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Order picking is one of the most costly logistics processes in warehouses. As a result, the optimization of order picking processes has received an increased attention in recent years. One potential source for improving order picking is the reduction of picker blocking. The purpose of this paper is to investigate picker blocking under different storage assignment and order picker-route combinations and evaluate its effects on the performance of manual order picking processes.

This study develops an agent-based simulation model (ABS) for order picking in a rectangular warehouse. By employing an ABS, we are able to study the behaviour of individual order pickers and their interactions with the environment.

The simulation model determines shortest mean throughput times when the same routing policy is assigned to all order pickers. In addition, it evaluates the efficiency of alternative routing policies–storage assignment combinations.

The paper implies that ABS is well-suited for further investigations in the field of picker blocking, for example, with respect to the individual behaviour of agents.

Based on the results of this paper, warehouse managers can choose an appropriate routing policy that best matches their storage assignment policy and the number of order pickers employed.

This paper is the first to comprehensively study the effects of different combinations of order picker routing and storage assignment policies on the occurrence of picker blocking.

Order picking, the activity by which a number of goods are retrieved from a warehousing system to satisfy a number of customer orders, is an essential link in the supply chain and is the major cost component of warehousing. The critical issue is to simultaneously reduce the cost and increase the speed of the order picking activity. The main objectives of this paper are: evaluate various routing heuristics and an optimal routine in a volume‐based and random storage environment; compare the performance of volume‐based storage to random storage; and examine the impact of travel speed and picking rates on routing and storage policy performance. The experimental results show the solution gap between routing heuristics and optimal routing is highly dependent on the travel speed and picking rate, the storage policy, and the size of the pick list. In addition, volume‐based storage produced significant savings over random storage, but again these savings are dependent on the travel speed and picking rate.

This paper aims to develop an approach to design a warehouse that uses class-based storage policy in a way that minimizes both space cost and material handling cost.

The authors argue for and develop an optimization model for joint determination of lane depth, lateral width and product partitions for minimizing the sum of handling and space costs. In doing so, the assumption of perfect sharing is also relaxed. Using computational experiments, the authors characterize the operating conditions based on pick density and cost ratio. The authors further outline an approach to decide the conditions under which it is advantageous to implement multiple classes.

More classes are preferred when both the pick density and cost ratio are higher and vice versa. Factors such as demand skewness, lane depth and stacking height affect the space-sharing dynamics.

The paper gives the practical insights on when the conditions under which it is advisable to partition a warehouse into a certain number of classes instead of maintaining and when to maintain as a single-class block. It also gives a method to estimate the space-sharing factor, given a combination of operating parameters.

Very few studies have seen class-based storage policy in the context of block stacked warehouse layout. Further, block stacking designs have mostly been approached with the objective of minimizing just the space cost. This study contributes to the literature by developing an integrated model, which has the practical utility.

This study is concerned with evaluating the performance of different storage policies and tour construction procedures for multi‐command picking using automated storage‐retrieval systems (AS/RS). The AS/RS machine travel time for each storage policy is derived as a function of the shape of the storage area, the number of picking points and the sequencing algorithm used. The combination of these variables yields various solutions to the problem, thus providing a useful guide in designing effective picking systems. The average travel time of the AS/RS in each configuration is calculated by computer simulation in a full factorial experimental design. A subset of the results derived from this study has been successfully implemented in a case study, involving the BMW AG Italy spare parts distribution warehouse.

A hybrid storage assignment (combination class-volume-based) framework considering quality proximity, customer and material categorization are key distinguished contents of this paper. In spite of using individual storage allocation approach, the hybrid allocation policy performs better under certain environment. The paper aims to discuss these issues.

Although it has been proved that every storage assignment policy has their advantages and limitations, one or more storage assignment policies with combination of zoning and layout design can be used together for further improvement. The authors have conducted this study at warehouse of a manufacturing firm that produce only single product with varieties of material and quality criteria. Picking optimization includes elimination of non-value-added activities like unwanted forklift and package movements, time and distance traveled for retrieval as well as storage. Other allied operations with respect to customer acceptance level and resource utilization are also considered.

The time and distance from manufacturing point to storage location are accountable as it also contributes to picking performance.

Quality-based cluster analysis is carried out to find out closeness among customers, which is used to propose algorithm with new layout design, zoning and storage allocation policy.

The retail revolution swing from traditional distribution to e-tailing services and unprecedented increase in internet adoption insist practitioners to diversely plan warehousing strategies. More than practically required storage space has been identified as wastes, and also it does not improve performance. An organized framework integrating storage design policies, operational performance and customer value improvement for retail-distribution management is lacking. Therefore, the purpose of this paper is to develop broad guidelines to design the “just-right” amount of forward area, i.e., “lean buffer” answering the following questions: “What should be lean buffer size? How effective the forward area is? As per demand variations, which storage waste (SKU) should be allocated with how much storage space? What is the amount of storage waste (SW)? How smooth the material flow is in between reserve-forward area?” for storage allocation in cosmetics distribution centers.

After forecasting static storage allocation between two planning horizons, if a particular SKU is less or non-moving, then it will cause SW, as the occupied location can be utilized by other competing SKUs, and also it impedes material flow for an instance. A dynamically efficient and self-adaptive, knapsack instance based heuristics is developed in order to make effective storage utilization.

The existing state-of-the-art under study is supported with a distribution center case, and the study investigates the need of a model adopting lean management approach in storage allocation policies along with test results in LINGO. The sensitivity analysis describes the impact of varying demand and buffer size on performance. The results are compared with uniform and exponential distributed demands, and findings reveal that the proposed heuristics improves efficiency and reduce SWs in forward-reserve area.

The presented model demonstrates a novel thinking of lean adoption in designing storage allocation strategy and its performance measures while reducing wastes and improving customer value. Future research issues are highlighted, which may be of great help to the researchers who would like to explore the emerging field of lean adoption for sustainable retail and distribution operations.

The purpose of this paper is to present a literature review on parts feeding policies and to provide the components of parts feeding systems via a classification structure.

This paper determines the scope and components of parts feeding systems via a classification structure under three main components such as the storage of parts, transport of parts and feeding policy. Afterward, it is focused on parts feeding policies and the related papers are reviewed and analyzed according to their feeding policy types, objectives, solution methodologies and the application types.

A classification structure showing the components and scope of parts feeding systems is provided. Parts feeding policies are handled in detail and feeding policy types, objectives, solution methodologies and application types in the existing studies are presented in this paper. However, the paper highlights the open research areas and advances for academics and presents applied solution methodologies and case studies for practitioners.

This paper reveals the scope of parts feeding systems by presenting a classification structure including three main components and related subcomponents and provides a comprehensive literature review on parts feeding policies.

This paper aims to propose a simulation model integrated with an empirical regression analysis to provide a new mathematical formulation for automated storage and retrieval system (AS/RS) travel time estimation under class-based storage and different input/output (I/O) point vertical levels.

A simulation approach is adopted to compute the travel time under different warehouse scenarios. Simulation runs with several I/O point levels and multiple shape factor values.

The proposed model is extremely precise for both single command (SC) and dual command (DC) cycles and very well fitted for a reliable computation of travel times.

The proposed mathematical formulation for estimating the AS/RS travel time advances widely applied methodologies existing in literature. As well as, it provides a practical implication by supporting faster and more accurate travel time computations for both SC and DC cycles. However, the regression analysis is conducted based on simulated data and can be refined by numerical values coming from real warehouses.

This work provides a new simulation model and a refined mathematical equation to estimate AS/RS travel time.

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.

Two researchers suggest a new approach to the most fundamental warehousing operation of all.