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Design and control of an AS RS (1)

Int J Adv Manuf Technol (2006) 28: 766–774
DOI 10.1007/s00170-004-2427-6
Riccardo Manzini · Mauro Gamberi · Alberto Regattieri
Design and control of an AS/RS
Received: 10 May 2004 / Accepted: 21 September 2004 / Published online: 25 May 2005
© Springer-Verlag London Limited 2005
Abstract Automated storage/retrieval systems (AS/RSs) are
a combination of equipment and controls which automatically
handle, store and retrieve materials (components, tools, raw material and subassemblies) with great speed and accuracy. Consequently, they are widely used in industrial companies to manage
products with cost-effective utilization of time, space and equipment. This paper presents a multi-parametric dynamic model of
a product-to-picker storage system with class based storage allocation of products. Thousands of what-if scenarios are simulated
in order to measure the impact of alternative design and operating configurations on the expected system performance and to
identify the most critical factors and combinations of factors affecting the response of the system. Class based storage proves
to be a very effective way of both reducing the picking cycle
time and maximizing the throughput of the system. The rapid effectiveness of visual interactive simulation (VIS) in supporting
the design (redesign) and control of new (existing) warehouses
emerges, responding to the need for flexibility which modern
companies need in order to adapt to strongly changing operating
conditions quickly.
Keywords Automated storage and retrieval system (AS/RS) ·
Class based storage · Factorial analysis · Order picking system
(OPS) · Warehousing system · What-if scenarios
1 Introduction
New, global and extended markets are forcing companies to
process and manage increasingly differentiated products with
shorter life cycles, low volumes and reduced customer delivery times. In today’s global marketplace companies need to
R. Manzini (u) · M. Gamberi · A. Regattieri
Department of Industrial Mechanical Plants,
University of Bologna,
Viale Risorgimento, 2., 40136 Bologna, Italy
E-mail: riccardo.manzini@mail.ing.unibo.it
Tel.: +39-51-2093406
Fax: +39-51-2093411
be able to deliver products on time, maintain market credibility and introduce new products and services faster than competitors. In particular, recent growth and strong development
of e-commerce has brought a new focus on warehousing facilities and especially on the design and management of order
picking systems (OPS) where typically thousands of customers’
orders have to be processed per day. This is especially true
in e-fulfillment (e.g., business to consumer – B2C fulfillment),
because internet consumers generally order one to two products in small quantities achieving a one-day cycle time for
orders [1].
Order picking (OP) can be defined as the retrieval of items
from their warehouse locations in order to satisfy demands from
internal or external customers: it is a process of gathering requested stock keeping units one order at a time. Picking operations are carried out by a great many large and medium-sized
companies, which belong to different industrial and service sectors. OPSs can be classified as picker to part (or product) systems
when the picker travels to picking locations, and part (or product) to picker if materials are automatically brought to the picker.
A well-know example of part to picker OPS is represented by automatic storage/retrieval systems (AS/RS), which are a response
to the increasing trend towards both the automation of warehousing operations and the increasing labor costs [2]. Other related
available automated technological options and devices are the
automated guided vehicles (AGV), pick list generation software,
bar coding, etc.
The aim of this study is to identify the most critical factors
affecting the response (i.e., the performance) of an AS/RS based
on the prevailing activity of picking.
AS/RSs are computer-directed storage and transport facilities for large capacity and high volumes of handled materials. They consist of storage racks erected along aisles with
unique or un-unique cell conveyors, input/output (I/O) stations for receiving and sending items, and storage/retrieval
(S/R) machines for providing transport between I/O stations
and storage cells. The automated stacker crane (i.e the S/R vehicle) travels within an aisle performing storage and retrieval
When considering the retrieval activity, there are two principal macro classes of OPSs:
• Unit load systems. Materials are moved and stored by devices
capable of moving and storing only a single-unit handling
load (e.g., pallets, odette boxes).
• Less than unit load systems. With multiple stops per trip. Vehicles are capable of handling multiple unit loads simultaneously. Order pickers, who can be identified with operatorsaboard retrieval vehicles (in picker to part systems) or with
S/R automated machines in AS/RSs, the object of this study,
retrieve sets of items or multiple handling units of the same
item on a single OP cycle. They visit different slots in the
warehouse before returning to the I/O or depot areas. Each
picker is responsible for picking a complete pool of customer
orders during a mission [3].
The aim of this study is to identify and measure the combined
and not combined effects of different system parameterizations
on the performance of both unit load and less than unit load part
to picker OPS.
The following section discusses the principal AS/RS and
OPS studies in the literature. Section 3 presents the storage location policies and particularly the class-based policy for the optimization of warehousing systems. Section 4 discusses batching
procedures for the reduction of picking cycle time and presents
the proposed genetic algorithm based on cellular automation.
Sections 5 and 6 present a dynamic model and related results
based on simulation for the what-if analysis of different OPS
configurations that work in different operating scenarios.
2 Literature overview
Manzini et al. [4] and Ferrari et al. [5, 6] discuss the importance
of flexibility, i.e., the ability of a system to adapt to changing
market demands in terms of both product variations or changes
(capability flexibility) and product quantity (capacity flexibility). Consequently, the importance of flexibility is progressively
emerging in inventory management and in picking activities (in
particular in automated S/R systems).
Several studies discuss the importance of integrating AS/RS
with other flexible devices, such as flexible manufacturing systems (FMSs) and AGVs. They present algorithms and procedures to support the optimization (e.g., storage allocation of materials, storage and retrieval sequencing) of the whole production
system [7–10].
A large set of studies discuss picker to part OPSs [2, 3, 11–
24]. In particular, the authors of this paper present in-depth
studies of the design and management of flexible picker to part
OPSs [21–24]: principal factors affecting the performance of the
picking activities are identified and their effects are measured
and compared.
The literature presents many studies supporting the design
and control of an AS/RS. In particular, Sharker and Babu [25]
present a review and comparison of analytical travel-time models
in unit-load AS/RS. Other significant studies on unit-load
AS/RS, for both single command and dual command with interleaving cycles (i.e., cycles performed on an opportunistic
basis where a retrieval transaction is combined with a storage
transaction), are presented by [26–30]. All these studies are generally based on analytical models and involve a small number
of multi-level parameters because of the NP-hard computational
complexity of the optimizing problem [21, 23, 24, 31, 32]. This
is especially true for the less than unit load OPS optimization
problem (OPSP), which is the object of this study.
The most important planning issues concerning the design
and management of both picker to part and part to picker OPSs
can be addressed on two levels (i.e., the level of policies and the
level of operations) and grouped into three main operating strategies [21–24, 33]:
• Storage location assignment and fulfillment policies, i.e., the
assignment of items to storage locations;
• Order consolidation, i.e., the transformation of customer
orders into picking orders;
• Routing and sequencing, i.e., the identification of the routing
of pickers through the warehouse.
The purpose of Sects. 3 and 4 is to present different alternative
policies for each of these groups of strategies.
2.1 OPS performance evaluation
There are a great many different criteria for the evaluation of
an OPS performance, but in the literature travel time (picking
cycle time) is commonly used: this is the expected amount of
time for the S/R machine to perform a list of storage or retrieval operations. In fact, the throughput of OPS is normally the
inverse of the travel time. The literature demonstrates that the
OP travel time accounts for about 50% of all OP activities [34],
while system cost accounts for over 65% of total operating costs
for a typical warehouse [13, 14, 35].
Reference [3] lists the principal contributors to the total picking cycle time:
• Administrative time at the I/O point, at the start and end of
a tour.
• Processing time the time spent extracting items and documenting the picking activity.
• Travel time between pick locations.
The importance of reducing picking variable traveling cycle time
emerges: it is a monotone increasing function of the distance
traveled. For this reason the measure of the mean distance traveled in a picking cycle is a significant indicator of the performance of the generic OPS. This distance is adopted as the response
(i.e., the performance) of the generic OPS in order to compare
different configurations (i.e., system parameterizations) and optimize the management of the whole system.
3 Storage location policies
The literature discusses storage location assignment strategies at
great length [24, 36]. It is possible to discriminate two macroclasses of assignment storage location rules:
• Storage location assignment policies. The selection of open
locations for incoming unit loads are subject to imposed
• Open locations selection rules (e.g., closest open location,
nearest neighbor) when the policy does not make a unique
Warehouse assignment-dispatching rules are differentiated by
the objective they address; known examples are [3, 4, 12, 15, 33,
34, 36–41]:
• Class based storage policy. It partitions all products into
a number of classes and reserves a physical portion within
racks for each class. This storage policy is generally managed according to the dispatching rule based on the cube per
order index (COI) introduced by [42]. It is defined as the
ratio of the number of storage addresses allocated to an item
and the number of transactions per period. This rule is applied by routing incoming items with lowest values to the
most accessible (nearest to I/O points) storage addresses of
a facility. In class-based storage policy each class is assigned
to a block of storage locations: for this reason the size of
classes and distributions of products among them have to be
properly designed. Within each block of storage locations,
material is generally stored randomly. The CL-K notation
denotes the number of classes (K ) according to a class-based
policy. Several authors demonstrate that storage policy based
on COI and its correlated assignment rule are the most effective with respect to random storage in unit load picking
systems [43–45]. Studies on picker to part less than unit
load OPS with a class based storage policy are presented
by [21, 22], and [23]. Any shape is possible for each: rectangular, triangular etc. [38]. Park and Webster [37] present
some algorithms for minimizing travel times according to
a class-based storage assignment and in three dimensional
storage systems: they refer to L-shaped classes in automated
OPS. Reference [40] studies the impact of combining storage
policy, tour construction heuristic and storage rack configuration on AS/RS travel time. Reference [3] develops a study
on the OPS optimization based in particular on the shape and
trend of the COI-based ABC curve.
• Turnover based storage policy. Items with highest transaction demand are stored in the most accessible addresses. In
particular, in continuous policy all rack locations are classified according to the following non-decreasing value:
t jin + t jout
where t jin and t jout are the travel time from generic location j
to the input and output stations respectively; and all products
are classified on non-increasing demand per reserved location. The aim is to prevent the generic unit-loads from being
assigned to locations designated for faster moving products.
• Randomized storage. It allows products to be stored anywhere in the storage area.
• Duration of stay storage. This is a rule for randomized systems and is based on the duration of stay.
• Dedicated storage. Materials are assigned to predetermined
locations based on throughput and storage requirements.
• Service level assignment. The aim of this policy is to determine the minimum size that satisfies a service-level objective
(expressed as a probability of a space shortage) in randomized or dedicated storage.
• Correlated storage policy. This is based on correlations between products, and reduces OP travel times by storing correlated products close to each other.
Randomized and dedicated storage are extreme cases of the
class-based storage policy: the former only considers one class
while dedicated storage policy assigns a class to each product;
in other words, class-based storage is a compromise between the
randomized and dedicated storage policies.
4 Batching and routing procedures
The order consolidation policies rearrange customer orders into
picking orders. In single order picking each customer order, generally made of multiple items to be picked within the system
(less than unit load OPS), is directly taken as a picking order.
In order batching picking several customer orders are combined
into a batch, i.e., a single picking order of requests (picking lines
in the picking list) belonging to different customers. The literature presents a great many studies proposing clustering procedures to solve the batching problem [24, 45–49]. In the proposed
study the order batching problem is defined as follows: given
a set of orders, each consisting of a number of orderlines, how
can the amount of orderlines be batched into picking orders according to the capacity of the picking device?
Reference [24] identifies and discusses three principal families of heuristic order batching algorithms:
1. Priority rule-based algorithms,
2. Seed algorithms,
3. Saving algorithms.
The group of research compares two different algorithms. Firstly,
a large set of scenarios are simulated according to the first come
first serve (FCFS) batching rule, i.e., customer orders and related
orderlines are assigned to batches one by one, picking orders of
a defined number of orderlines (i.e., a parameterized number of
slots to be visited) according to the application of the shortest path
(SP) routing policy [50]. Secondly, a heuristic rule based on genetic programming and cellular automation [51] applied to the
traveling salesman problem (TSP) with Chebyshev distances [52,
53] is proposed. In this the picking requests (i.e., the amount of orderlines belonging to an input orderlist, which represents the mission assigned to an AS/R vehicle) are rearranged within the orderlist (TSPmission algorithm) or within pools of orderlines which
saturate the vehicle capacity (TSPtrip algorithm). In other words,
the generic mission is composed of different trips within the storage area according to the finite capacity of the picking vehicle:
the rearrangement of locations to be visited is applied to the entire
mission (TSPmission ) or to each vehicle trip (TSPtrip ). In the latter
condition, TSPtrip is applied many times within the same mission.
The hypothesis is that a set of picking missions, generated randomly and according to a defined storage allocation policy, are
assigned to the generic AS/R truck. This total number of picking
and stochastic requests, which generate random outputs and are
assigned to one vehicle, is calculated in order to predict a picking
cycle time (the performance of the system) for a single mission
with a high level of acceptance (model validation).
5 Dynamic AS/RS model
This section presents the proposed dynamic model of an AS/RS
and concentrates on the multi-level factors which parameterize
the system. The product to picker OPS has been modeled by
the application of an object-oriented visual interactive simulation
(VIS) tool [54]. This model involves more than 40 interrelated
dynamic processes (e.g., initializing, ordering, batching, routing)
and 2000 functional entities (e.g., control points, loads, vehicles, routes). Firstly, by the analysis of real warehousing systems
based on picking activity the most significant parameters have
been collected in order to define the list of principal factors
which could affect the response of an AS/RS. These factors are
the object of the system parameterization process. This set of
factors is composed of the following:
1. AREA (A). This is related to the storage capacity of the
warehousing system and is proportional to the number of locations capable of receiving picking items.
2. CURVE (y). The class based storage location assignment
policy is adopted and is based on three classes (A, B and C).
The generic class is identified by different COI values, according to a COI – Pareto ABC curve that is related to physical stocks and movement frequencies. The notation x/y,
attributed to this factor, indicates that x% of cumulated stor-
Fig. 1. Automated storage and retrieval
system with class based storage allocation. Classes A, B, C
age commits y% of material movements. x is assumed to be
equal to 20.
CLASS (a/b/c). This factor deals with the physical dimension of each class of storage item. The notation a/b/c and a,
b, c values indicate the portion of the total storage capacity
(in percentage), which is associated with classes A, B and C
respectively. If (a, b, c) = (0.10, 0.25, 0.65), then 10% of the
most requested items belong to the first class of movement
(A), which is located very closed to the I/O area. Figure 1 exemplifies the distribution of items within the three classes for
a defined value of class factor.
DIMORD. This is the number of lines of a picking list and
it is associated with a mission assigned to a retrieval vehicle.
The generic picking vehicle begins and ends its mission at
the I/O zone, when it reaches the end of the list. The picking
mission is made of several routes (trips) which begin and end
at the I/O: every time the vehicle saturates its capacity it has
to reach the depot (I/O) area, unload the items collected during the current trip and continue the mission by initializing
a new trip.
CAPACITY. This factor relates to the capacity of the picking machine and to the average number of items picked at
each stop during a vehicle trip. In particular, it represents the
average number of stops per route (i.e., trip).
SHAPE (p&q). This factor is the ratio between the length and
the height of the generic rack (see Fig. 1).
K . This is the ratio between the length ( p ) and the height (q )
of the area dedicated to class A in accordance with a rectangular shape. This value can differ from the ratio p/q (shape
factor). B and C classes’ configuration is L-Shape (Fig. 1).
The previously described batching procedures were tested in
order to quantify the effect of the rearrangement of the pick-
ing requests on the response of the system, i.e., the normalized
picking cycle time (T _Normal). T _Normal is the response of
the system recorded during the what-if analysis and is defined as
the ratio between the generic picking cycle time and the worst
(i.e., the longest) cycle time obtained by the same system configuration but using a random allocation of products. In fact, the
what-if analysis demonstrates that the greatest values of picking
cycle time are the result of a non-application of the class based
storage policy (i.e., c = random in Eq. 3). The T _Normal value
is defined according to the following:
T _NormalArea= A (c, l, s, r, d, k)
CTime(c, l, s, r, d, k/a = A)
max {CTime(c, l, s, r, d, k)i /a = A}
{CTime(c, l, s, r, d, k/a = A)}
= max {CTime(a = A, c = random, s, r = 1, d = 1, k)}
where CTime(c, l, s, r, d, k) is the picking cycle time for the
mission associated with the picking list; a = AREA value,
c = CURVE value, l = CLASS value, s = SHAPE value, r =
CAPACITY value, d = DIMORD value, k = K value.
T _Normal is calculated for different storage capacities
(AREA value) and the relationship 2 offers the opportunity to
compare all values obtained in accordance with different system configurations, storage capacities and operating scenarios.
This response value is capable of measuring the impact of different policies and factor values (e.g., class dimensions, COI-curve,
number of picks per picking list, vehicle capacity) on the system
6 Results and factorial analysis
A multi-level factorial analysis was conducted in order to measure the impact of each factor and combinations of factors on the
throughput capacity of the system (i.e., the inverse of the mean
Fig. 2. Plot of main effects on
normalized picking cycle time
(T _Normal)
picking cycle time, T _Normal). Moreover, thanks to the collection of a large portfolio of simulation results, the simulation steps
and accompanying statistical analysis provide several OPS design guidelines for the choice of the best picking strategies (free
parameters) given a subset of system constraints (unfree parameters). The collected output of every simulation run is the global
picking cycle time for a set of more than 1000 stops (orderlines)
belonging to different trips and picking cycles. All of the principal results are presented in Fig. 2 (plot of main effects) where
the picking cycle time (i.e., travel distance in agreement with
the definition introduced in Sect. 2.1) is normalized in comparison with the cycle time based on a random storage policy. In
this graph the data means of T _Normal (the normalized cycle
time) are a measure of the system performance: the average response of the system is seen when each of the free-parameter
(AREA, RATIO, SHAPE, etc.) changes its value. The dashed
line identifies the mean value obtained from the total number of
simulation runs. From this figure the importance of certain factors affecting the picking cycle time T _Normal, compared to the
other factors, is emphasized. In particular, Fig. 2 demonstrates an
insignificant influence of AREA factor on system performance;
as a consequence, all results presented in this manuscript can
be generalized for a warehousing system with a generic storage
The effect of each factor and each combination of factors
on the response of the system is measured more accurately by
applying the design of experiment (DOE) analysis to the normalized response time of the modeled part to picker OPS. The DOE
analysis is a two-level factorial analysis (i.e., the effect of each
factor is measured according to two levels) which is based on the
following statistical model [55]:
Y = α+
βi X i +
βij X i X j +
βijk X i X j X k + . . . + u
i, j
i< j
i, j,k
i< j<k
where Y is the response of the system, α is a fixed parameter, X i
is the value assumed by the factor i, βi is a multiplying parameter
associated with factor i and is a measure of the effect of i on the
response Y (similarly for βij , βijk , etc.) and u is the error on the
expected response of the system.
In particular, the effect of a factor (e.g., CLASS, CAPACITY) or a combination of factors (e.g., CLASS*CAPACITY,
CLASS*CAPACITY*CURVE) on Y is quantified by the following standardized effect (defined for the factor i in Eq. 5) in
accordance with a t-Student distribution of values:
β̂i − βo
V(β̂i )
AS/R - OPS levels
SHAPE ( p/q)
CURVE (x/y)
A, A/2
5, 10, 15, 20, 25, 30
[1, 3, 5, . . . , 31]
1&1, 2&1, 4&1, 8&1
50/20, 60/20, 70/20, 80/20, 90/20, 95/20
5/40/55, 10/50/30, 20/30/50, 20/50/30
0.3, 1.7, 3.5
where β̂i and β0 are respectively the expected value and the mean
value of βi , and V(β̂i ) is the variance of βi . A standardized effect
defined for a generic combination of factors can be introduced
in the same way. Hypothesis testing is conducted on the value
of β0 according to both the t-Student distribution of t, and the
following null hypothesis:
H: β0 = 0 .
If t (Eq. 5) is a great value the influence of the related factor i
(or combination of factors) on the system’s response becomes
critical. This 2k factorial analysis is the basis for a multi-level
n ki factorial analysis where n i is the number of levels assumed
by factor i: a large number of two-level factorial analyses were
conducted for different couples of levels belonging to the values
reported in Table 1.
The total amount of two-level factorial analyses on the whole
portfolio of simulated scenarios (on values of Table 1) is:
n i 2
Table 1. Factorial analysis. Factors’ levels
= 291600 .
Figure 3 present two final classifications of factors, and combinations of factors, which affect the system’s response for a fixed
value of K (equal to 1.7) and AREA (according to the demonstration of its non-influence). This classification is obtained by
analyzing several different two-level factorial studies; the values
of t, expression 5, have been collected and cumulated.
Figure 3a shows the mean value of t (standardize cumulated
value) calculated in agreement with the following expression:
Std_ti =
ti, j
i, j
i identifies a factor or a combination of factors,
j identifies a factorial study (i.e., an operating scenario),
ti, j is calculated in accordance with Eq. 5.
An absolute standardized cumulated value for each factor and
combination of factors, collected by Fig. 3b, is introduced in accordance with the following expression:
ti, j .
Std_ti, ABS =
i, j
Figure 3a and b shows that CURVE is the most critical factor;
followed by CAPACITY. Then the third critical factor in terms of
cumulated effects is DIMORD, while SHAPE is the third critical
factor in terms of the measure of the absolute cumulated effect
(i.e., in accordance with the absolute value of t).
Figure 4 presents the simulated picking cycle times values
obtained by the application of different batching procedures
Fig. 3. (a,b) Pareto analysis of cumulative standardized and absolute cumulative standardized factor effects
Fig. 4. TSPtrip algorithm and batch procedures’ evaluation
Fig. 5. TSPtrip algorithm and class based storage policy
(batch x, batch y, batch xy and TSP) when the dimension of
the pickinglist (i.e., DIMORD value) is 30. In particular routines
batch x and batch y rearrange the picking requests in accordance
with a non-decreasing value of the following times calculated
from I/O point:
where vx and v y are the velocity of the AS/R vehicle along x
and y (as defined in Fig. 1); (x, y)i is the location of slot i.
Batch xy rearrangement is based on the following Chebyshev
xi yi max , .
The effect of the batching rearrangement in terms of T _Normal
reduction is not great, especially when the class based policy is
not applied (Fig. 5).
7 Conclusions and further research
The design and control of a part to picker OPS is one of the
most critical issues in planning and optimization of modern industrial facilities. The paper presents significant results, obtained
by using a dynamic multi-factorial analysis, capable of supporting the management of AS/RSs operating in flexible conditions.
In particular, a classification of parameters’ effects on the system
performance is presented. The analysis proves to be innovative
for the large number of factors involved in accordance with the
NP-hard complexity of the optimization problem. Specifically,
the integrated use of simulation modeling and statistical factorial analysis proves to be effective. New in-depth studies on
AS/RSs and picking activities (e.g., quantifying and minimizing
global operating costs, changing the position of I/O area, etc.)
are achieved. The measure of the effects of OPS design on the
management of the other supply chain activities (e.g., packaging
and loading processes, purchasing activity, geographical location
of factories and warehousing facilities) is a critical issue that is
not as yet sufficiently discussed in the literature.
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