Analytical models for pick distances in fishbone warehouse based on exact distance contour

The pick distance models for a unit load warehouse employing fishbone layout conventionally use semicircular approximation for distance contour which can result in significant error. This paper develops discrete and continuous pick distance models for fishbone layout under random, full turnover, and class-based storage policies based on exact polygonal distance contour. Class-based storage policy with three classes was found to give pick distance comparable to full turnover policy over a range of demand skews and warehouse shapes studied. The discrete and continuous models are compared considering finite storage space, aisle width and discontinues in the ABC curve for a real life data. The sensitivity of warehouse performance over a range of warehouse parameters is studied. We also outline a methodology for class-based storage design where class partitions can be derived for a warehouse of any dimension from the results of a unit area warehouse.     

Analytical models for a new turnover-based hybrid storage policy in unit-load warehouses

This paper considers a low-level unit-load warehouse employing single-deep storage lanes and a single-command manual storage and retrieval policy. Analytical travel distance models are developed for class-based and full turnover storage policies under across-aisle, within-aisle and a newly proposed hybrid product placement schemes. Our computational studies show that the analytical models developed in this paper are very accurate as compared to simulation results and a comparative study with a real-world warehouse case. Hybrid storage policies proposed in this paper outperform all other traditional storage policies. The paper also presents insights and simple design rules to warehouse practitioners.     

Travel distance estimation and storage zone optimization in a 2-block class-based storage strategy warehouse

Order picking has been considered as the most critical operation in warehousing. Recent trends in logistics demand faster but more reliable order picking systems. The efficiency of an order picking process greatly depends on the storage policy used, i.e. where products are located within the warehouse. In this paper, we deal with the most popular storage policy that is class-based (or ABC) storage strategy. Particularly, we investigate the problem of determining the optimal storage boundaries (zones) of classes in each aisle for manually operated warehouses.

We first propose a probabilistic model that enables us to estimate the average travel distance of a picking tour. We found that the differences between results obtained from simulation and the model were slight. Using the average travel distance as the objective function, we present a mathematical formulation for the storage zone optimization problem. However, the exact approach can handle only small size warehouse instances. To circumvent this obstacle, we propose a heuristic for the problem. Numerical examples we conducted show that the heuristic performs very well in all the cases.     

A heuristic approach to warehouse layout with class-based storage

This paper presents a procedure for warehouse layout. It employs the principles of class-based storage to increase floor space utilization and decrease material handling. Three phases of the procedure are outlined: (1) determination of aisle layout and storage zone dimensions, (2) assignment of material to a storage medium, and (3) allocation of floor space. An industrial case study illustrates the procedure for a warehouse required to store 739 different products totaling more than 10000 pallets of material. An improved layout was developed and compared with the existing alternative. The proposed layout offered savings of more than 20 000 square feet of floor space and an approximately 45% reduction in material handling distance.     

Simulation study of an automated storage/retrieval system

In this paper we present a simulation study of an automated storage/retrieval system and examine a wide variety of control policies. We compare several storage location assignment policies. For the class-based storage policy, we apply a recent algorithm that enables us to evaluate the trade-off between storage space requirements and travel times. We also study a new storage location policy which combines low storage space requirements with short mean travel times. Furthermore, we study the sequencing of storage and retrieval requests whereby we focus on the trade-off between efficient travel of the S/R machines and response time performance.     

Experimental investigation of shared storage assignment policies in automated storage/retrieval systems

In this study, two shared storage assignment policies are compared in an Automated Storage/Retrieval System (AS/RS) by using computer simulation. The AS/RS is assumed to operate under a continuous review, order quantity, reorder point inventory policy. The average travel time of the AS/R machine for storing and retrieving products is used as the main performance measure. Sensitivity of the system to product variety, inventory replenishment lead time, and demand rate is investigated, as well as the effects of the inventory policy and the product classification technique used. Our main conclusion is that the turnover-based policy, in general, outperforms the duration of stay-based policy. However, the' difference between the performance of the two policies becomes insignificant under certain conditions.     

Improvement of AS/RS performance using design and application of common zone

Minimization of overall queue length in AS/RS is studied in this research. In general, class-based storage policy is recommended for overall AS/RS operation, but it has a rack shortage problem under demand variation. To resolve this problem, an area along the board of two classes in AS/RS racks is allocated and is named the common zone. This zone is designed to handle the rack shortage problem associated with the items in a particular class under class-based storage policy. Thirty percent common zone size is determined as the most favorable allocation among various sizes through simulation experiments. This particular size common zone policy is compared with other well-known policies such as class-based, relocation, and random storage policy. These four operation policies under three different levels of workloads with demand variations are simulated. Crane moving time, rack shortages, delay times as well as throughputs are checked for the policy evaluation.     

Dual command travel times and miniload system throughput with turnover-based storage

We analyze travel times for automated storage/retrieval systems. In particular, we apply our travel time model to turnover-based storage systems and determine the mean and variance of dual command travel times. We present detailed numerical results for selected rack shape factors and ABC inventory profiles. We then investigate the effect of alternative rack configurations on travel time performance measures. We also show how to determine the throughput of miniload systems with turnover-based storage and exponentially distributed pick times.     

Designing an optimal turnover-based storage rack for a 3D compact automated storage and retrieval system

Compact, multi-deep (3D) automated storage and retrieval systems (AS/RS) are becoming increasingly popular for storing products. We study such a system where a storage and retrieval (S/R) machine takes care of movements in the horizontal and vertical directions of the rack, and an orthogonal conveying mechanism takes care of the depth movement. An important question is how to layout such systems under different storage policies to minimize the expected cycle time. We derive the expected single-command cycle time under the full-turnover-based storage policy and propose a model to determine the optimal rack dimensions by minimizing this cycle time. We simplify the model, and analytically determine optimal rack dimensions for any given rack capacity and ABC curve skewness. A significant cycle time reduction can be obtained compared with the random storage policy. We illustrate the findings of the study by applying them in a practical example.     

Using dynamic demand information and zoning for the storage of non-uniform density stock keeping units

The warehouse order-picking operation is one of the most labour-intense activities that has an important impact on responsiveness and efficiency of the supply chain. An understanding of the impact of the simultaneous effects of customer demand patterns and order clustering, considering physical restrictions in product storage, is critical for improving operational performance. Storage restrictions may include storing non-uniform density stock keeping units (SKUs) whose dimensions and weight constrain the order-picking operation given that a priority must be followed. In this paper, a heuristic optimisation based on a quadratic integer programming is employed to generate a layout solution that considers customer demand patterns and order clustering. A simulation model is used to investigate the effects of creating and implementing these layout solutions in conjunction with density zones to account for restrictions in non-uniform density SKUs. Results from combining layout optimisation heuristics and density zoning indicate statistical significant differences between assignments that ignore the aforementioned factors and those that recognise it.     

An evaluation of routing policies for order-picking operations in low-level picker-to-part system

This paper evaluates the performance of three routing policies in the order-picking process, i.e. return, traversal, and midpoint policy. It is assumed that items are assigned to storage locations on the basis of the cube-per-order index (COI) rule in a low-level picker-to-part warehousing system. First, for the three policies, analytical models are developed for the total expected travel distance of the order picker considering the number of the stocking aisles is even or odd. Then the developed models are compared with simulation results to show the validity. Finally, the performance of the three policies is examined by varying the parameter value of the COI-based ABC curve, number of picks in the pick list, and ratio of the length to the width of the warehouse.     

Data mining-based algorithm for storage location assignment in a randomised warehouse

Data mining has long been applied in information extraction for a wide range of applications such as customer relationship management in marketing. In the retailing industry, this technique is used to extract the consumers buying behaviour when customers frequently purchase similar products together; in warehousing, it is also beneficial to store these correlated products nearby so as to reduce the order picking operating time and cost. In this paper, we present a data mining-based algorithm for storage location assignment of piece picking items in a randomised picker-to-parts warehouse by extracting and analysing the association relationships between different products in customer orders. The algorithm aims at minimising the total travel distances for both put-away and order picking operations. Extensive computational experiments based on synthetic data that simulates the operations of a computer and networking products spare parts warehouse in Hong Kong have been conducted to test the effectiveness and applicability of the proposed algorithm. Results show that our proposed algorithm is more efficient than the closest open location and purely dedicated storage allocation systems in minimising the total travel distances. The proposed storage allocation algorithm is further evaluated with experiments simulating larger scale warehouse operations. Similar results on the performance comparison among the three storage approaches are observed. It supports the proposed storage allocation algorithm and is applicable to improve the warehousing operation efficiency if items have strong association among each other.     

Heuristic algorithms for a storage location assignment problem in a chaotic warehouse

The extensive application of emerging technologies is revolutionizing warehouse management. These technologies facilitate working with complex and powerful warehouse management models in which products do not have assigned fixed locations (random storage). Random storage allows the utilization of the available space to be optimized. In this context, and motivated by a real problem, this article presents a model that looks for the optimal allocation of goods in order to maximize the storage space availability within the restrictions of the warehouse. For the proposed model a construction method, a local search algorithm and different metaheuristics have been developed. The introduced algorithms can also be used for other purposes such as to assess when and how it is convenient to perform relocation of stored items to improve the current level of storage space availability. Computational tests performed on a set of randomly generated and real warehouse instances show the effectiveness of the proposed methods.     

Order picking system design: the storage assignment and travel distance estimation (SA&TDE) joint method

Of all the warehouse activities, order picking is one of the most time-consuming and expensive. In order to improve the task, several researches have pointed out the need to consider jointly the layout of the warehouse, the storage assignment strategy and the routing policy to reduce travelled distances and picking time. This paper presents the storage assignment and travel distance estimation (SA&TDE) joint method, a new approach useful to design and evaluate a manual picker-to-parts picking system, focusing on goods allocation and distances estimation. Starting from a set of picking orders received in a certain time range, this approach allows to evaluate the combinations of product codes assigned to storage locations, aisles, sections or warehouse areas and to assess the most relevant ones, for the best location and warehouse layout, with the aim of ensuring optimal picking routes, through the application of the multinomial probability distribution. A case study is developed as well, in order to clarify the concept that underlies the SA&TDE joint method, and to show the validity and the flexibility of the approach, through the calculation of the saving at different levels of detail.     

A storage assignment policy in a man-on-board automated storage/retrieval system

This paper deals with a man-on-board automated storage/retrieval system where each customer order consists of a number of different items and is picked one at a time. For the system the problem examined is to allocate storage locations dedicatedly to items so that the total travel time required to pick all the given orders per period is minimized. The problem is shown to be formulated as a variant of the generalized assignment model. A heuristic for the storage layout problem is developed based on the group technology concept considering both order structure and frequency. Through the heuristic, close relationships between items are identified from the order structure, and then based on the relationships the items are compelled to be stored closely in the storage rack following a space-filling curve. Experimental results are provided to describe the performance of the heuristic     

The impact of dwell point policy in an Automated Storage/Retrieval System

Automated storage and retrieval systems (AS/RS) are devices that allow intensive storage of materials. They can improve the supply chain performance, assuring more available volume for storage, lower labour costs and higher handling throughput of warehousing. Furthermore, the automated control allows the probability of errors in storage and retrieval to be minimised, along with the probability of product damage during movements. The purpose of the paper is to find the best solution in order to find the optimal dwell point policy, among different rules, able to minimise the travel time and distance travelled by stacker cranes, and consequently warehousing costs. An original and innovative model was developed in order to investigate the dwell point position for random allocation of unit loads. A software platform was developed to validate the proposed model by computer simulations. The performance of the system was analysed in a parametric/continuous way, varying at the same time the number of spans and levels, the height of the input/output point and the interval between requested missions. The results show that the developed model allowed convenience areas to be identified among the policies in which the travel time, distance travelled, and consequently warehousing costs are minimised, by varying different parameters.     

A model for warehouse layout

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.     

Routing methods for warehouses with multiple cross aisles

This paper considers routing and layout issues for parallel aisle warehouses. In such warehouses order pickers walk or drive along the aisles to pick products from storage. They can change aisles at a number of cross aisles. These cross aisles are usually located at the front and the back of the warehouse, but there can also be one or more cross aisles at positions in between. We describe a number of heuristics to determine order picking routes in a warehouse with two or more cross aisles. To analyse the performance of the heuristics, a branch-and-bound algorithm that generates shortest order picking routes is used. Performance comparisons between heuristics and the branch-and-bound algorithm are given for various warehouse layouts and order sizes. For the majority of the instances with more than two cross aisles, a newly developed heuristic appears to perform better than the existing heuristics. Furthermore, some consequences for layout are discussed. From the results it appears that the addition of cross aisles to the warehouse layout can decrease the handling time of the orders by lowering average travel times. However, adding a large number of cross aisles may increase average travel times because the space occupied by the cross aisles has to be traversed as well.     

Simultaneous determination of warehouse layout and control policies

In a supply chain’s order fulfilment process, it is often the warehouse that plays a central role in making the right product available to the right customer at the right time. This paper aims to improve warehouse performance by deriving an effective design method for the simultaneous determination of warehouse layout and the warehouse’s control policies. The authors consider layout variables for the warehouse such as the number of primary aisles utilised, the number of cross-aisles and the aisle length under several different design philosophies. Concurrently, control policies such as storage policies and routing are considered. Simulation is utilised to determine the performance of the various resulting scenarios. A screening and selection procedure is employed to reduce the required number of replications while achieving a predetermined precision in identifying the best configuration. The approach is applied for an industrial partner in this research and the results of experimentation are compared to a baseline scenario which describes a proposed new facility in the Netherlands. The results reveal a large potential for performance improvement.     

基于数据挖掘的动态货位指派系统

目的为提高需求快速变化、波动较大的在线零售企业的仓库货位优化效率。方法利用数据仓库和数据挖掘法,研究基于复合规则的动态货位指派策略。对该货位指派的数据集成分析、指标计算、规则生成和货位指派等4个模块进行分析,并设计库区标定算法和规则生成算法来生成货位指派规则集。结果基于复合规则动态货位指派不仅能够节约拣货距离,而且拣货效率受需求变化的影响非常小。结论数值实验表明,与传统的货位指派策略相比,基于复合规则动态货位的指派系统可以得到更好的结果,并且在平均订单规模较大和需求偏度大的情况下效果更加明显。