Class-based Storage Location Assignment: An Overview of the

Literature

Behnam Bahrami, Hemen Piri and El-Houssaine Aghezzaf

Department of Industrial Systems Engineering and Product Design, Ghent University, Belgium

Keywords: Warehouse Operations, Class-based Storage Location Assignment Problem, Literature Review.

Abstract: Storage, per se, is not only an important process in a warehouse, also it has the greatest influence on the most

expensive one, i.e., order picking. This study aims to give a literature overview on class-based storage location

assignment (CBSLAP). In this paper, we discuss storage policies and present a classification of storage

location assignment problem. Next, different configuration of classes are presented. We identify the research

gaps in the literature and conclude with promising future research directions.

1 INTRODUCTION

Establishment of effective and smooth logistics

operations is under pressure by the growing trend of

shorter time window order fulfillment, bigger product

assortment and smaller order quantities. Contributing

to a large share of the total product costs, logistics

operations are determinants in company’s survival in

the current competitive business world. The

efficiency and effectiveness of a distribution network,

in turn, greatly depends on the performance of the

nodes in such a network, i.e., the warehouses

(Rouwenhorst et al, 2000). Warehouse operations are

thus crucial in the context of logistics. They provide

a means to make the storage of all kind of inventories,

from raw material to final products, easier among

upstream to downstream stages of a supply chain

(Choy et al, 2017). Planning the warehouse

operations in an effective way is not simple because

they consist of different activities (Lam et al, 2015).

These operations can be categorized into four

activities or processes: receiving, storage, order-

picking and shipping ((van den Berg and Zijm, 1999);

(Gu et al, 2007); (Rouwenhorst et al, 2000)).

The interface of a warehouse for incoming and

outgoing material flow are receiving and shipping.

Storage deals with assignment of products to storage

locations to utilize space as much as possible and

facilitate efficient material handling (Gu et al, 2007).

Order picking is the retrieval of items from their

storage locations and can be performed manually or

(partly) automated (Rouwenhorst et al, 2000).

Storage is traditionally considered as the most

important facet of logistics. Efficient inventory

control, lower personnel cost, higher productivity,

and convenient product identification are the

outcomes of a proper storage system (Fontana and

Cavalcante, 2014). Order fulfillment time, and

thereby customer service level, can substantially be

improved by even slight storage process

enhancements (Fontana and Nepomuceno, 2017).

The amount of stored products, the time and the

rate of reorders, and the place of inventories in the

warehouse are three basic and main issues that should

be addressed in storage function (Gu et al, 2007).

Classical inventory control fields of lot sizing and

staggering deal with the first two topics that are out

of the scope of this paper.

The storage location assignment problem (SLAP)

deals with how to put the stock keeping units (SKUs)

away in a warehouse to optimize a performance

measure (Kovács, 2011). Customers ask for more

diverse products which cause warehouses to take on

larger product assortment and this situation

accordingly leads to a more complex storage location

assignment problem (Choy et al, 2017). Storage

location assignment influences almost all key

warehouse performance indicators including order-

picking time and cost, productivity, shipping and

inventory accuracy, and storage density (Frazelle,

2002). The most important performance measures in

a warehouse are generally related to the time or effort

required for order picking (Kovács, 2011). Picking

performance is directly affected by storage process

and, therefore, it is tried to consider this interaction in

390

Bahrami, B., Piri, H. and Aghezzaf, E.

Class-based Storage Location Assignment: An Overview of the Literature.

DOI: 10.5220/0007952403900397

In Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2019), pages 390-397

ISBN: 978-989-758-380-3

the design stage (Davarzani and Norman, 2015).

Roodbergen and de Koster (2001) have presented

four approaches to reduce travel time or distance for

order picking activity: (1) determining good order

picking routes; (2) zoning the warehouse; (3)

assigning products to the right storage locations; (4)

picking orders in batches. The third approach, storage

assignment of SKUs, is more influential on the

effectiveness of order picking than any of the other

three approaches and a well-designed storage

assignment approach could substantially decrease the

travel distance or time of order picking (Chiang et al,

2014). Higher material handling costs and lower

space utilization are the outcomes of unsystematic

assignment of SKUs to storage locations (Choy et al,

2017).

In the next section, we introduce, classify and

discuss different storage policies and various existing

methodologies of CBSLAP in the literature are

presented. Section 3 is about configuration of classes

and finally we conclude the paper with presenting the

identified research gaps and future research

directions..

2 STORAGE POLICIES

Products can be assigned to storage locations either

arbitrarily or based on certain criteria. The first option

is often referred to as “random policy”; we will refer

to it as the “haphazard policy”. The second option is

referred to as “dedicated storage”. Haphazard storage

assigns SKUs to locations chaotically over planning

horizon while with the dedicated storage the location

are kept for specific products in a warehouse. These

two policies are the extremes of a spectrum of policies

(Malmborg, 1998). In between of these extreme

policies of haphazard and dedicated storage, is class-

based storage policy. Conversely, haphazard and

dedicated storage can be seen as extreme cases of the

class-based storage policy: haphazard storage

considers a single class and dedicated storage

considers one class for each product.

As a general comparison, dedicated storage

locates compact and high-demanded items near the

input/output (I/O) point, thus is more material

handling friendly in comparison to haphazard policy.

On the other hand, it needs more storage space to

accommodate the maximum inventory levels of each

product in their predetermined locations. Class-based

storage is a compromised policy that tries to combine

the advantages of both policies (Gu et al, 2007). A

detailed explanation of each policy will follow.

Since haphazard and class-based policy permit

different SKUs to be put away in the same location

successively, they are also called shared storage

policies, (see figure 1) ((van den Berg and Zijm,

1999); (Kulturel et al, 1999)).

Figure 1: Classification of storage policies.

2.1 Haphazard Storage

The haphazard policy is a simple procedure and the

only information that is needed to implement this

policy is if the storage locations are available or not.

The most common haphazard policies consist of

random location assignment, closest open location,

farthest open location and longest open location, (see

figure 1), (Gu et al, 2007).

In random assignment, the SKUs are assigned to

empty location considering probability. If the

replenishers put away the SKUs however they are

convenient, the result is probably the so called closest

open location storage. They normally put the items in

the first vacant locations they come across, which

eventually leads to a warehouse with full locations

close to I/O point and more spots farther away (de

Koster et al, 2007). Hausman et al (1976) explain if

the SKUs are transported in full pallets, closest open

location storage and random storage result in the

same performance. Farthest open location policy

allocates the most remote free positions from the I/O

point to SKUs. If the locations are assigned to SKUs

based on the time they have not been occupied, then

it is the longest open location policy.

Haphazard storage is a popular policy in practice

due to its simplicity and advantages including space

utilization, simple implementation, immunity to

demand and assortment fluctuations, and uniform

usage of aisles that leads to lower congestion.

However, since SKUs do not have predetermined

locations a tracking system is required that may cause

difficult and confusing positioning. Moreover, in this

class of policies, the lack of a systemic view

Class-based Storage Location Assignment: An Overview of the Literature

391

eventually declines the global warehouse

performance because of not considering consecutive

processes and not utilizing product information

((Chiang et al, 2011); (Quintanilla et al, 2015)).

2.2 Dedicated Storage

In dedicated storage polices a storage location is

allocated and reserved for SKUs over the planning

horizon. This allocation is based on a suitable

criterion. Kallina and Lynn (1976) present four major

determinants for this: compatibility,

complementarity, popularity and space. Compatible

items can be kept nearby one another without taking

risk of contamination, infection, corrosion, or other

damages, and hence incompatible items should not be

stored closely. Complementary refers to those

products that are often concurrently ordered together

and it may be beneficial to keep them in adjacent

locations. Popular items are those that have a higher

demand and if they are stored in locations closer to

I/O point, the total travelled distance reduces since

popular items are the greatest contributor to this

distance. Finally, it is better to allocate the locations

near I/O point to the less bulky items.

The most common criteria in the literature, which

have also been illustrated in figure 1, are explained as

follows.

2.2.1 Part Number

Assigning SKUs based on their part number is

probably the earliest storage policy. Some researchers

(e.g. (Brynzér and Johansson, 1996); (Fontana and

Nepomuceno, 2017)) have already mentioned part

number as a criterion for dedicated policy. Back in the

years, without having an information system to track

the items, dedicated storage based on the part

numbers was helping the storekeepers to find the

position of the SKUs by following the sequence of the

part numbers. Afterwards, when IT solutions became

widespread, cheap and accessible, the application of

part number as a criterion for dedicated policy

became obsolete and old-fashioned.

2.2.2 Turnover

One of the most popular criterion for dedicated

storage assignment is based on the turnover or

demand of the products. With this criterion, the most

desired products are placed to the most accessible

locations which are usually the ones close to I/O

point. Remote locations are assigned to slow-movers

(de Koster et al, 2007). One of the problem with this

policy is that product turnover rate and the warehouse

product portfolio always fluctuate causing violating

turnover-based assignment of locations that

eventually demands relocations of SKUs to keep the

assignment principle and its advantages (Roodbergen

and Vis, 2009). In the literature, the turnover-based

storage (also known as full-turnover or volume-

based) often represents the dedicated policy

2.2.3 Cube-per-order

One of the first dedicated storage algorithm is the

cube-per-order index (COI) which was proposed by

Heskett (1963). The COI is defined as the ratio of

maximum allotted space to the number of

storage/retrieval operations per unit time. The

algorithm places the products with lower COI to more

convenient locations and as COI increases the SKUs

are located in more distant spots farther from I/O

point (Cormier and Gunn, 1992). Although the COI

algorithm was initially conceived as a heuristic,

several authors later showed that it yields an optimal

solution in certain specific environments.

It is worth to mention when single-command, i.e.

either a single storage or a single retrieval in each

cycle happens, is prevalent, then COI is an excellent

candidate. However, Schuur (2015) shows that there

is no performance guarantee when a single-command

storage strategy is implemented for a multi-command

situation, that is, storage and picking of several loads

in one cycle. In particular, the worst-case behavior of

the COI strategy is infinitely bad.

2.2.4 Duration-of-Stay

Even though it was first introduced by Goetschalckx

and Ratliff (1990) as a shared storage policy, we

classify Duration of Stay (DOS) policy as a dedicated

one, because based on our definition it is a criterion

which an items is assigned to a location. With this

policy, in a system where the input and output rate are

equivalent, products units, upon their arrivals, get a

better location if they stay shorter in the warehouse.

In other words, the shorter the DOS of units of

products, the closer the location to the I/O point they

are placed. The information of incoming/outgoing of

all units of a specific must be available to apply DOS

procedure while the only required information for

turnover policy is the turnover rate at product level

(Pohl et al, 2011). This is a crucial consideration as

DOS approach needs the most data in comparison to

other policies for storage location assignment

(Goetschalckx and Ratliff, 1990). Kulturel et al

(1999) simulated the performance of turnover-based

and DOS-based storage and it turned out that the

former had better performance where the reasons may

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stem from the barely existing assumptions of the DOS

model in real situations which are more like a pure

fantasy in warehouse contexts ((Gu et al, 2007);

(Goetschalckx and Ratliff, 1990)).

2.2.5 Correlation

Correlated storage (or family-grouping) considers

products complementary where stores similar

products close to each other in the warehouse. This

strategy requires a suitable index to determine, or at

least estimate, the correlation among items of the

warehouse assortment. The lack of accurate data to

calculate this index confines correlated storage

application. Dependent demands of different products

are easily recognized by the bill of material (BOM) in

production environments. However, these

interrelations are more complex to utilize in

distribution warehouses. These changing and hard to

predict relationships emanate partially from clients

purchase preferences and patterns which can be

derived from different resources such as catalogs,

promotional plans, market surveys and similar

information (Sadiq et al, 1996). Recently the

advances in the field of big data, data analytics and

data mining facilitate identification of correlated

products.

Dedicated storage policies have the lowest space

utilization in comparison to other policies for this

they allot space for all items such that to be able to

accommodate the maximum level of inventory while

most of the time the inventories are not at their

maximum level and even stock-outs may happen (de

Koster et al, 2007).

In addition, contrary to haphazard policies which

utilize the picking aisles evenly, the picking activities

in the COI-based and turnover-based storage policies

concentrate on the regions where items with low COI

and high turnover are located. For one order picker

system, clearly congestion is not an issue. This is also

true for small warehouses as well as large warehouses

divided into zones with one order picker in each zone

(Caron and Perego, 1998). However, in those

environments where several order pickers work

simultaneously the situation is different and more

pickers does not essentially leads to higher

throughput. This issue is more severe in storage

systems where turnover-based approach

implemented, that is, the more the number of workers

the higher the productivity reduction. This is a main

incapability in this class of storage policies which is

more troublesome where demand seasonality is

present as well (Ruben and Jacobs, 1999).

Finally, although dedicated storage yields the

minimum travel time, it is not practically popular as

its implementation is not simple thus is used as a

performance benchmark to evaluate other storage

policies. Even some authors (e.g., (Rosenblatt and

Eynan, 1989)) view its implementation as

“practically impossible”. A reason is that abundant

information is a prerequisite for a high performance

from the policy. Accurate data, continuous

supervision and capability to cope with ceaseless

changes are requirements of a successful dedicated

storage which are all difficult to gain and accomplish

in many warehouses ((Tompkins et al, 2010); (Rao

and Adil, 2013)).

2.3 Class-based Storage

Class-based storage is a compromised policy that

classifies SKUs into product classes, based on an

appropriate criteria such as volume or usage rate. The

SLAP now is the problem of assignment of SKUs to

a product class and then a class to a storage region in

the warehouse. Items are positioned in their class

following a simple haphazard rule, e.g. random or

nearest open location. Haphazard policy is actually

the class-based policy with one class and if each

product has its own class, then it is dedicated policy

(Gu et al, 2007). Class-based storage with three

classes often is referred to as ABC storage

(Roodbergen and de Koster, 2009). Class-based

storage is popular among practitioners due to its great

capabilities such as simple implementation,

manageable maintenance and ability to cope with

product mix and demand variations (Le-Duc and de

Koster, 2005). No need for full sorted list of SKUs

and more convenient administration are the reasons

of easier implementation of class-based storage in

comparison to dedicated storage. Class-based storage

also outperforms haphazard storage in terms of travel

time that is comparable to that of dedicated storage as

well (Petersen and Aase, 2004).

The general belief, among the warehouse

community, is that dedicated storage yields lower

travel distances than class-based storage. Petersen

and Aase(2004) demonstrate that a turnover-based

dedicated policy performs better than class-based

policy with three classes but this improvement is less

than 1% that even this may not be true because

haphazard allocation of SKUs in the classes causes

lower storage area and consequently shorter order

picking time (Muppani and Adil , 2008a). Travel time

of class-based policy, in traditional research, is

considered at its best to approach to that of turnover-

based policy. However, Guo and de Koster (2015)

Class-based Storage Location Assignment: An Overview of the Literature

393

show that the average one way travel distance of the

turnover-based storage is not a lower bound in the

warehouse. Space sharing is the answer of this

contradiction; since SKUs share the warehouse space

in haphazard storage hence less space is required

(almost two third) in comparison with turnover-based

policy that subsequently influences the average travel

time. Furthermore, Muppani and Adil (2008b)

observed that where a system suffers from high

inventory fluctuations of SKUs, class-based solutions

perform better than dedicated approach.

The strength of class-based policy is in taking

advantage of the logic of dedicated storage, while

avoiding the exhaustive chores alongside (Petersen

and Aase, 2004). For this, Class-based policy classify

products based on some criteria, and once all products

have their class being determined, neglect the criteria

for the period of planning horizon to exploit the

simplicity and convenience of haphazard storage

policy. Most previous studies used turnover rate as

the basis to classify products for storage assignment

(Chiang et al, 2014) but all other criteria which were

explained for dedicated storage may be applied for

this purpose. This is the reason why these criteria

have been connected to the class-based box with a

dashed line in figure 1.

3 CONFIGURATION OF

CLASSES

The performance of a warehouse is highly affected by

its layout (configuration), the way SKUs are placed in

and picked from locations and also the position of I/O

points. Several authors studied configuration of

classes in a warehouse. A surprising result in this field

is that the optimal configuration for a warehouse with

a specific capacity is independent of the storage

policy. This fact makes the design of storage system

easier since the designers do not have to worry about

which policy is or will be put into practice. They just

need to optimize the configuration considering a

simple (e.g. haphazard) policy, whatever the result is,

the storage shape is optimal for other policies such as

turnover-based or class-based storage (Zaerpour et al

2013)

3.1 Class Formation

Rosenblatt and Eynan (1989) developed a one-

dimensional search procedure to determine optimal

boundaries for class-based policy. They show that

using a relatively small number of classes can result

,in average, travel times which approach travel times

obtained for the turnover-based assignment. Some

authors have already suggested some numbers for

class formation. For instance, Rao and Adil (2013)

claim that maximum of three classes is sufficient to

get a major extent of the benefit of turnover policies

and Guo and de Koster (2015) argue a class-based

policy with a small number of classes, no more than

5, is optimal.

Although conventional research (e.g., (Eynan and

Rosenblatt, 1994), (Rosenblatt and Eynan, 1989))

show that there is inverse relation between picking

time and the number of classes (figure 2), warehouse

managers limit the number of classes to a small

number. Yu et al (2015) demonstrate that the travel

Figure 2: Inverse relation between picking time and the

number of classes (Yu et al, 2015).

time function has a different shape (figure 2) and,

contrary to previous studies, there is an optimum for

the number of classes. Another main result of their

study is the insensitivity of travel time function to the

number of classes in a wide range around the optimal

number of classes which is something between 3 to 8.

This is a good news for the warehouse managers since

this gives them more freedom in implementing class-

based policy and they can also take into account their

practical constraints.

3.2 Implementation of Classes

Hausman et al (1976) consider the problem of finding

class regions for the class-based storage policy. The

authors suggest L-shaped (figure 3(a)) class regions.

This shape is optimal for Chebyshev travel times, if

only single-command cycles are present. They

analytically determine optimal class sizes for two

classes in a square-in-time rack, such that the mean

single-command travel time is minimized. Graves et

al (1977) observe that L-shaped regions are not

necessarily optimal when dual-commands occur.

Petersen and Schmenner (1999) present four

ICINCO 2019 - 16th International Conference on Informatics in Control, Automation and Robotics

394

Figure 3: Implementation of classes.

variations for turnover-based storage: diagonal,

within-aisle, across-aisle, and perimeter storage

which can also be considered as different variations

for class-based policy, (figure 3(b)-(e)). They show

within-aisle storage with a middle I/O point is the best

storage policy for all pick lists. The middle I/O point

is better than the corner I/O point. However, this

difference becomes almost nonexistent for large pick

lists.

The within-aisle strategy has also later been

shown to have a higher performance than other

storage implementation strategies regardless of

thenumber of storage classes. ((Petersen et al, 2004);

(van Gils et al, 2017)).

4 CONCLUSION AND

FUTUREESEARCH

DIRECTIONS

This paper draws a framework for the class-based

storage location assignment problem in the ware-

house storage process. According to the examined

studies, a number of conclusions are addressed.

First of all, we would like to draw the attention of

researchers to integrated warehouse operations. The

importance of integrated warehouse problems has

already been highlighted by some other authors (e.g.,

(Roodbergen and Vis, 2009); (Cergibozan and Tasan,

2016); (van Gils et al, 2017)). However, the focus of

the research community has been on combination of

the storage, batching and routing. The significant

statistical correlation of storage, batching and routing

has already been tested and confirmed.

Second, the potential advantage of integrated

models is clear but they have not still been validated

in complex industrial contexts. This gap is not only

limited to integrated models but also incorporate

studies which just deal with SLAP in its own. The few

published industrial case studies ((van Oudheusden et

al, 1988); (Zeng et al, 2002); (Dekker et al, 2004))

accentuate the lack of balance between papers with an

assumption-restricted modeling approach and those

based on the complex reality of warehouses. This gap

has been also underscored by other studies ((Gu et al,

2007); (Davarzani and Norman, 2015)) in warehouse

literature and it shows the limited cross fertilization

between research community and practitioners. A

good liaison establishment between academia and

industry is a win-win situation. On the one hand, it

helps researchers in better understanding the reality to

identify possible future research challenges from the

industrial point of view. On the other hand, research

results with a validity check on real-case

environments will have a more substantial impact on

practice. Therefore, practical case studies and

research, explaining applied or validated method-

logies which illustrate the potential advantages of

implementing scientific literature results to real

problems, or on discovering the unknown challenges

which hinder their successful implementation is

another direction for future contribution.

Some researchers introduced other measures for

SLAP along with economics measures. Future

research should focus on other performance measures

as well. For instance, an important subject in progress

is the sustainability issues in logistics. Sustainable

operations have been widely studied in past years, but

the inclusion of metrics in warehouse management

have still place for examination (Staudt et al, 2015).

Although energy efficiency and environmental

performance have gained increasing attention during

past couple of decades in operations management

literature, majority of the reviewed literature focused

on economic efficiency of SLAP. Social awareness

and governmental regulations about global warming

and environmental issues spotlight this topic. Another

instance is the inclusion of human factors into SLAP

models. Reminding that majority of operating

warehouses are still manual systems, put more

emphasize on the importance of further research in

this field.

Finally, the early focus of warehouse

management research was on process improvement

which essentially does not need IT tools. However,

the complexity of warehouse operations has increased

in recent years and more complicated algorithm and

models appear in warehouse management

publications (Staudt et al, 2015). Application of

information systems in warehouse management is a

growing tendency and the related new technologies

Class-based Storage Location Assignment: An Overview of the Literature

395

will certainly be used for decision making in the

future. We believe there is big room to study

opportunities and challenges of employing more

advanced technologies and initiatives such as

augmented reality, internet of things, cloud

technologies, cyber physical systems and Industry 4.0

not only in SLAP but also in other warehouse

processes in general.

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