picker through a single-block, parallel-aisle warehouse and the sequencing of incoming orders. We present an exact polynomial time routing algorithm for the case of a given order sequence, which is an extension of the algorithm of Ratliff and Rosenthal [Ratliff HD, Rosenthal AS (1983) Order-picking in a rectangular warehouse: A solvable case of the traveling salesman problem. Oper. Res. 1(3):507–521], and a heuristic for the case in which order sequencing is part of the problem. In addition, we investigate the use of highly effective traveling salesman problem (TSP) solvers that can be applied after a transformation of both problem types into a standard TSP. The numerical studies address the performance of these methods and study the impact of AGV usage on picker travel: by using AGVs to avoid returns to the depot and by sequencing in (near-) optimal fashion, picker walking can be reduced by about 20% compared with a traditional setting. Sharing AGVs among the picker workforce enables a pooling effect so that, in larger warehouses, only about 1.5 AGVs per picker are required to avoid picker waiting. Summary of Contribution: New technologies, such as automatic guided vehicles (AGVs) are currently considered as options to increase the efficiency of the order-picking process in warehouses, which is responsible for a large part of operational warehousing costs. In addition, picker-routing decisions are more and more often based on algorithmic decision support because of their relevance for decreasing unproductive picker walking time. This paper addresses both aspects and investigates routing algorithms for AGV-assisted order picking in parallel-aisle warehouses. We present a dynamic programming routine with polynomial runtime to solve the problem variant in which the sequence of picking orders is fixed. For the variant in which this sequence is a decision, we show that the problem becomes NP-hard, and we propose a greedy heuristic and investigate the use of state-of-the-art exact and heuristic traveling salesman problem solution methods to address the problem. The numerical studies demonstrate the effectiveness of the algorithms and indicate that AGV assistance promises strong improvements in the order-fulfillment process. Because of the practical relevance of AGV-assisted order picking and the presented algorithmic contributions, we believe that the paper is relevant for practitioners and researchers alike. [ABSTRACT FROM AUTHOR]

Order pickers and individual differences between them could have a substantial impact on picking performance, but are largely ignored in studies on order picking. This paper explores the role of individual differences in picking performance with various picking tools (pick by voice, RF-terminal picking and pick to light) and methods (parallel, zone and dynamic zone picking). A unique realistic field experiment with 101 participants (academic students, vocational students and professional pickers) is employed to investigate the influence of individual differences, especially the Big Five personality traits, on picking performance in terms of productivity and quality. The results suggest that (PbV) performs better than RF-terminal picking, and that Neuroticism, Extraversion, Conscientiousness and the age of the picker play a significant role in predicting picking performance with voice and RF-terminals. Furthermore, achieving higher productivity appears to be possible without sacrificing quality. Managers can increase picking performance by incorporating the insights in assigning the right pickers to work with a particular picking tool or method, leading to increased picking performance and reduced warehousing costs. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

Order picking has often been considered as one of the most labor- and time-intensive tasks in warehouse operations. Among the various planning problems that have to be solved in manual picker-to-parts systems, the routing of the order picker usually accounts for the highest share of the total warehouse operating cost. To minimize the cost of order picking, researchers have developed various routing procedures that guide the order picker through the warehouse to complete given customer orders. For some warehouse layouts such as the chevron warehouse, an optimal routing algorithm has not yet been proposed. This article, therefore, contributes to filling this research gap by developing an optimal order picker routing policy for the chevron warehouse. The optimal routing algorithm proposed in this article is based on the concept of graph theory and utilizes a dynamic programming procedure. In addition, we propose various simple routing heuristics for the chevron warehouse. In computational experiments, the average order picking tour lengths resulting from optimal routing and from the simple heuristics are compared. Moreover, we compare the performance of the chevron warehouse to the conventional two-block warehouse under various conditions using the tour lengths obtained by the optimal algorithms. [ABSTRACT FROM AUTHOR]

Put‐to‐light order picking systems invert the basic logic of conventional picker‐to‐parts systems. Instead of successively visiting the storage positions of the stock keeping units (SKUs) when collecting picking orders, an order picker accompanies successive bins each containing multiple items of a specific SKU along a lane of subsequent orders. Whenever the picker passes an order requiring the current SKU, which is indicated by a light signal, she puts the requested number of items into the bin associated with the order. Such an order picking system is well‐suited if the assortment is not overly large and all orders demand similar SKUs, so that it is mainly applied in distribution centers of brick‐and‐mortar retail chains. This paper evaluates four different setups of put‐to‐light systems, which, during operations, require the solution of different storage assignment and SKU sequencing problems. We formulate these problems, prove computational complexity, and suggest suited solution algorithms. By applying these algorithms in a comprehensive computational study, we benchmark the impact of the four different setups on picking performance. In this way, warehouse managers receive decision support on how to set up their put‐to‐light systems. [ABSTRACT FROM AUTHOR]

The order picking process involves a series of activities in response to customer needs, such as the selection or programming of orders (batches), and the selection of different items from their storage location to shipment. These activities are accomplished by a routing policy that determines the picker sequence for retrieving the items from the storage location. Therefore, the order picking problem has been plenty investigated; however, in previous research, the proposed models were based on demand fulfilling, putting aside factors such as the product weight--which is an important criterion--at the time of establishing routes. In this article, a mathematical model is proposed; it takes into account the product's weight derived from a case study. This model is relevant, as no similar work was found in the literature that improves the order picking by making simultaneous decisions on the storage location assignment and the picker-routing problem, considering precedence constraints based on the product weight and the characteristics of the case study, as the only location for each product in a warehouse with a general layout. [ABSTRACT FROM AUTHOR]

In this study we consider a pick-and-sort order picking system, in which batches of orders are picked simultaneously from different (work) zones by a group of order pickers. After picking, the orders are transported by a conveyor to a next station for consolidation and packing. Packing can only occur when an order has been picked completely. For a given number of workers, each assigned to a single zone, a larger number of zones reduces pick time (since travel time reduces), but increases waiting time for completion at the packing stations, because more partial batches needing assembly arrive at the packing stations. Our aim is to determine the optimal number of zones such that the total (picking and packing) time to complete a batch is minimised. We solve this problem by optimally assigning items to pick routes in each zone. We illustrate the method with data taken from a distribution centre of one of the largest online retailers in The Netherlands. [ABSTRACT FROM AUTHOR]

Increasing productivity and reducing labour cost in order picking processes are two major concerns for most warehouse managers. Particularly picker-to-parts order picking methods lead to low productivity as order pickers spend much of their time travelling along the aisles. To enhance order picking process performance, an increasing number of warehouses adopt the concept of dynamic storage where only those products needed for the current order batch are dynamically stored in the pick area, thereby reducing travel time. Other products are stored in a reserve area. We analyse the stability condition for a dynamic storage system with online order arrivals and develop a mathematical model to derive the maximum throughput a DSS can achieve and the minimum number of worker hours needed to obtain this throughput, for order picking systems with a single pick station. We discuss two applications of dynamic storage in order picking systems with multiple pick stations in series. In combination with simulation modelling, we are able to demonstrate that dynamic storage can increase throughput and reduce labour cost significantly. [ABSTRACT FROM AUTHOR]

This paper investigates the order-batching problem both as a theoretical problem, including its special variants, and as a practical problem, with possible heuristic solution procedures. It shows that certain special cases or variants of the order batching problem are solvable, while other cases of the problem are NP-hard. It also develops a new Mixed-Integer Programming (MIP) model to obtain near-exact solutions to the problem. The MIP provides good lower and upper bounds to the problem, which prove useful in the investigation of the heuristic solution procedures. The paper identifies and modifies slightly the order batching heuristic, which shows the strongest results in the numerical experiments. Although good solutions are obtained with this particular heuristic, the results indicate that it is computationally intensive. Furthermore, solutions obtained through the MIP model suggest there is still room to obtain even better solutions to the problem via heuristic procedures, although the challenge is to do so without further increasing the computational burden. [ABSTRACT FROM AUTHOR]

In this paper, we consider a newly designed compact three-dimensional automated storage and retrieval system (AS/RS). The system consists of an automated crane taking care of the pallets' movements in the horizontal and vertical direction. A gravity or powered conveying mechanism takes care of the pallets' depth movement in the rack. Our research objective is to analyze the system performance and optimally dimension the system. For single-command cycles, the crane's expected retrieval travel time is the same for gravity and powered conveyors; we give a closed-form expression. From the expected travel time, we calculate the optimal ratio between three dimensions that minimizes the travel time for a random storage strategy. In addition, we derive an approximate travel time expression for dual command cycles for the system with powered and gravity conveyors, respectively, and use it to optimize the system dimensions. Finally, we illustrate the findings of the study by a practical example. [ABSTRACT FROM AUTHOR]

Companies have been trying continuously to reduce their logistics costs in the current competitive markets. Warehouses which are important components of the logistics systems draw attention in accordance with cost reducing efforts. Warehouse management is becoming more critical, as storage and distribution of the ordered items to customers is a cost-intensive process for the companies, and this process affects the customer service level directly. In warehouse order picking systems, order batching problem is encountered when several orders are demanded, and this paper reveals recent developments and particular trends for the order batching problem. This study aims to give a literature review on order batching operations in the framework of order picking process. It also displays current position and importance of the problem among warehouse operations, and presents future research directions based on key findings. The paper examines several research papers and existing reviews about order batching and order picking. Papers are selected after a systematic approach with predetermined search strategy, classified and presented with important characteristics under main headings. [ABSTRACT FROM AUTHOR]

Paper presents analysis of the order picking system efficiency under congestion situations. Analysed picking system is manual and organized as picker-to-part system with very narrow aisles. About 150 different variants of the system was taken into consideration. Each of them is characterized by different storage policies (random, class-based and volume-based), number of order pickers and picking strategies (single picking and batch picking). Main attention was paid to picker blocking phenomenon and its impact on order picking efficiency. Authors explain where, when and why such situations may happen. Additionally, they point out how the number of pickers, storage policy and picking strategy impact pickers blocking and picking process efficiency. Due to complexity of presented issue, dynamics and volatility of picking processes, simulation method was implemented. [ABSTRACT FROM AUTHOR]

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]

Purpose – The purpose of this paper is to study the prevalence of human learning in the order picking process in an experimental study. Further, it aims to compare alternative learning curves from the literature and to assess which learning curves are most suitable to describe learning in order picking. Design/methodology/approach – An experimental study was conducted at a manufacturer of household products. Empirical data was collected in the order picking process, and six learning curves were fitted to the data in a regression analysis. Findings – It is shown that learning occurs in order picking, and that the learning curves of Wright, De Jong and Dar-El et al. and the three-parameter hyperbolic model are suitable to approximate the learning effect. The Stanford B model and the time constant model led to unrealistic results. Practical implications – The results imply that human learning should be considered in planning the order picking process, for example in designing the layout of the warehouse or in setting up work schedules. Originality/value – The paper is the first to study learning effects in order picking systems, and one of the few papers that use empirical data from an industrial application to study learning effects. [ABSTRACT FROM AUTHOR]

Abstract: We develop discrete time models for the throughput time distribution of orders arriving to a one-block warehouse. The models accommodate single- or multi-line orders, and we show how to use them to determine the optimal batch size, given a desired probability of on-time order fulfillment. Experiments suggest that the optimal batch size is slightly higher than one would choose if minimizing average throughput time. [Copyright &y& Elsevier]

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]

Among distribution centre operations, order picking has been reported to be the most labour-intensive activity. Sophisticated storage assignment policies adopted to reduce the travel distance of order picking have been explored in the literature. Unfortunately, previous research has been devoted to locating entire products from scratch. Instead, this study intends to propose an adaptive approach, a Data Mining-based Storage Assignment approach (DMSA), to find the optimal storage assignment for newly delivered products that need to be put away when there is vacant shelf space in a distribution centre. In the DMSA, a new association index (AIX) is developed to evaluate the fitness between the put away products and the unassigned storage locations by applying association rule mining. With AIX, the storage location assignment problem (SLAP) can be formulated and solved as a binary integer programming. To evaluate the performance of DMSA, a real-world order database of a distribution centre is obtained and used to compare the results from DMSA with a random assignment approach. It turns out that DMSA outperforms random assignment as the number of put away products and the proportion of put away products with high turnover rates increase. [ABSTRACT FROM AUTHOR]

In today's competitive scenario of increasingly faster deliveries and smaller order sizes, a solution that is being used more frequently is the pick-and-pass Order Picking System (OPS). The purpose of this study is to define a framework for the pick-and-pass system design, by expanding previous literature on this topic. The framework aims at minimising the overall picking costs while respecting the required service level (i.e. order throughput time), and can be easily applied to the selection stage of OPS design. The number of zones and the number of pickers per zone are among the main project data for the framework application. Analytical models are used to estimate the travel distance, and network queuing theory to analyse the mean order throughput. On the basis of the proposed framework, pick-and-pass system performance is examined as a function of two typical design conditions: order size and number of items. [ABSTRACT FROM AUTHOR]

Many warehouses store at least some goods in two areas, a reserve area that is efficient for storage and a forward area that is efficient for order picking. The forward-reserve allocation problem determines the set of Stock-Keeping Units and their space allocations in the forward area to maximize the forward area's benefit by trading off the relevant costs of order picking and internal replenishment. The mathematical model of this decision resembles the classical knapsack problem with the additional complexity that it has a discontinuous nonlinear cost function. A simple greedy heuristic has been proposed in the literature to solve this problem. This paper proposes an alternative branch-and-bound algorithm that can quickly solve the problem to optimality. Heuristic and optimal solutions are numerically compared using problem instances based on real warehouse data. Results suggest that the heuristic solutions are very close to the optimal ones in terms of both the objective value and the forward assignment. [ABSTRACT FROM AUTHOR]

Numerous design and cost parameters, combined with an endless variety of equipment types, make it difficult to choose the right order picking system (OPS). The purpose of this study is to develop a methodology to support warehouse designers in choosing the most suitable OPS. By developing a new OPS classification, we carried out an in-depth survey on over 68 distribution centres that have been recently built in Italy. The results of the critical analysis allowed developing a design methodology to choose the most suitable OPS. This methodology has been integrated in the structured procedure for OPS design, developed by Yoon and Sharp ( IIE Trans 28:379–389, ). Finally, a numerical case study is presented to illustrate the application of the proposed design methodology. [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]

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 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]

In today's competitive global economy, the focus is on faster delivery of small more frequent orders of inventory at a lower total cost This often precludes the use of full pallet picking in warehouses so firms commonly use manual picking of cases and broken-cases. Many firms increase the efficiency of their warehouses by using zone picking Zone picking requires that a worker only pick those stock-keeping units (SKUs) stored within their picking zone. In this paper we examine the configuration or shape of these picking zones by simulating a bin-shelving warehouse to measure picker travel where SKUs are assigned storage locations either using random or volume-based storage. The results show that the size or storage capacity of the zone, the number of items on the pick list, and the storage policy have a significant effect on picking zone configuration. In addition, we found that the absence of a back cross aisle also affected picking zone configuration. These results offer solutions to managers looking to implement improvements in distribution center operations. [ABSTRACT FROM AUTHOR]