Class-based Storage Research Articles

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.

Modeling class-based storage assignment over life cycle picking patterns

This paper aims to introduce a new approach for the design and management of warehousing systems, which involves variable demand patterns in agreement with the life cycle of stock keeping units (SKUs). Two cost-based mixed integer linear programming (MILP) models are formulated to address both the technology selection, e.g. part-to-picker systems (as automated storage and retrieval systems – AS/RS, miniloads, etc.) and picker-to-part order picking systems, and the SKU assignment to the storage areas. In particular, a class based storage assignment over a life cycle picking metric is adopted. This metric is a rolling measure of popularity.The proposed approach, the adopted measure of popularity and the developed models can support (1) decision making for the selection of warehousing and material handling (W&MH) systems, (2) the determination of the storage capacity for the storage classes, (3) the assignment of SKU to the storage classes and (4) the dynamic, i.e., time-based, management of re-warehousing handlings. Two significant numerical examples illustrate the application and the potential of the proposed approach and models.

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.

Class-Based Storage Assignment in a Unit-Load Warehouse Employing AS/RS with Inventory Space Allocation Considering Product Specific Setup to Holding Cost Ratio

Many authors have used continuous optimization models for class-based storage allocation for unit load warehouses employing AS/RS system assuming the same setup to holding cost ratio for all products in their EOQ inventory policy. Accordingly, an ABC curve based on demand ordering of products is used to determine pallet turnovers in their travel distance models which is not applicable in many practical situations and can be inaccurate. This paper develops a new model to determine two/three class optimal partitions while relaxing assumption of constant setup to holding cost ratio in the EOQ formula and as a consequence uses an ABC curve based on decreasing ranking of product turnovers rather than that based on demand. Computational experience shows that this new continuous model approximates pick travel distances which are very close to as compared to that of a semi-discrete model (SDM) proposed in this paper. Further computational experiments are also provided comparing traditional models with our formulations.

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.

A travel time estimation model for a high-level picker-to-part system with class-based storage policies

Most previous related studies on warehouse configurations and operations only investigated single-level storage rack systems where the height of storage racks and the vertical movement of the picking operations are both not considered. However, in order to utilize the space efficiently, high-level storage systems are often used in warehouses in practice. This paper presents a travel time estimation model for a high-level picker-to-part system with the considerations of class-based storage policy and various routing policies. The results indicate that the proposed model appears to be sufficiently accurate for practical purposes. Furthermore, the effects of storage and routing policies on the travel time and the optimal warehouse layout are discussed in the paper.

Using the Efficient Frontier to Obtain the Best Solution for the Storage Location Assignment Problem

The main variables that influence the efficiency of a warehouse are the use of space and the order picking distance. In the literature, there are proposals to add the costs with space and order picking in order to evaluate each alternative for storage location assignment. However, there were problems with the adoption of this methodology, including difficulties in determining the costs and tradeoffs between them. These difficulties can result in solutions that are suboptimal. Based on these facts, this paper proposes a class-based storage process and storage location assignment by a cube-per-order index (COI) that analyzes the space required and the total order picking distance by Pareto-optimal calculations. The efficient frontier possibilities allow the reduction of the set of alternatives, and the DM can analyze only the alternatives on efficient frontier.

Optimal class boundaries, number of aisles, and pick list size for low-level order picking systems

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

Class-based storage with exact S-shaped traversal routeing in low-level picker-to-part systems

We consider a low level picker-to-parts system in a one-block warehouse with P/D point located at a corner point. We develop new and exact analytical models to estimate travel distance for random, full turnover and class-based storage in which a single picker follows an S-shaped traversal routeing policy. Our analytical results for random, full turnover and class-based storage follow corresponding simulations very closely with an accuracy of within 2% for the range of warehouse parameters considered. We also report the percentage improvement obtained by the optimal 3-class storage strategy over the random storage policy and the potential gain of this 3-class policy as compared to the full turnover policy. Further, we conduct a computational study to determine the optimal layout parameters for a warehouse with a 3-class optimal storage strategy for a given pick size per tour. Sensitivity analysis of pick travel distances with important warehouse parameters is also presented.

On the suboptimality of full turnover-based storage

Full turnover-based storage has been widely claimed to outperform the commonly used ABC class-based storage policy in terms of the resulting storage and retrieval travel time. In the first paper describing the full turnover-based policy, simultaneously implicitly assuming shared storage (that is to say storage space that can be used by other products once the original product's inventory is depleted) was modelled, since no specific space was reserved to store the maximum inventory of a product. However, full turnover-based storage is a dedicated storage policy where the storage space allocated to one product must be able to accommodate its maximum inventory level. Since then, many authors have cited the results of the full turnover-based storage policy while often overlooking its incompatible shared-storage assumption. This paper adapts classic travel time models to accommodate full turnover-based dedicated storage. We consider different warehousing system configurations such as square-in-time (SIT) and non-SIT racks, and speed acceleration and deceleration effects to calculate the storage/retrieval (S/R) machine's travel time. Surprisingly, but in line with practice, the results of the adapted travel time models show that random and class-based storage normally outperform full turnover-based storage.

Solving Stereo Warehouse Storage Location Assignment Problem Based on Genetic Algorithm

Class-based stereo warehouse storage policy distributes products among a number of classes and for each class it reserves a region within the storage area. In this paper, a genetic algorithm method of using the intersectant operation of chromosome repair and mutation operation of two-way movement detection is developed to solve an integer programming model for storage assignment. Computational experience on randomly generated data sets and an industrial case shows that improved genetic algorithm gives superior results than the genetic algorithm without improving for storage location assignment with ordering restriction.

Pareto-Based Multi-Objective Optimization for Two-Block Class-Based Storage Warehouse Design

This research proposes a Pareto-based multi-objective optimization approach to class-based storage warehouse design, considering a two-block warehouse that operates under the class-based storage policy in a low-level, picker-to-part and narrow aisle warehousing system. A mathematical model is formulated to determine the number of aisles, the length of aisle and the partial length of each pick aisle to allocate to each product class that minimizes the travel distance and maximizes the usable storage space. A solution approach based on multiple objective particle swarm optimization is proposed to find the Pareto front of the problems. Numerical examples are given to show how to apply the proposed algorithm. The results from the examples show that the proposed algorithm can provide design alternatives to conflicting warehouse design decisions.

Performance evaluation of In-Deep Class Storage for Flow-Rack AS/RS

This article presents a new storage-retrieval method called In-Deep Class Storage, designed for Flow-Rack AS/RS. Class-based storage is a well-known method that has an extensive literature; our method is based on the fact that it is more efficient to dedicate the front layers of each bin to the class of the most popular items rather than dedicating whole bins close to the drop-off station. Clearly, this idea is not trivial to implement due to the dynamic behaviour of such racks. Thus, two separate algorithms have been defined, one for storage and one for retrieval, enabling dynamic use of our approach, with the only hypothesis of a Pareto distribution of item demand. This article presents a simulation study designed to compare the performance of random storage and retrieval with the use of the algorithms. This study shows a significant improvement of the expected retrieval delay, the main performance indicator selected for the study.

Proximity Value for a Storage Assignment Policy in a Multi-I/O Point ETV

This paper is focused on an analysis of Elevating Transfer Vehicle (ETV) System installed in an existing air cargo terminal. The operation of ETV System is very similar to that of Automated Storage and Retrieval System (AS/RS), and the existence of multi-input and output (I/O) points in ETVs is assumed. The proximity value (PV) is defined for the procedure of storage assignment in ETVs with multi-I/O points. The validity and usefulness of the PV in the assignment procedure of automated storage are shown through the simulation study. For a multi-I/O point ETV, dynamic time based PVs are more efficient for the storage policy while the class-based storage policy works better for a single I/O point system. Although, this study focuses mainly on an efficient policy for an ETV system, the proposed rule for the operation of the system would work with an AS/RS as well.

Optimisation of storage allocation in order picking operations through a genetic algorithm

This paper explores the use of a genetic algorithm (GA) to optimise item allocation in a warehouse, with the ultimate purpose of reducing the travel time of pickers, thus streamlining order picking operations. The GA is described along with a numerical example, reflecting a fast moving consumer goods warehouse, where items are assumed to be allocated according to a class-based storage system. Starting from that configuration, and taking into account the set of orders to be fulfilled, the GA identifies a new item allocation, which significantly decreases the travel distance (by approximately 20%). This involves a corresponding decrease in the cost of picking operations, and allows the warehouse to respond quicker to the requests of customers. The GA and its numerical implementation are supported by a general purpose software, such as Microsoft Excel®, programmed under visual basic for applications; the resulting tool is thus easy to use in real scenarios.

Application of genetic algorithms for sequencing of AS/RS with a triple-shuttle module in class-based storage

Materials handling systems, as a main support to the dynamism of logistic systems, must be highly productive, well utilised, and very efficiently controlled. In the case of high volume, large capacity warehouse systems, an AS/RS (automatic storage/retrieval system) is a typical solution to these requirements. The performance of an AS/RS is closely related to the implementation of different control policies. The sequencing policy, used to create minimum overall handling time tours, has a great impact on system performances. The selection of an appropriate sequencing method is even more significant in a more complex case of multiple shuttle S/R (storage/retrieval) devices because of the mutual paring between more than one storing and retrieving tasks. This paper is focused on the sequencing problem of a triple-shuttle AS/RS in a class-based storage system under a modified sextuple command cycle policy with a planning horizon that comprises the realisation of several successive cycles of S/R device. To solve the problem, three commonly used greedy heuristics (nearest neighbour, reversed nearest neighbour, and shortest leg) are adapted, and a genetic algorithm is proposed.

Optimal placement of warehouse cross-aisles in a picker-to-part warehouse with class-based storage

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.

A class-based storage warehouse design using a particle swarm optimisation algorithm

Classical warehouse design is commonly done in two steps by first determining the aisle layout and dimension followed by the assignment of items to storage. The design process is performed iteratively until a design with appropriate performance criterion is found. This paper proposes an approach for warehouse design in one step by determining the aisle layout and dimension while simultaneously assigning shelf spaces for storing the items based on item classes. A mathematical model is formulated to determine the number of aisles, the length of aisle and the length of each pick aisle to allocate to each product class that will minimise the average travel distance for a warehouse that operates under a class-based storage policy. A particle swarm optimisation algorithm was developed to determine the optimal warehouse design. The proposed method not only accomplishes the task in one step but also can identify multiple alternative designs. A case study is used to illustrate the proposed algorithm.

Storage location assignment and order picking optimization in the automotive industry

The objective of this study is to design storage assignment and order picking system using a developed mathematical model and stochastic evolutionary optimization approach in the automotive industry. It is performed in two stages. At the first stage, storage location assignment problem is solved with a class-based storage policy with the aim of minimizing warehouse transmissions by using integer programming. At the second stage, batching and routing problems are considered together to minimize travel cost in warehouse operations. A warehouse in the automotive industry is analyzed, and an optimum solution is obtained from an integer programming model. Due to the computational time required for solving the integer programming problem, a faster genetic algorithm is also developed to form optimal batches and optimal routes for the order picker. The main advantage of the algorithm is the quick response to production orders in real-time applications. The solutions showed that the proposed approach based on genetic algorithms can be applied and integrated to any kind of warehouse layout in automotive industry.

Improving the productivity of order picking of a manual-pick and multi-level rack distribution warehouse through the implementation of class-based storage

Determining the proper storage locations for potentially thousands of products is one major task that faces the warehouse manager during the design of a new warehouse or the refurbishment of existing facility. There are many factors affects the storage assignment. For example, order picking method, size and layout of the storage system, material handling system, product characteristics, demand trends, turnover rates and space requirements. Selecting appropriate storage assignment policies (i.e. random, dedicated or class-based) and routing methods (i.e. transversal, return or combined) with regards to above factors is a possible solution to tackle this problem. However, the efficiency of these policies is interdependent. This paper aims to present a simulation study of a real case regarding storage assignment problem of a manual-pick and multi-level rack warehouse. Performance of the case is measured in terms of travel distance and order retrieval time. The results of the study indicate that the key to effective implementation of a storage assignment system is to match the types of warehouse storage system and the variety of items in the customer order. Also, the use of key performance indicator should clearly reflect the needs of the warehouse.