Class-based Storage Policy Research Articles

The single picker routing problem with scattered storage: modeling and evaluation of routing and storage policies

Despite ongoing automation efforts, most warehouses are still manually operated using a person-to-parts collection strategy. This process of collecting items of customer orders from different storage locations accounts for the majority of the operating costs of the warehouse. Hence, optimizing picker routes is an important instrument to reduce labor costs. We examine the scattered-storage variant of the single picker routing problem in a one-block parallel-aisle warehouse. With scattered storage, an article can be stored at several storage locations within the warehouse, whereas with classic storage, each article has a unique storage location. We use our recently published network-flow model with covering constraints that is based on an extension of the state space of the dynamic-programming formulation by Ratliff and Rosenthal. With modifications in the state graph, this model serves for both exact and all established heuristic routing methods for picker routing. The latter include traversal, return, largest gap, midpoint, and composite. We show that these routing policies can also be implemented through adaptations in the state space. Extensive computational studies highlight a comparison of the different routing and storage policies (in particular class-based storage policies) in the scattered storage context. Analyses demonstrate which combinations of policies are advantageous for the given warehouse layout. For class-based storage policies, we emphasize how the scattering of articles of different classes should be performed: scattering of C-articles is advantageous with reductions of up to 25%. In contrast, when articles are uniformly distributed, A-articles should be scattered.

Comparison of Expected Distances in Traditional and Non-Traditional Layouts

The performance of a unit-load warehouse depends on numerous parameters such as storage area, layout, aisle configuration, width-to-depth ratio, the number and locations of dock doors, storage policy, etc. These parameters typically relate to the layout configuration, which can be either traditional (rectangle-shaped) or non-traditional (contour-line-shaped). In this paper, we analyze the performance of rectangle-shaped and contour-line-shaped storage areas within a unit-load warehouse having multiple dock doors. Expected distances traveled in rectangle-shaped storage areas are compared with expected distances in their counterpart contour-line-based storage areas when an ABC class-based storage policy is used to assign unit-loads. For a single product class, the expected-distance for a rectangle-shaped storage area is at most 6.07% greater than it is for the corresponding contour-line-shaped storage area. Depending on the skewness of the ABC curve or storage areas for multiple classes, the expected distance for rectangle-shaped storage areas can be no more than 0.59% greater than it is for the corresponding contour-line shaped storage areas when multiple dock doors are distributed with a specified distance between them.

Modelling and performance analysis of shuttle-based compact storage systems under different storage policies

<p style='text-indent:20px;'>Short response time for order processing is important for modern warehouses, which can be potentially achieved by adopting appropriate storage policy. This paper models and analyses shuttle-based compact storage systems under random and class-based storage policies using a probability and queueing based approach. The ABC curve and the basic Economic Order Quantity model are used to determine the assignment of storage locations under class-based storage policy. The performance measures are obtained by using an iterative approach based on parametric-decomposition. The analytical model is validated against simulations and the results show our model can accurately estimate the system performance. Numerical experiments based on a real case are carried out. The results show that the best performance is likely to be provided by a class-based storage policy with a steep ABC curve and a skewed demand rate distribution. Multiple system configurations in terms of number of tiers, depth/width ratio, size of storage positions and velocity of system resources are also analyzed. The results suggest benefits of class-based storage policy regardless of the number of tiers. Moreover, the class-based storage policy outperforms the random storage policy in the system with a small depth/with ratio. However, the random storage policy is recommended for the system with deep storage lanes.</p>

Stochastic models of routing strategies under the class-based storage policy in fishbone layout warehouses

In order to improve the picking efficiency of warehouses, shorten the time cost and promote the development of the logistics industry, this study analyzes the routing strategies in fishbone layout warehouses under the class-based storage strategy. The fishbone layout was divided into three storage areas for class A, class B, and class C items according to the proportion using the straight line, to meet the classification requirements of items. Under the class-based storage strategy, to evaluate the performance of the return routing strategy and the S-shape routing strategy, the stochastic models of the expected walking distance of the two routing strategies in the fishbone layout warehouse are established by calculating the sum of the expected walking distances in diagonal cross-aisles and picking aisles. Finally, the stochastic models of the two routing strategies are simulated and verified, and the impacts of the two routing strategies on walking distances are analyzed by comparing the expected distances under different ordering frequencies and space allocation strategies. The numerical results show that the return routing strategy has an advantage over the S-shape routing strategy when determining the relevant parameters of the fishbone layout and picking orders. Meanwhile, it also provides a theoretical basis for research on stochastic models of routing strategies in fishbone layout warehouses under the class-based storage strategy.

An analytical approach for a performance calculation of shuttle-based storage and retrieval systems with multiple-deep and class-based storage

ABSTRACT This paper presents a method for determining the performance of shuttle-based storage and retrieval systems (SBS/RS) with tier-captive, single-aisle shuttles serving tiers of multiple-deep storage and using a class-based storage policy. This approach is used in the design process of SBS/RS and in the upgrading process of existing SBS/RS. With this approach, it is possible to evaluate the improvement in the performance of multiple-deep storage system by applying a class-based storage policy. The basis of this calculation method is a continuous-time, open-queueing system with limited capacity. The cycle times of lifts and shuttles, as determined by a spatial value approach, combined with a probability-based approach to mention the storage policy. To take the multiple-deep storage into account, another probability-based approach is applied. A European material handling provider had given the data used in this publication. Through an example, the influences of the storage policy and the storage depth are depicted.

Travel time models for tier-to-tier SBS/RS with different storage assignment policies and shuttle dispatching rules

This paper explores both random and class-based storage policies and three shuttle dispatching rules, random, distance-based, and demand-rate based, for a tier-to-tier shuttle-based storage and retrieval system (SBS/RS). Modeling the system as a discrete-time Markov Chain, this study derived the shuttle distribution under each policy combination, and further developed the expected travel time models for both single-command (SC) and dual-command (DC) operations. The developed models can help a warehouse designer or manager to comprehensively make four decisions for SBS/RS, whether SC or DC operations should be adopted, the most appropriate storage policy, the best shuttle dispatching rule, and the number of shuttles. The travel time models can evaluate each possible policy combination quickly through multiple criteria, such as the capacity, customer responsiveness, and costs of shuttles. Numerical experiments showed significant impacts of the policy combination on the expected travel time. Class-based storage, if implementable, is always better than random storage under each combination of shuttle dispatching rule and operational mode (i.e., SC or DC). Under random storage and SC operations, the distance-based shuttle dispatching rule is always better than the random rule, and their difference is concave in the number of shuttles. When classed-based storage is adopted and the number of shuttles is small, the demand-rate-based rule is the best under SC operations. Its benefit becomes more obvious when demand rates become more heterogeneous until some point but decreases as the system has more shuttles. Under DC Operations, depending on equipment speeds, the distance-based shuttle dispatching rule can be worse than the random rule under random storage. Under class-based storage and DC operations, the demand-rate-based rule is still the best when demand rates are heterogeneous. The differences between SC and DC operations are more significant when demand rates are more homogenous. When a system wants to be more responsive to customers, it should be run under SC operations. If the overall capacity is of interest, DC operations are much better.

Optimal two-class-based storage policy in an AS/RS with two depots at opposite ends of the aisle

Although automated storage/retrieval systems (AS/RSs) with two depots at opposite ends of each aisle (TD-AS/RSs) have been adopted in I-shaped warehouses (one of the three typical warehouse layouts), class-based storage policy has surprisingly not been investigated in TD-AS/RSs. This paper studies TD-AS/RSs with two storage zones, aiming to determine the optimal first storage zone that minimises the S/R machine’s expected travel time. We first formulate an expected travel time model. The analysis is then performed for two situations: (1) the ratio between the number of storage and total requests (Ratio-ST) is constant and (2) the Ratio-ST changes with time but follows a probability distribution. For the first situation, utilising proposed properties, an efficient optimal search-based algorithm (i.e. Algorithm 1) is developed. The results show that the proposed storage policy can improve system performance by up to about 60% and 40%, respectively, compared with two policies used in practice. Contrary to the literature results, our results indicate that the single command cycle performs better than the dual command cycle in the TD-AS/RS. For the second situation, an algorithm based on Algorithm 1 is developed. A case study using two real-world datasets confirms that our policy outperforms practically used policies in this situation.

Within-aisle or across-aisle? Optimisation and comparison of two class-based storage policies in multi-dock unit-load warehouses

As E-Commerce develop, customers' tight delivery requirements are driving warehouses to operate more efficiently. This paper studies two types of class-based storage policies for a multi-dock unit-load warehouse: the within-aisle policy and the across-aisle policy. The aim is to determine the optimal class boundary and explore the dominance situation (i.e. which policy performs better in which situation). We first develop an expected response distance model for each policy based on a general layout of multiple docks. Then, closed-form optimal first zone boundary and warehouse dimension are given for the across-aisle policy. For within-aisle policy, an efficient algorithm is developed (based on the proved unimodality property) for obtaining its optimal first zone boundary. Besides, each policy's dominance situation is analysed for two typical situations. Numerical results show that both policies can bring up to 60% performance improvement compared with the random storage policy. Impacts of docks layout and warehouse dimensions on each policy's dominance situations are illustrated. It shows that the across-aisle policy performs better in the situation where the docks' and wall's centrelines are adjacent, and in the situation where the warehouse is narrow and deep. Managerial insights on policy selection and dock layout design are presented for warehouse managers.

Analytical model to estimate the performance of shuttle-based storage and retrieval systems with class-based storage policy

This paper presents a method to determine the performance of shuttle-based storage and retrieval systems (SBS/RS) with tier-captive single-aisle shuttles and class-based storage policy. The use of this approach takes place both in the design process of SBS/RS and in the redesign process of SBS/RS. With this approach, it is possible to evaluate performance improvement by applying a class-based storage policy. Another beneficial scope of application of this approach is the evaluation of the placement of different classes throughout the rack to achieve the maximum throughput. The basis of this calculation method was a continuous-time open-queueing model with limited capacity. The cycle times of lifts and shuttles, as determined by a spatial value approach combined with a probability-based approach to mention the storage policy, could be directly used in the presented method with their time distributions. The data used herein were provided by a European material handling provider. An example was presented to outline how this calculation model can be used for optimizing the already existing SBS/RS via the application of a class-based storage policy. Through the example, depending on the configuration of the policy, applying the class-based storage to an existing SBS/RS would increase the throughput by up to twice the throughput without class-based storage policy.

A Bi-Level Bi-Objective optimization model for the integrated storage classes and dual shuttle cranes scheduling in AS/RS with energy consumption, workload balance and time windows

Dual shuttle cranes scheduling problem is an option of Automated Storage and Retrieval System (AS/RS), which has paid marginal academic regard, so it could be extended to advance novel challenges that should be investigated. In this paper, an integrated class-based storage location assignment and dual shuttle cranes scheduling problem is addressed by introducing a novel bi-level optimization model. In the upper-level, the locations of storing and retrieving products under class-based storage policy, multi-period planning and inventory considerations are determined, which its objective functions are included minimization of total cost and energy consumption. The solution of this level is utilized by the lower-level, which is related to the scheduling of dual shuttle cranes. In the lower-level, a broad range of necessities of rewarding scheduling, ranging from workload balance to time windows under multi-period planning is reflected. Since the proposed model is a bi-level and bi-objective, a Modified Cooperative Coevolutionary Algorithm for Bi-Level Optimization (MCoBRA) is presented with the intention of solving the proposed model. Finally, in order to illustrate the validation of the proposed model and solution approach, a broad range of numerical experiments are sketched out, and some comparative analyses concerning two existing algorithms in the literature are provided. Sensitivity analyses show that considering class-based storage policy in the upper-level has significant influences on the whole of the objective functions, which could be offered better planning of operations.

An integrated design approach for class-based block stacked warehouse

PurposeThis 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.Design/methodology/approachThe 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.FindingsMore 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.Practical implicationsThe 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.Originality/valueVery 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.

Optimal dimensions for multi-deep storage systems under class-based storage policies

Multi-deep automated storage and retrieval systems (AS/RSs) have seen many implementations in warehouses due to their high floor space utilization. However, the rack size, which will fundamentally affect the operating efficiency of the storage and retrieval machine in the system, should be optimized analytically. While extensive studies have been made to analyze the system performance and optimize operating policies for single-deep and double-deep AS/RSs by travel-time models. No analytical models are available for multi-deep AS/RSs. To fill this gap, we develop travel-time models for this system, considering the random storage policy and two class-based storage policies in a multi-deep AS/RS: the one that zoning only on picking face and the one that zoning on both picking face and the depth direction. Based on the travel-time models, we derive the formulation of the optimal system size, to minimize the expected travel time of S/R machine. Simulation models are built to validate the analytical models and the results show that the maximum relative error between analytical result and simulation result is 4.3%. Numerical experiments are conducted to find the optimal size of a multi-deep AS/RS and compare the performance of these storage policies. The results show that class-based storage policy always outperform the random storage policy in terms of expected travel time, and the class-based storage policy that zoning on both picking face and the depth direction may not be better than that only zoning on the picking face, while it may be harder to handle in the system.

A modeling of dynamic storage assignment for order picking in beverage warehousing with Drive-in Rack system

This paper develops a dynamic storage assignment model to solve storage assignment problem (SAP) for beverages order picking in a drive-in rack warehousing system to determine the appropriate storage location and space for each beverage products dynamically so that the performance of the system can be improved. This study constructs a graph model to represent drive-in rack storage position then combine association rules mining, class-based storage policies and an arrangement rule algorithm to determine an appropriate storage location and arrangement of the product according to dynamic orders from customers. The performance of the proposed model is measured as rule adjacency accuracy, travel distance (for picking process) and probability a product become expiry using Last Come First Serve (LCFS) queue approach. Finally, the proposed model is implemented through computer simulation and compare the performance for different storage assignment methods as well. The result indicates that the proposed model outperforms other storage assignment methods.

Analytical Optimization for the Warehouse Sizing Problem Under Class-Based Storage Policy

The aim of this paper study the impact of class-based storagepolicy basedon the optimal configuration of U-flow single command warehouses using aon an ABC product classification. For that purpose, the authors proposea non linear optimization model to minimize the expected travel distanceof the warehouse and use analytical methods to solve it.The most important contribution is to provide a mathematical proof thatregardless of the storage policy (It does not matter the specifics of theturnover pattern of the products), the optimal warehouse has a width thatis the double of its length, and the pick and deposit point should be locatedin the middle of the width of the warehouse.In addition, authors perform a sensitivity analysis that indicates that theoptimal solution is robust, meaning that a certain deviation from the op-timum layout does not impose a significant penalty on the expected traveldistance of the warehouse.

Design and simulation based validation of the control architecture of a stacker crane based on an innovative wire-driven robot

Automated Storage/Retrieval Systems (AS/RS) have an important role in the improvement of the performance of automated manufacturing systems, warehouses and distribution centers. Existing AS/R systems are usually based on Cartesian Storage/Retrieval Manipulators (SRM). Such systems have reached their maximum performance due to the limitations of their underlying mechanical design and associated control architecture. Going beyond the limits of existing systems requires structural innovation and breakthrough solutions to enhance their design and performance. In this study, we introduce the design and simulation based evaluation of a stacker crane based on an innovative wire-driven SRM. We describe the basic components and provide an overview of the mechanical design of the system. We design the high-level control architecture that allows handling mini-load operations. We develop the equations that determine the single and dual command cycle times for the wire-driven SRM in case of random and class-based storage policies. We validate the suggested control architecture using a simulation software specifically developed for this purpose. We benchmark the wire-driven SRM against an equivalent Cartesian SRM. Results show that the new wire-driven SRM design and control architecture are more competitive than Cartesian SRM in terms of travel cycle times, and more suitable for buildings growing in height. We design and evaluate an innovative wire-driven Storage/Retrieval Manipulator (SRM).We determine single and dual command cycle times for random and class-based storage policies.We benchmark the wire-driven SRM against an equivalent Cartesian SRM.The new design and control architecture have more competitive travel cycle times.The new design and control architecture are more suitable for buildings growing in height.

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.

Travel Time Analysis of an Open-Rack Miniload AS/RS under Class-Based Storage Assignments

Automated storage and retrieval system (AS/RS) is one of the major warehousing systems, which is widely used in distribution centers and automated production and can play an essential role in modern factories for work-in-process storage, as well asin integrated manufacturing systems. In this study an open-rack miniload AS/RS with unidirectional-upward mobile loads within the rack is considered. In this system, the stacker crane is used for the retrieval operations and shuffling procedures, while the storage operations are carried out by separate devices namely, storage platforms. The proposed AS/RS has one storage platform for each rack to unload several loads at the same time into the rack. Travel time of the proposed AS/RS is analyzed under class-based storage policy by means of Monte Carlo simulation. The results of this study can be used as guidelines for the design and analysis of this kind of AS/RS. Index Terms—Automated storage and retrieval systems (AS/RS), Open-rack miniload AS/RS, travel time, monte carlo simulation.

Impact of required storage space on storage policy performance in a unit-load warehouse

The performance of a storage policy in a warehouse is usually evaluated on the basis of the average one-way travel distance/time needed to store/retrieve a load. Dividing the storage space into zones based on item turnover frequency can reduce the travel distance. However, for a given number of stored items, a larger number of storage zones also requires more storage space, because of reduced space sharing between the items, which increases travel time. This study considers the required space consumption by storage zoning in comparing the performance of random, full turnover-based and class-based storage policies for a unit-load warehouse operated by a forklift in single-command mode. A generalised travel distance model that considers the required space consumption is developed to compare the performance of these policies. Results show that the one-way travel distance of a random policy decreases with the increase in skewness of the demand curve. By considering the required space consumption, a class-based storage policy performs generally better than a full turnover-based policy. In addition, the optimal warehouse shape factor (ratio of warehouse width to depth) appears to decrease with the skewness of the demand curve. Warehouse managers are advised to adopt a wide-shallow warehouse layout when the item demands are approximately equal, whereas a narrow-deep layout is preferred when the demand curves are steep.

Warehouse Design under Class-Based Storage Policy of Shuttle-Based Storage and Retrieval System

In this study, we aim to find out the best rack design for shuttle-based storage and retrieval system (SBS/RS) under class-based storage policy (CSP). SBS/RS is a new technology in automated storage and retrieval system (AS/RS) which is developed for high transaction environments where mini-load AS/RS crane may not be able to keep pace with the transaction rate needed over a given number of storage locations. We consider several rack design concepts in terms of number of aisles, tiers and bays in the warehouse. The performance of the system is evaluated in terms of utilizations of lifts and storage/retrieval devices and cycle times of storage/retrieval transactions. We utilize simulation for the modeling purpose. The results indicate that CSP works better under high rise warehouse design. So, this may create less number of lifts requirement in the system. Hence, the warehouse design with CSP may tend to have lower investment cost. By this study, we also aim to provide practitioners and academia a significant insight for SBS/RS design under several rack design concepts for CSP.

A statistical study employing agent-based modeling to estimate the effects of different warehouse parameters on the distance traveled in warehouses

In manually operated warehouses, the travel distance of the order picker has profound effects on the warehouse cost and efficiency. Estimating this distance is difficult because the warehouse environment is a stochastic one, affected by a great number of parameters. Therefore, we present a comprehensive statistical study to assess how the different warehouse parameters and their interactions affect the travel distance. To estimate the travel distance, we simulate the different designs using agent-based modeling (ABM). Having 324 different designs, ABM has enabled us to build one computer model to simulate all the cases. The study shows that having one cross aisle only and using a class-based storage policy decreases the travel distance. Moreover, the results obtained show that choosing the best routing policy depends on the warehouse layout, which proves the importance of considering the interactions among the different parameters.