A simulation model to compare Autonomous vehicle based warehouses with traditional
AS/RS systems
Karel Bauters1, 2
Kurt De Cock1
Joris Hollevoet1
Gianni Dobbelaere1
Hendrik Van Landeghem1,2
Department of Industrial Systems Engineering and Product Design
Ghent University 1
B 9000, Ghent, Belgium
Flanders Make 2
B-3920, Lommel, Belgium
E-mail: [email protected]
KEYWORDS
Automated Guided Vehicles, Warehousing, Automatic
Storage and Retrieval Systems, Order Picking, Simulation,
FlexSim, Modelling, Material Handling Systems, KIVA,
Autonomous Vehicles
ABSTRACT
Contemporary market conditions require highly flexible and
scalable warehousing systems. This leads to the development
of new solutions for parts picking and storage processes.
KIVA systems devised a goods-to-man system that uses
Automated Guided Vehicles (AGV’s) to retrieve mobile
inventory pods from the warehouse. This research analyzes
the performance of such an Autonomous Vehicle based
warehousing system and compares it to the performance of a
more traditional Automated Storage and Retrieval System
(AS/RS). This paper presents a modular simulation model
that evaluates and compares the performance of both
Automated Vehicle and AS/RS warehousing solutions. The
model aims to support decision makers in the design process
of automated warehousing systems.
INTRODUCTION
Warehousing systems have applications throughout many
industries such as manufacturing, warehousing, retail and ecommerce. Every application has its own specific
characteristics, constraints and requirements which lead to
the development of a plethora of different warehousing
solutions. Warehousing systems are typically evaluated on 4
criteria (Schmidt and Schulze, 2009):
 Cost: both investment and running cost
 Service quality: Order lead time, error rate …
 Flexibility: ability to adapt to changing process
parameters
 Scalability: ability to grow with increasing demand
or system load
Nowadays, flexibility and scalability have become
increasingly important due to changing market expectations.
E-commerce customers demand a high variety of products,
small order sizes, high quality and short delivery times at low
cost (Marin and Carrasco-Gallego, 2013). To meet these
demands, most warehouses switched from a traditional
picker-to-parts set-up to automated parts-to-picker systems.
Automated storage and retrieval sytems are the most
commonly used type of warehouse systems today (Meller
and Mungwattana, 1997). AS/RS Systems consist of aisles
of racks through which a storage and retrieval(S/R) crane
moves in both horizontal and vertical direction. The crane
retrieves pallets (I/O) from the racks and brings them to the
input/output station. There it picks up pallets that need to be
stored and puts them back in the warehouse. The main
benefits of AS/RS‘s are very high throughput rates, efficient
use of costly warehouse space, low labor costs and better
tracking possibilities of inventory (Dotoli and Pia Fanti,
2002). Despite these advantages, the flexibility of traditional
AS/RS’s is rather limited due to the physical buildup of the
system and its inability to handle multiple types of handling
units (pallets)(ElMaraghy et al., 2014). Multishuttle AS/RS’s
and miniloads have been developed in recent years but they
could only partially overcome these shortcomings.
In the ongoing search for increased flexibility and scalability,
a new parts-to-picker system based on Automated Guided
Vehicles (AGV’s) has been developed by a company called
KVA systems (MWPVL 2012). The AGV’s are equipped
with a lifting mechanism to pick up picking racks and bring
them to the picking stations. AGV’s and racks can easily be
added to the system when necessary. Furthermore, storage
locations can be dynamically changed in accordance with
changing market parameters. All this provides a level of
flexibility and scalability that cannot be matched by any
other parts-to-picker system to date.
Very little is known about the performance of these KIVA
systems. The fact that Amazon acquired KIVA systems
swiftly and subsequently took the products from the market
for their own use, suggests at least that the performance of
this system could be spectacular and thus requires research.
In this research paper, the performance and behavior of these
systems is analyzed and compared to traditional AS/RS’s
through a detailed simulation model.
AUTONOMOUS VEHICLE WAREHOUSE SYSTEM
Principle and mechanics
The key innovation in the Kiva System is the application of
robots/AGV’s capable of lifting and carrying around shelving
units, called inventory pods. The robots itself are called drive
units and move the inventory pods from a storage location to
a picking station and back. These picking stations can be
used for order fulfilment or for replenishing goods to the
inventory pods. Figure 1 shows an example of a typical
order-picking station.
Figure 1: picking station layout (Cardinal, 2012)
The drive units are small enough to drive underneath the
inventory pods. The electrical lifting mechanism then raises a
shelf that supports the inventory pod. The shelf can be turned
to prevent the pods from spinning and keep them stable when
the robot moves through the warehouse.
The pods consist of tray and bins located on a number of
shelves. The variety of tray and bin sizes creates a mixture of
storage locations which are easily adaptable to fit the
requirements of the products in the warehouse
A warehouse equipped with this system typically consists of
3 main zones:
• A storage zone where inventory pods (and thus products)
are located. Aisles in between inventory pods provide
enough place for moving inventory pods to pass.
• A picking zone where operators receive orders and pick the
correct products from the inventory pods delivered by drive
units.
• A no-human zone where only drive units pass to go from
the storage zone to the correct picking station (also referred
to as the buffer zone).
Figure 2 gives an overview of such a typical layout.
Figure 2: Autonomous vehicle warehouse layout
In this picture the inventory pods are arranged in blocks of 5by-2. Because this arrangement is commonly used, we will
implement it in the simulation model as well. Note that
changes to the way the racks are organized can be made
depending on the requirements of the warehouse. Even
blocks which are 3 or more racks wide can be used to use the
available space more efficiently. In that case slow moving
articles are placed in the middle of the blocks because
retrieval times for those locations are higher.
The picking stations are equipped with barcode scanners and
a pick-to-light system, which are controlled by a computer
connected to the main network. These tools are used to
minimize the picking error in order to have maximum orderaccuracy. Replenishing the inventory pods is done in stations
with exactly the same layout and setup, which again adds
flexibility to the system. Stations near the packing and
shipping departments will more likely be used as picking
stations than the ones near the drop-off points or unloading
docks in the warehouse.
The main efficiency gain of the system lies in the fact that
picking operators only perform actual picking tasks, while
inefficiencies due to moving, searching and mistakes are
minimized. Warehouses based on autonomous vehicles
should be able to deliver a new pod every six seconds which
sets a baseline picking rate of minimum 600 picking lines per
hour per operator (Wurman et al., 2008). However, this rate
will even increase when the operator can pick multiple parts
per pick face.
System control
KIVA systems is a distributed system with many independent
agents, so emergent behavior is possible (but unknown at this
time). This contrasts with traditional AS/RS systems, which
are highly constrained in their architecture. The location of
the movable pods can be adapted dynamically and matched
with the current demand specifications. Seasonal products for
instance, will be stored very close to the picking stations
when their demand is high. When the demand drops, these
inventory pods will move further to the back of the
warehouse. The control of this system can be seen as a huge
real-time resource allocation problem with resources as shelf
space at the station, product quantity (inventory), drive units,
storage pods and physical space.
Each drive unit in the system has a drive unit agent (DUA)
and each station an inventory station agent (ISA). The whole
system then is centralized in the Job Manager (JM) which
takes care of the resource allocation. These agents all
communicate through XML messages. The JM receives
customer orders and assigns drive units, inventory pods and
(a) station(s) to carry out the different tasks. When a pod
arrives at a picking station, the ISA makes sure the light
indicates the corresponding product to pick to increase order
accuracy. The ISA then communicates with the other agents
to request possible replenishment and/or any other tasks.
The DUA on the other hand needs to guide all drive units in
order to have no collisions and fast transport from point A to
point B. The drive units are equipped with 2 camera’s that
can read (2 by 2”) QR-code stickers (Figure 3) to determine
their current location.
The models are used to analyze the behavior of the system in
changing circumstances. The model is therefore made
modular. This way, new models can easily be generated from
the information provided by an input file instead of having to
create a new model manually for every change in the
parameter set.
Figure 3: QR location tag
The drive unit communicates with the DUA and a heuristic is
then used to determine the best route for that particular drive
unit.
The goal of the control logic aims to maximize the utilization
of the (operators in) the picking station while reaching the
required throughput levels with the least possible amount of
drive units, warehouse space and inventory. Finding an
optimal solution of this large optimization model in real-time
is impossible. Furthermore, human operators are involved in
the systems, creating uncertainty and variability for the
model. For these reasons, the system relies on a number of
heuristic to control all operations in the warehouse.
The job assignment is the task of assigning orders to workers
in different stations. Picking costs decrease when an operator
can pick multiple products from a pod. This is more likely to
happen when multiple orders in the same station share
common products. The heuristic therefore aims to assign
similar orders to the same picking station.
Once an order is located to a specific workstation the picktask assignment kicks in. By knowing the products in the
order, the system determines which inventory pods are
necessary. These pods get then assigned to certain (available)
drive units. The heuristic combines distance and the number
of needed products that are available on the candidate pods.
A third and last assignment action is related to the
replenishment of inventory pods. When the inventory drops
below a certain level, the product needs to be replenished.
The replenishment assignment heuristic will determine where
to store these parts, orders a drive unit to pick up the required
inventory pod and communicated the pods’ new location
after replenishment in the station. The current and next (after
being call by a picking- or replenish station) position of a
pod depends on the chance that a product on a pod is going
to be ordered again soon. No specifics regarding the
algorithms used are currently available in literature.
All these assignments are done in real-time based on the
information the system gets from the picking stations, drive
units, order lists … This ensures a very flexible and modular
system which is able to adapt in no time to any changes in
warehouse requirements or parameters.
SIMULATION MODEL
This section provides a detailed explanation of the simulation
models for both the Autonomous Vehicles and the AS/RS’s.
The models are made in Flexsim®. Flexsim is commonly
used to model, simulate, analyze and visualize processes in
numerous industries such as manufacturing, warehousing and
healthcare.
Order- and product lists
A number of different order- and product lists with different
characteristics were created to compare the performance of
both systems. These lists can be implemented in the model in
Flexsim but this enlarges the model drastically. This caused
errors and crashes when running the model. To solve this
problem, a SQL database was set up and linked with the
Flexsim model. The database contains most of necessary data
for the model (locations, demands, order lists …). The
possibility to write/read data to and from the database
independently from Flexsim, enables us to run multiple
models in parallel. For this model we opted for a MySQLserver and used HeidiSQL as a client. An excerpt from an
order list is shown in Table 1.
Table 1: Excerpt from order list
checkpoint
order number
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
2
2
2
3
3
3
3
3
ProductsInOrder ProductId
3
3
3
4
4
4
4
5
5
5
5
5
Count
4042
2949
4891
2887
2699
5498
6636
7662
1077
4407
4991
5975
Distribution
2
3
2
4
5
4
5
2
2
1
2
2
2
2
2
2
2
2
2
2
2
2
2
2
Order lists are generated through a script implemented in
Flexsim. The script builds these order lists based on userdefined parameters and stores them in the database.
Table 2 gives an overview of all generated order- and
product lists.
Table 2: Generated order and product lists
number
1
2
3
4
5
6
7
8
9
10
11
12
#products
10000
15000
20000
25000
10000
10000
10000
10000
10000
10000
25000
25000
#products Checkpoint Checkpoint
per order
Number distribution
1-5
5000
1-4
1-5
5000
1-4
1-5
5000
1-4
1-5
5000
1-4
1-10
5000
1-4
1-15
5000
1-4
1-5
10000
1-4
1-5
15000
1-4
1-5
5000
1-6
1-5
5000
1-8
1-5
25000
1-4
1-5
12500
1-4
The number of products indicates the total number of SKU’s
stored in the warehouse. The number of products per order is
uniformly distributed and equals the number of different
SKU’s in one single order. The checkpoint number is
implemented to simulate seasonality. Product preferences
change throughout the year (Christmas period, Valentine’s
Day, etc.) and certain warehouse systems are better in
dealing with fluctuating product preferences than others.
Seasonality will chance when a certain number of orders is
processed, indicated by the checkpoint number. The choice
to change seasonality after a fixed number of orders rather
than a certain time interval, was made to enable us to
compare the throughput of different warehousing systems on
an equal basis. The checkpoint distribution uniformly divides
the parts in pools of products that have their own probability
of occurrence in an order. This probability changes following
a checkpoint to simulate the seasonality.
Automated storage and retrieval systems model
One of the requirements of the simulation model was a high
degree of modularity. This was achieved by first building a
model that contains all basic elements of an AS/RS
warehousing system. In this model, the size of the racks is
fixed. All objects in the model are programmed to be
generic if possible and custom code was added to enable the
communication with the SQL server. All these objects were
stored in a user library. These objects can then be called from
a custom Flexsim script to add the desired cranes, racks and
picking stations to the model. Changing the size of the racks
can easily be done by manually changing the rack object in
the library before running the script. A screenshot from an
example model containing 4 cranes and 7 picking stations is
shown in Figure 4.
If so, the box is diverted to the station, otherwise it just
continues its way along the conveyer. When products arrive
in the picking station, a function is triggered to read how
many units are needed to determine the stay time of the box
in the station.
Every AS/RS crane is connected to two racks. The racks
store all information on the location of the parts it contains in
the SQL database. When parts return to the rack, their new
location will be determined based on the demand rate of that
part in order to minimize the travel times of the cranes.
Picking stations send message to the cranes when a new
order set is imported in the model. The cranes will check
which parts are located in the racks they are connected to and
start retrieving these parts from the racks. All AS/RS cranes
work in a dual command mode. This means that, after a
retrieval, the crane will first check if there parts available for
storage in the I/O station. If that is the case, the crane will
first bring this part to the racks before retrieving another part
on its way back to the I/O station.
Parts are transported from the racks to the picking station and
back by means of a conveyer belt. Decision points (DP) are
added to the conveyer system to direct the parts to the right
location as shown in Figure 5. When a part passes DP A, the
conveyer checks whether or not this part needs to be
presented in the corresponding picking station or not and
sends is respectively to DP B or DP C.
Figure 5: Decision points for routing to picking stations
The same logic is used to determine the routing of the parts
at the AS/RS racks. (Figure 6)
Figure 4: Screenshot from the AS/RS model
In the remainder of this section, the basic components and
logic of the model will be discussed.
The picking stations can process up to 10 orders
simultaneously. Which means that, in practice, a maximum
of 10 order totes can be placed and processed in the picking
station at the same time. This batching parameter can be
easily changed (between 1 and 10) in the experimenter when
the simulations are run. Each picking station contains a
limited list with the orders it needs to process. When the
number of order lines in that list drops below a certain level,
a function is called to import a new set of orders from the
order database. The part numbers of the SKU’s in this new
order set are then communicated to the cranes, which start
retrieving these parts from the warehouse whenever they are
finished processing the previous order set. Parts travel from
the cranes to the picking stations by a conveyer belt. When a
box passes a picking station, a function is called to check
whether or not this part needs to be presented in this station.
Figure 6: DP for routing to AS/RS cranes
Autonomous vehicle (KIVA) model
Since KIVA systems are not commonly used in industry yet,
there are no objects, such as the AS/RS racks and cranes,
readily available to model this type of warehousing system.
Therefore the whole model is built up using standard Flexsim
components which were modified to fit the needs of the
KIVA model. A Flexsim script, similar to the one described
in the previous section, was devised to add objects and their
connections to the model. Again all data necessary to run the
model is stored in an SQL database to speed up the model.
The components in the model are now briefly discussed in
the remainder of this section.
One of the most important parts of the model is the grit. This
grit contains waypoints in which the routing logic of the
drive units is stored. In this grit, 2x5 blocks of racks are
used. Each block of racks is surrounded by two vertical drive
lanes which are bidirectional and will only be used by loaded
drive units. To overcome the problem of deadlocks, onedirectional lanes were implemented underneath the racks for
empty drive units. Furthermore, 2 return lanes were placed
on the outside of the warehouse. The buffer zone in between
the warehouse and the picking station enables easy routing of
the AGV’s to the desires picking station. Figure 7 gives an
overview of the complete model.
Figure 7: overview of the autonomous vehicle model
The routing principles for the drive units are hard coded into
the control points in the grid. As mentioned before, empty
AGV’s move up and down the warehouse underneath the
racks. The grit recognizes empty AGV’s through a custom
label in the AGV object which changes when parts are
picked up or dropped off. The return lanes are implemented
for the inventory pods that are brought back to the
warehouse. Loaded AGV’s will travel from the picking
stations upwards to the warehouse on these lanes. When the
AGV reaches the right level, it will then move horizontally to
the desired lane to drop off the parts. The left return lane is
used to go to the odd rows, the right one is used to go to the
even rows. This way, a directional flow in the warehouse is
created to minimize the risk of deadlocks and collisions.
The AGV’s in the model are basically the standard AGV
objects from the Flexsim library. Small adjustments were
made to make it possible to change the travel speed by
changing a parameter in the input variable list when the
model is generated. The picking station consists of a normal
processor, a BasicFr (basic fixed resource – basic model in
Flexsim which can be customized with custom coding) and
some lanes with decision points. The BasicFr triggers the
AGV’s to pull a rack to the station. The process time for the
part is determined in the same way it was done for the AS/RS
model. Once the order is processed in the picking station, the
model checks for other orders in other stations that require
the same rack. If these orders exist, the AGV is directed to
the picking station; otherwise an SQL function is triggered to
find a new location for the inventory pod in the warehouse.
RESULTS
Autonomous vehicle analysis
Following parameters in the model can be changed:
A. # sku’s per rack
B. # AGV’s used
C. # orders per picking station
D. AGV speed
E. # picking stations
F. Charging power
G. Picking time distribution
H. Free rack space
Most of these parameters are rather intuitive, although two of
them need some further explanation. The number of sku’s
per rack indicates how many different sku’s will be assigned
to one single rack. This parameter can be changed to
accommodate different inventory levels or take into account
the dimension of the parts stored in the racks. The lower the
number of sku’s in a rack, the higher the total number of
racks in the warehouse will be.
Free rack space indicates the number of unused rack
locations in the model. Free rack space can be used to shift
rack positions according to changing product preference.
DoE parameter selection
The goal of this section is to investigate which of the
previously listed parameters have a significant influence on
the systems performance. The same order data was used for
all runs (order list 1 – Table 2), so the influence of
parameters can be compared on the same basis.
Table 3 gives an overview of the different parameter settings
that were used in this analysis.
Table 3: Parameter significance scenarios
Factor
letter
A
B
C
D
E
F
G
H
Factor Name
1
2
Level
3
4
5
# sku’s per rack
40-80 50-90 60-100 70-110 80-120
9
8
7
6
5
# orders per picking station
13
11
9
7
5
AGV speed
3
3,5
4
4,5
5
# picking stations
11
10
9
8
7
Charging power
50
55
60
65
70
Picking time distribution
5-7 6-8
7-9 8-10 9-11
Free rack space
20
30
40
50
60
# AGV’s used
The first and most important KPI is the throughput of the
system. Figure 8 shows the effect of changing the level of
every parameter on the total systems’ output.
The same analysis was done on the utilization of the picking
stations and AGV’s and the average order lead time. These
results are visualized in following graphs. (Figure 10, Figure
11 and Figure 12)
Figure 8: influence of system-specific parameters on
throughput
The graph shows clearly that the throughput is mainly
influence by the number of SKU’s per rack and the number
of AGV’s in the system. On the other hand it also indicates
that the charging power and the free rack space have very
little or no influence at all. Surprisingly enough is the number
of picking stations of rather low importance, which is
counterintuitive. This could be explained by a high
utilization of the AGV’s, which would make the marginal
gain of an extra picking station very low. The following table
(Table 4) describes a set if scenarios devised to check this
hypothesis.
Figure 10: influence of system-specific parameters on
utilization of AGV's
Table 4: interaction between number of AGV's and
number of picking stations
Factor
letter
A
E
Factor Name
# AGV's used
#picking stations
1
9
1
Level
2
3
12
15
3
5
4
18
7
Figure 9 visualizes the marginal effect of changing the
number of AGV’s and picking stations on the throughput and
station utilization. It is clear that the number of picking
station does influence the systems throughput under the
condition that there are enough AGV’s available.
Figure 11: influence of system-specific parameters on
utilization of the picking stations
Figure 12: influence of system-specific parameters on the
average order lead time
Figure 9: Marginal effect of changing #AGV's and
Picking Station on Throughput
From these graphs we can learn that the charging power and
the free rack space have a very low significance level.
Therefore we will not include them in the DoE model. A full
factorial design with the 6 remaining parameters and 2
Levels takes 64 runs. The two extra parameters would raise
the required number of runs to 256. Table 5: DoE parameter
levels summarizes the parameter settings used in the DoE.
These parameter settings are rather extreme but still realistic
enough to draw conclusions.
Table 5: DoE parameter levels
Level
Factor
letter
A
B
C
D
E
G
Factor Name
# sku’s per rack
# AGV’s used
# orders per picking station
AGV speed
# picking stations
Picking time distribution
-
+
Figure 15: DoE - interaction plots
40-80
9
7
3
2
5-7
80-120
18
13
4
5
8-10
The results of the DoE are analyzed in R. The parameters
chosen for the model using R are the 6 main parameters and
their interactions (57), together with one implicit constant. A
Daniel plot was constructed to select the most significant
effects. From this plot (Figure 13) we learn that the main
effects A, B, C, D, E and G are significant together with their
interactions BE, BG, EG, AE, DE, AC, BEG, BDE and
ABE.
Figure 13: DoE - Daniel plot
Main effect and interaction plots are used to validate these
results.
Figure 14: DoE - Main Effect plot
Subsequently and ANOVA (analysis of variance) was
performed to reevaluate the significance of the effects that
are still in the model. Insignificant effects are again removed
before performing a regression analysis on this model. The
results of the regression analysis are summarized in
following formula:
Throughput = 173.16 + 9.87A + 41.62B + 1.62C + 4.04D +
18.14E - 7.36G + 1.41AC + 1.67DE + 4.51AE + 4.85EG 5.29BG + 15.89BE + 0.54AB - 0.2BD +2.39ABE +
2.79BDE + 3.10BEG
Based on the DoE, the highest throughput found was 284
orders/hour. This was achieved by having all levels high
except for the picking times parameter since the fastest picks
are done within that level.
A similar analysis was performed on the utilization of the
picking stations. Without going into further detail, the results
of this analysis are summarized in following formula:
Ut_picking_station = 45.09 + 1.28A + 9.14B – 14.6E +
3.01G-0.86BE + 0.62BG - 0.77EG+2.11BEG
System behavior analysis
In the previous section, the behavior of the system under
changing system-specific parameters is described. This
section analyzes the effect of changing global parameters,
such as product preference and order characteristics, on the
systems’ behavior. For this analysis we will investigate the
performance of a warehouse with fixed system-specific
parameters for a set of different order lists.
Table 1 summarizes the system-specific paramters used in
this analysis.
Table 6: Behavior analysis - system-specific parameter
settings
Level
Factor Name
Factor
Value
# sku’s per rack
40-80
# AGV’s used
16
# orders per picking station
13
AGV speed
4
# picking stations
5
Charging power
60
Picking time distribution
5-7
Free rack space
30
different racks that need to be presented to the picking
stations. More retrievals per order combined with higher
picking times per order in the picking station, results in a
lower throughput value. It is important to notice the
throughput doesn’t decrease linear with an increasing amount
of parts per order. The number of parts leaving the system
every hour will thus increase.
The effect of changing the global parameters on the
utilization of AGV’s and picking stations was also
investigated. The results of this analysis are shown in Figure
17 and Figure 18 respectively.
The global parameters and their level values are summarized
in following Table 7.
Table 7: Global parameter scenarios
Factor
letter
A
B
C
D
Factor Name
# products
# products in order
Checkpoint number
Distribution
1
10000
1-5
5000
1-4
Level
2
15000
1-10
10000
1-6
3
20000
1-15
15000
1-8
Figure 17: Global parameter effect on AGV utilization
The effect of changing these parameters over the previously
described levels on the throughput of the system is visualized
in Figure 16.
Figure 18: global parameter effect on picking station
utilization
Figure 16: Global parameter effect on throughput
The graph clearly shows that the checkpoint number has
almost no influence on the throughput. This means that the
system is perfectly capable of reorganizing the warehouse
according to changing product preferences without a
noticeable loss of performance. The number of products in
the warehouse and number of products in one order on the
other hand, have a negative influence on the throughput. The
first effect can be explained by the fact that an increasing
number of parts in the system requires a larger warehouse,
which leads to higher travelling times for the AGV’s. The
latter effect can easily be explained by the fact that the
throughput is expressed in number of orders processed per
hour. Orders with a higher number of parts require more
The utilization of the AGV’s in the system stays quite stable
and high over all scenarios. This means that the AGV’s are
almost constantly busy retrieving parts from the warehouse
or reorganizing parts in the warehouse. This immediately
explains the perceived effect of the number of product pools
and the utilization of the picking stations. More product
pools means that more racks will be reorganized. When the
AGV’s are used for reorganizing the warehouse, they cannot
bring enough parts to the picking station, resulting in a
decrease of the utilization.
Comparison between KIVA and AS/RS
To compare the performance of an AS/RS system to a
warehouse using autonomous vehicle, an AS/RS model
similar (in cost) to the KIVA system described in the
previous section was generated. For this, we assumed equal
costs for the picking stations and racks. An AS/RS crane
costs about $125.000 while an AGV costs about $35.000.
This means that a system with 5 AS/RS cranes compares
quite well to the system with 16 AGV’s described earlier.
The AS/RS system-specific parameters used are:
• Number of cranes: 5
• Number of picking stations: 5
• Number of racks: 10
• Number of orders per picking station: 13
• Crane speed (Hor. and Ver.): 3m/s
• Crane (de)acceleration (Hor. and Ver.): 1.45m/s2
• Conveyor speed: 1m/s
Figure 19: Comparison KIVA vs. AS/RS
From a look at Figure 19 we learn that the influence of
changing global parameters on the AS/RS’s performance is
similar to the effect it has on the KIVA system. However, the
throughput of the AS/RS system is consistently lower than
the throughput of the autonomous vehicles. Similar results
were found when comparing the utilization levels of the
AGV’s and picking stations. The parameters seem to have o
comparable effect on the KPI’s. However, the utilization of
the picking stations in the AS/RS model is about 10% higher
than the one in the autonomous vehicles model. This can be
explained by the way picking times are calculated. Parts in an
AS/RS warehouse are delivered to the picking station in
bulk, resulting in significantly larger picking times. Hence
the higher utilization, although the throughput is lower.
CONCLUSION AND FURTHER RESEARCH
In this paper, a detailed simulation model to investigate the
performance of a warehouse system based on the use of
autonomous vehicles and mobile inventory pods was
presented. The presented simulation model is made fully
modular and a script was developed to generate the model
automatically. All data necessary to run the model is stored
in an SQL database in order to make more efficient use of
computing power and memory.
From the experiments done in this research, we can conclude
that the KIVA system outperforms a traditional AS/RS
consistently. Because of the dynamic character of the system,
it is perfectly capable of coping with changing requirements
and market conditions. Moreover is the scalability of the
system unmatched by any other warehousing solution.
Further research good be done to implement the
replenishment of the warehouse in the model to make it more
realistic. The simulation model could also be extended with
some optimization features to make it more usable as a
design tool.
REFERENCES
Dotoli, M. and M. Pia Fanti. 2002 “Modeling of an AS/RS serviced
by rail guided vehicles with petri nets: a control perspective.”
In proceedings of International conference on Systems, Man
and cybernetics, IEEE
ElMaraghy, H.; Güller, M. and Hegmanns, T. 2014. “Variety
Management in Manufacturing Simulation-based Performance
Analysis of a Miniload Multishuttle Order Picking System”. In
Procedia CIRP, Volume 17, pp. 475-480
Marin, L.F and R. Carrasco-Gallego. 2013. “A design framework
for flexible automated warehouses”. In Proceedings of the 7th
international conference on Industrial Engineering and
complexity management, (Valladolid, Spain, July 10-12)
Meller R.D. and A. Mungwattana. 1997. “Multi-shuttle automated
storage/retrieval
systems”.
IEEE
transactions,
Vol.29, Issue 10, pp. 925–938
Schmidt, T. and F. Schulze. 2009. “Future approaches to meet
complexity requirements in material handling systems”. FME
Transactions, Vol. 37, pp159-166
Wurman, P.R.; R. D’Andrea and M. Mountz. 2007. “Coordinating
hundreds of cooperative, autonomous vehicles in warehouses”.
In Proceedings of the 19th national conference on Innovative
applications of artificial intelligence, Vol. 2, pp1752-1759
WEBREFERENCES
MWPVL: Distribution center order picking technologies compared.
http://www.mwpvl.com/html/order_pick_technologies.html.
Accessed: 2016-07-25.
David Cardinal: Warehouse robots come of age.
http://www.extremetech.com.
Accessed: 2016-09-05