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In this paper an overview on automated storage and retrieval system AS/RS is denoted. In industries AS/RS systems are the main task that designed for automated storage and retrieval of things in manufacturing where their application vary widely from simple storage and retrieval system for small parts to central systems where production, assembly, and manufacturing operations are concentrically located around them. The selection of the storage system depends upon the available space, weight of items to be stored, method of storage operation and another factors that take the important role in the design of the automated storage and retrieval systems. This paper will have all argument that needed to construct AS/RS system in a survey form which will gives us a highlight about the factors that consider the backbone to build the warehouses. The performance of AS/RS will be the result for interaction of many complex and stochastic subsystems. The differences among the surveyed approaches are discussed and the results are summarized. General Terms Automated storage and retrieval system.
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The Automatic Storage and Retrieval System: An Overview
ArticleinInternational Journal of Computer Applications · November 2019
DOI: 10.5120/ijca2019919603
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International Journal of Computer Applications (0975 8887)
Volume 177 No. 16, November 2019
36
The Automatic Storage and Retrieval System: An
Overview
Hanan M. Hameed
Electrical Engineering Dept.
University of Basrah
Basrah, Iraq
Kharia A. Al Amry
Electrical Engineering Dept.
University of Basrah
Basrah, Iraq
Abdulmuttalib T. Rashid
Electrical Engineering Dept.
University of Basrah
Basrah, Iraq
ABSTRACT
In this paper an overview on automated storage and retrieval
system AS/RS is denoted. In industries AS/RS systems are
the main task that designed for automated storage and
retrieval of things in manufacturing where their application
vary widely from simple storage and retrieval system for
small parts to central systems where production, assembly,
and manufacturing operations are concentrically located
around them. The selection of the storage system depends
upon the available space, weight of items to be stored, method
of storage operation and another factors that take the
important role in the design of the automated storage and
retrieval systems. This paper will have all argument that
needed to construct AS/RS system in a survey form which
will gives us a highlight about the factors that consider the
backbone to build the warehouses. The performance of AS/RS
will be the result for interaction of many complex and
stochastic subsystems. The differences among the surveyed
approaches are discussed and the results are summarized.
General Terms
Automated storage and retrieval system.
Keywords
Multi- robot system, AS/RS, warehouses, workstation.
1. INTRODUCTION
The Automatic Storage and Retrieval System (AS/RS)
generally it can be known as material handling support
systems those are in majority used in automated factories,
distribution centers, warehousing and non-manufacturing
environment.
Basically, Warehouses can be classified into three types:
Distribution warehouses
Production warehouses
Contract warehouses.
In distribution warehouses, products from different suppliers
are received and stored for delivery to several customers.
Production warehouses are usually used for storing raw
materials, semi-finished products and finished products in a
production facility. In a contract warehouse, warehousing
operations are performed on behalf of one or more customers
[1].
Today, quite recently appreciable attention has been going in
the direction of automation for storage and retrieval system
(AS/RS) by the industrial companies. AS/RS represent an
innovation alternative to controlling and managing
warehouse. Many benefits can be obtained from the
automation of AS/RS systems such as saving in labor costs
and floor space ,the ease and the speed of handling items and
improved throughput level (Fig. 1) show basic element of
general AS/RS system that consist of one or more storage
aisles that are serviced by a Storage /Retrieval machine . The
stored material are held by a system of storage racks and
aisles[2]. The S/R machine are used to deliver and retrieval
materials in and out of inventory. There are one or more
input/output stations in each AS/RS aisle for delivering the
material into the storage system or moving it out of the
system. In AS/RS terminology, the input/output stations are
called Picked and Deposit station [3].
For mobile robot AS/RS system S/R machine will be a mobile
robot that take the roll of S/R machine for storage and
retrieval material between rack and input/output station so,
this research will discussing the development in AS/RS
system reaching for using the mobile robot for these systems
(Fig. 2) where the term Flexible Manufacturing System
generally means a fully automated system that consist of four
basic elements which are:
Robot
Workstation
Material transport and storage system
Computer controlled system [4].
2. HISTORY OF AS/RS
The AS/RS is a major category of material handling
equipment. There are primarily two types of AS/RS, unit-load
AS/RS and the mini-load AS/RS. AS/RS usually consists of
conveyors, storage racks SR and automated S/R machine that
can travel along narrow aisles between the SR to store and
retrieve loads. The S/R machine can manipulate either pallets
(unit-load system) or totes (mini-load system) [5]. Over the
past 50 years, many studies of AS/RS had been performed
within the material handling research community. The
intensive development of AS/RS began with the development
of informational and computer science. Hausman et al.
(1976), Graves et al. (1977) presented travel time models for
AS/RS assuming that the SR was square-in-time, which meant
that times to the most distant column tx = L SR / vx and tier
ty = H SR / vy (L&H length and height ) were both equal (tx
=ty).Distance IR sensor.
They analyzed different storage strategies, e.g. randomized,
turnover based and class-based storage assignment rules.
Gudehus (1973) presented principles for calculations of the
cycle times for the Single Cycle SC and Dual Cycle DC. In
the case of the SC the S/R machine could perform one storage
or retrieval request, only. More advanced is the DC where the
storage and the retrieval request are done simultaneously by
the S/R machine. With regard to other cycle time expressions,
he considered the impact of the acceleration and deceleration
rate on travel times.
International Journal of Computer Applications (0975 8887)
Volume 177 No. 16, November 2019
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Fig. 1 General form of Basic Automatic Storage and Retrieval System.
Fig. 2 .Layout of the Robotic AS/RS System.
Bozer and White (1984) presented an analytical travel time
model for calculating the SC and DC for non-SIT (Square-In-
Time) racks. Their models were based on randomized storage
and retrieval with different I/O configurations of the input
queue. Their analytical travel time model was based on the
assumption that the S/R machine travels all the time at
constant velocity. Hwang and Lee (1990) presented travel
time models by considering the operating characteristics of
the S/R machines for AS/RS and non-SIT racks. Sari et al.
(2005) have presented the travel-time models for the 3D flow-
rack AS/RS. They have introduced the flow-rack, where
Transport Unit Load (TUL) loaded by the S/R machine by one
end of the rack, travels to another end of the rack to be
retrieved. For the storage operation, the S/R machine operates
in the same way as the S/R machine in the unit-load AS/RS.
However, the retrieval operation for a particular TUL requires
that the S/R machine removes all TUL stored in front of the
requested TUL. The acceleration/deceleration effect on the
travel time was not considered by the authors. Lerher et al.
(2006) developed analytical travel time models for multi-aisle
AS/ RS by considering the operating characteristics of the S/R
machine. Using the proposed analytical travel time models,
average travel time can be evaluated. Gu et al. (2007)
presented a comprehensive review of research on warehouse
operation. De Koster at al. (2008) have presented an optimal
storage rack design for the 3D compact AS/RS. They have
introduced the combination of the S/R machine for TUL
movement in the horizontal and vertical directions and the
system of inbound and outbound conveyors (powered or non-
powered) for the depth movement. Roodbergen and Vis
(2009) presented a comprehensive explanation of the current
state-of-the-art in AS/RS. Rouwenhorst et al. (2000) presented
a comprehensive review of warehouse design and control.
Lerher et al. (2011) presented simulation analysis of a mini-
load multi-shuttle AS/RS. Recently Bortolini et al. (2015a)
proposed an extension for analytical models when computing
the expected travel time for the SCs and DCs of AS/RS in
three-class-based storage systems. Later, Bortolini et al.
(2015b) proposed non-conventional easy-applicable
configuration for unit load warehouses with diagonal cross-
aisles. Accorsi et al. (2015) presented time and energy based
assignment strategy for unit-load AS/RS warehouses.
Janilionis et al. (2016) presented a comparison between
routing algorithms for storage and retrieval mechanisms in
cylindrical AS/RS [6]. E. Vijayaragavan, Sanketh Bhat,
Abhishek Patel and Divyang Rana at 2018a perform study to
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Volume 177 No. 16, November 2019
38
design and analyze of a mobile robot for storage and retrieval
System wherein a load of 550N was applied on the robot, to
simulate the weight of the storage rack. The results of the
analysis are discussed and their implications on the
performance of the robot are studied, which help in assessing
the performance and efficiency of the robot with respect to its
aim [7]. Imen Kouloughli, Pierre Castagna and Zaki Sari at
2018b presented a completely new model named SRMAS
(Storage and Retrieval for Multi-Agent System) that
developed a novel and original because it merges two
techniques carefully chosen and adapted to AS/RS
gravitational conveyors which led to a tidy rack [8]. Also, A.
T. Rashid, F. R. Ali and O. T. Rashid presented several
algorithms that deal with the enhancement the length of the
paths and the time of arrival in the static and dynamic object
store system using the line follower robot and representing the
trajectories by the Bezier curve and the digital differential
analyzer algorithms [9-13].
3. THE STORY OF AS/RS SYSTEMS
In typically configuration, the S/R machine carries at most
one pallet. Pallets for storage arrive at the input station and
wait at an accumulator conveyor until the S/R machine
transports these to their storage locations. This makes it
necessary to always select the first pallet from the input
queue, i.e. First Come First Served (FCFS). The S/R machine
deposits retrieval loads at the output station. The S/R machine
has three independent drivers for horizontal, vertical and fork
movement. Hence the travel time of the S/R machine is
measured by the maximum of the isolated horizontal and
vertical travel times. Figure (3) gives an overview of AS/RS
systems, based on the type of crane, type of the loads and the
types of racks that may be used. The overview also includes
carousals and mobile racks, which are usually not considered
as part of the classical AS/RS systems [14].
4. DEFINTION OF THE AS/RS
AS/RS system that can be defined as a combination of
equipment’s and controls which automatically handle, store
and retrieve materials with great speed and accuracy, without
direct handling by a human worker[15].In general automated
storage and retrieval system is a set of structured parts that at
the end gives us AS/RS systems such as single or more than
this of aisles which consist of multi-tiered racks; stacker
crane ( S/R machine); input/output (I/O) stations ;
accumulating conveyors and a central supervisory computer
and communication system. AS/RSs require serious analysis
during the initial design phase because at this stage the
designers determine the capacity and throughput system. For
example, during the design phase, managers make decisions
on the rack configuration and capacity (single or double
depth), the number of aisles and storage/retrieval machines
(S/R machine) as well as the location of the Input/output
station (I/O station). Once the AS/RSs are implemented, a
number of control decisions must be made to obtain the
performance .These control decisions include decisions on
storage policies, the location point of the S/R machine and
scheduling, etc. [16].
In order to give a comprehensive study about AS/RS must be
we look forward for the steps that required to structure these
systems [15]. The main stages for design any AS/RS is
physical design stage and decision design stage. So for
physical design stage several types of AS/RS are generally
classified according to their physical configuration, namely.
The number of S/R machines and their capacity,
The disposition of racks and aisles in the system.
The positions of the input/output station,
The depth of the racks i.e. maximum number of
products that could be stored in the same cell.
Fig. 3 Various System Concepts for AS/RSs.
Several AS/RS configurations are used in manufacturing
systems and distribution centers, such as: unit-load, mini-
load, man-on-board, deep-lane and flow-rack [17]. While the
application of control policies must manage the system in
order to obtain the maximum profitability. Sequencing the
storage and retrieval requests is one of the important policies.
The company has to determine the sequence in which the
storage and retrieval requests are conducted in order to
maximize the performance of the AS/RS system. The
performance measures may differ according to what the
company want such as travel time per request, number of
requests handled per time period, total time required to handle
a certain number of requests, waiting times of the cranes,
waiting time of requests to be stored/retrieved [18,19].
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5. TYPES OF AS/RS
AS/RS can arrange warehouse management for material
handling system where without automation, filling higher
quantities of smaller orders means more pickers traveling
through a distribution center. Eventually, at about 3,000
orders per hour, operations hit a tipping point in which foot
traffic clogs thoroughfares and operations hit a productivity
plateau. Rather than just refining manual processes, highly
automated systems and fulfillment workflow modifications
work together to deliver necessary increases in speed and
accuracy. AS/RS enables different, more effective
methodologies to solve the challenge of high-throughput e-
commerce fulfillment [20]. Figure(4) shows general form of
AS/RS system with main unit that required for any AS/RS
system .So for this , can be divide the AS/RS system
according to the method of handling a load to four type :
Fig. 4 General form for AS/RS System
1. Unit load AS/RS: is used to store and retrieve loads
that are palletized or stored in standard-sized
containers .Entire unit load is carried by forklift
trucks, conveyors, etc.. Unit load stored and
retrieved from AS/RS system in a certain interval
of time[21].
2. Mini load System:. This system is designed to
handle small loads such as individual parts, tools,
and supplies that are contained in bins or drawers in
the storage system. Total storage volume is less than
unit load system .Such a system is applicable where
the availability of space is limited
3. Man-on-board: This system allows storage of items
in less than unit-load quantities. Human operator
rides on the carriage of the SR machine to pick up
individual items from a bin or drawer where instead
of retrieving entire load automatically man will be
ride over S/R machine and pick only one item from
unit load.
4. Deep lane AS/RS : This is a high-density unit load
storage system that is appropriate for storing large
quantities of stock. The items are stored in multi
deep storage with up to 10 items in a single rack,
one load behind the next. Each rack is designed for
flow-through, with input and output on the opposite
side. Picker located at the end of aisle can pick
required few items from unit load and S/R crane
restore the unit load to the rack.
6. AS/RS DESIGN DECISIONS
Design decision consist of physical design and control policy ,
so Tab. (1) shows an over view for design decision for general
AS/RS system. AS/RS design decision consisted of two stage
the first stage called physical design while the second stage is
called control design. The physical design stage consists of
two rules that specifies physical base for the system. The
choice of the AS/RS type and the selected of the system that
must be configured. Then these parameters include: Number
of aisles, the rack structure, as shown in Fig.(5) Number of
aisles, Product characteristics, required throughput, required
storage space available land space and etc.
Table (1) An over view of Design Decision for AS/RS
Class of
problems
Decision to be made
System
configuration
Number of aisles
Height of the storage racks
Length of the aisles
Equally sized or modular storage
locations
Number and location of the I/O points
Number of crane per aisle
Number of order pickers per aisle (if
any)
Storage
assignment
Storage assignment method
Number of storage classes
Positioning of storage classes
Batching
Type of batching (static or dynamic)
Batch size (capacity or time based)
Selection rule for assignments of orders
to batches
Sequencing
Sequencing restriction (e.g., due dates)
Type of operation (single or dual
command )
Scheduling approach (block or
dynamic)
Sequencing method
Dwell- point
Type of positioning (static or dynamic)
Location where idle cranes will be
placed
a
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Fig. 5 Design of an AS/RS and Related Decisions.
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Volume 177 No. 16, November 2019
41
6.1 Physical design
Typically when we designing an AS/RS system where the
first step is Physical system design which can be defined as a
configuration and dimensions of the system. System can be
configured based upon previous available data, space
availability and requirement, available budget and required
throughput from system. System can be configured as:-
Load size to be handled.
Size parameters such as number of rows, number of
bays in each row, individual storage space
dimensions, number of storage spaces required.
Number of aisles, Rack height and Length of Rack,
Number of S/R machine required.
Selection of storage spaces such as Equal sized or
modular to face variable dimensions.
Location of input-output station [22].
Rack structured :-The rack structured consider the important
step in the physical design for AS/RS system where there are
two type of rack system either dynamic or static , this
classification depends on the behavior of the rack system. In a
dynamic rack system, the rack moves itself to bring the
required a Stock Keeping Unit (SKU) to the pickup point
while a static rack system is a rack system, where there is no
ability for the rack to change the storage locations of SKUs
within it through its own movements, as in the shelf rack
system. An integrated rack system another form of rack
where there are some automated mechanisms are integrated
with the rack system. There are two types of integrated
system. Movable racks, such as those of the Kiva system, use
a driverless vehicle or robot that used to bring the rack to the
pick-station and the other type is static rack includes a robot
or automated mechanism built within the rack system
(integrated in), as in auto store[23].
6.2 Control design
6.2.1 Dwell-Point Policy
A dwell point is an idling location of a Storage/Retrieval
(S/R) machine when it becomes idle.[24] We determine a
dwell point position which minimizes the expected travel
time to the position of the first operation after the idle period.
An effective dwell point policy may reduce the response times
of the AS/RS, since the S/R machine typically performs a
sequence of operations following an idle period. Hence, if the
first operation is advanced, then all operations within the
sequence are completed earlier [25].
Typical dwell point rules include:-
1. Dynamically position the S/R machine at a location
that minimizes the expected S/R machine travel or
response time from dwell point to the points of
need.
2. Dynamically position the S/R machine at a location
that minimizes the maximum S/R machine travel
or response time from dwell point to the points of
need.
3. Always position the S/R machine at the input station
whenever idle.
4. Always position the S/R machine at the output
station whenever idle
5. Always position the S/R machine at the mid-point
location in the rack whenever idle.
6. Dynamically position the S/R machine at the last
location it visited following the completion of either
a single command or a dual command cycle [21].
6.2.2 Input / Output Position policy
Input-output locations. There is one input and one output zone
(I/O). The input zone is used to introduce full boxes from
outside the system and has limited capacity. The output zone
is used to remove empty, faulty or out- dated boxes. When a
box arrives at the output zone, it automatically leaves the
system. The functionality of the output differs from the
standard AS/RS problem, where it is used to satisfy the
customers' requests. This function is made by the picking
zones [26].
The position of the I/O station(s) is also a factor that affects
the AS/RS operation. Bozer and White (1984) analyzed and
derived the expected travel time of the following alternative
configurations for the I/O station:
1. Input and Output at opposite ends of the aisle.
2. Input and Output at the same end of the aisle, but at
different elevations.
3. Input and Output at the same elevation, but at a
midpoint in the aisle.
4. Input and Output elevated at the end of the aisle.
6.2.3 Routing and Sequencing Type of operation
policy
Cycle time term refers to the time an order takes from its
entrance to the system until it reaches the shipping area. The
cycle time has a close relationship with the customer service
level and thus with customer satisfaction. This is due to the
fact that customers usually expect timely delivery. The
operation time is not the only factor that effects the cycle
time, however, there are many other factors such as
palletizing time, transportation time within the whole process
and also the packaging time. Storage and picking strategies
have a substantial effect on the cycle time. [27]
The Type of operation time defines which command cycle is
applied to operate an AS/RS. In a single command cycle (SC),
either a single storage or a single retrieval operation is
performed. A storage cycle consists of picking up the load at
the I/O point, traveling of the S/R machine to the storage
position, placing the load into the rack and returning back to
the I/O point. A retrieval cycle is performed similarly.
Consequently, during a storage cycle the return travel is an
empty run of the S/R machine, while for a retrieval cycle, the
travel to the storage position is an empty run.
6.2.4 Storage location assignment policy
Storage location assignment is a set of rules that determines
where incoming storage totes will be located in the storage
rack [28] or A storage assignment method is a set of rules
which are used to assign items to storage locations [29] or the
selection of open locations for incoming unit loads. There are
three storage assignment policies random storage, full
turnover storage and class-based storage.
1. Random a storage assignment policy
Random storage is a completely shared storage policy where
all incoming storage totes can be stored in any aisle and any
open location in the rack. Also it allows products to be stored
anywhere in the storage area. Within a storage class, items are
randomly stored .The name, the open locations in random
storage are chosen because of an open location selection rule
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42
instead of a random choice. One basic rule important here is
closest open location (COL). This means that an incoming
tote is stored to the closest unoccupied location from the I/O
point, with respect to travel time. [30].
Closest-open-location storage assignment
In practice, incoming items (e.g. on a pallet) are often
allocated to the closest empty location. ‘Closeness’ here is
defined by the travel distance from the input/output (I/O)
Point (or depot) to the storage location
2. Dedicated storage assignment policy
Materials are assigned to predetermined locations based on
throughput and storage requirements or each item has its own
storage location. To minimize the travel distance, the closest
to-depot storage locations are commonly reserved for items
with a high turnover and little storage space occupation. An
early type of this storage assignment method is the COI-based
storage assignment, where the COI of an item is defined as the
ratio of the required storage space to the order frequency of
the item The COI-based method sort’s items by increasing
COI ratio and locations on increasing distance from the I/O
point. Next, items are assigned one by one to locations in this
sequence (items with the next lowest COI ratio to next
quickest-to-access locations).
3. Class based storage policy
Figure( 6 ) shows the class based assignment that 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 [31]. 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.
Fig. 6 Atypical Zone Positioning for three Classes in
Square in Time Rack (upper part) and Rectangular Rack
(lower part).
7. ORDER BATCHING
When there are a required items that are collected on tours
through the warehouse, where the number of stops on each
tour is limited by the available capacity of the picking vehicle
/picking device on the one hand and by the capacity
requirement of the items to be picked on the other hand.
Customer order can be combined (batched) into to picking
orders (batches) until the capacity of the device is exhausted.
Capacity can be expressed in the number of customer order.
Splitting of customer order, i.e. , the inclusion of items from
the same customer order in several picking order[32].
Usually, an order is selected, to be included in a batch, based
on a measure of the ‘distance’ from the order to the seed order
of the batch.
8. CONCLUSION
This paper summarizes the various components in an
automated storage and retrieval system, lists also the benefits
of automating a company’s storage operation. Details of the
various control strategies are included and a summary of the
performance measures applied to such systems. The findings
from this overview are that there is currently a large amount
of research on-going with particular emphasis on improving
throughput by analyzing storage, retrieval and dwell point
strategies. The best recorded performance was content a
current dwell point, simultaneous travel, dual control, free
nearest storage and nearest retrieval strategies selected in
combination. In general, dual control improved performance
(in terms of throughput), simultaneous travel was found to be
better than rectilinear travel, dwell point at origin gave very
poor results, and a dwell point at current, pick point or deposit
point appears best considers the popularity of items, not their
space occupation. COI-based storage is that the volume-based
assignment only considers the popularity of items, not their
space occupation. The pick volume of an item can be
expressed in number of units or pick lines during a certain
time horizon. The difference between this method and COI-
based storage is that the volume-based assignment only
considers the popularity of items, not their space occupation.
9. REFERENCES
[1] Matej Borovinšek, Banu Y. Ekren, Aurelija Burinskienė,
Tone Lerher,” MULTI-OBJECTIVE OPTIMISATION
MODEL OF SHUTTLE-BASED STORAGE AND
RETRIEVAL SYSTEM ” Vilnius Gediminas Technical
University (VGTU) Press , Transport, Vol.32(2):
pp.120137, 2017.
[2] Renata Kudryavtseva ,” Simulation-based analysis of
performance of automated storage and retrieval system
Storage/retrieval (S/R) crane performance “, Bachelor’s
thesis Technology, communication and transport
Degree Program in Logistics Engineering, May 2018.
[3] Salah Bashir,” THE IMPLEMENTATION AND
ANALYSIS OF A TENDON-BASED STEWART-
GOUGH-PLATFORM (SGP) FOR AN AUTOMATED
STORAGE AND RETRIEVAL SYSTEM FOR MINI-
LOAD”, PhD thesis, 5, 2013.
[4] Smita U. Chakole,” development of Robotic Automated
Storage and Retrieval System (AS/RS)”, International
journal of computational Engineering Research, vol.3
issue 3, March 2013.
[5] HEUNGSOON FELIX LEE,” Performance analysis for
automated storage and retrieval systems”, IIE
Transactions, 1997.
[6] M.R. Vasili, S.H. Tang, N. Ismail, and S. Sulaiman,”
CLASS-BASED STORAGE ASSIGNMENTS FOR
MINILOAD AS/RS WITH OPEN-RACK
STRUCTURE”, International Journal of Engineering and
Technology, Malaysia, , Vol. 5, No. 2, pp. 118-128
,2008.
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Volume 177 No. 16, November 2019
43
[7] E. Vijayaragavan , Sanketh Bhat , Abhishek Patel and
Divyang Rana,” ] Design and analysis of a mobile robot
for storage and retrieval system”, 2nd International
conference on Advances in Mechanical Engineering
(ICAME 2018) IOP Publishing, IOP Conf. Series:
Materials Science and Engineering 402,2018.
[8] Imén Kouloughli, Pierre Castagna, Zaki Sari,” Reducing
retrieval time in Automated Storage and Retrieval
System with a gravitational conveyor based on Multi-
Agent Systems”, J. Appl. Comput, jacm.scu.ac.ir,vol.
4(1) ,pp.55-68, 2018.
[9] A. T. Rashid, F. R. Ali, and O. T. Rashid, “Design and
Construction a Dynamic Store System using the Bezier
Curve Algorithms”, International Journal of Computer
Applications, vol. 179, No. 42, pp. 22-29, 2018.
[10] A. T. Rashid, F. R. Ali, and O. T. Rashid, “Software
implementation of a static store system using the digital
differential analyzer algorithm”, International Iraqi
Conference on Engineering Technology and their
Applications, The Islamic University - Najaf Iraq,
2018.
[11] F. R. Ali, and A. T. Rashid, “Design and implementation
of static and dynamic objects store systems using line
follower robots”, International Conference on Advances
in Sustainable Engineering and Applications, Wasit
university - Iraq, 2018.
[12] F. R. Ali, and A. T. Rashid, “Software implementation of
objects store system using line follower robots”, Second
Al-Sadiq International Conference on Multidisciplinary
in IT and Communication Science and Applications,
2017.
[13] F. R. Ali, and A. T. Rashid, “Design and Construction
Objects Store System using line follower robots”,
International Journal of Computer Applications, vol. 181,
No. 15, pp. 27-35, 2018.
[14] JAINIL PRAJAPATI, HARSHAL GOYAL, JEEL
PATEL, APURVA GANDHI, SAMARTH
BHADUWALA,” AUTOMATED STORAGE AND
RETRIEVAL SYSTEM FOR EDUCATIONAL
PURPOSE- A REVIEW”,
[15] M. R. Vasili, Sai Hong Tang and Mehdi Vasili ,”Chapter
8 -Automated Storage and Retrieval Systems: A Review
on Travel Time Models and Control Policies”,
[16] OUHOUD. A, GUEZZEN. A, SARI. Zaki.,”
Comparative Study between Continuous Models and
discrete models for Single Cycle Time of a Multi-Aisles
Automated Storage and Retrieval System with Class
Based Storage”, IFAC-PapersOnLine at
www.sciencedirect.com , pp.13411346,2016.
[17] Latéfa GHOMRI & Zaki SARI,” Mathematical modeling
of retrieval travel time for flow-rack automated storage
and retrieval systems”, IFAC (International Federation of
Automatic Control) Hosting by Elsevier , j.ifacol,2015 .
[18] Tony Wautersa, Fulgencia Villab, Jan Christiaensa,
Ramon Alvarez Valdesc, Greet Vanden Berghea,” A
decomposition approach to dual shuttle automated
storage and retrieval systems”,
[19] Roodbergen, K. J., Vis, I. F.,” A survey of literature on
automated storage and retrieval systems”, . European
Journal of Operational Research, 2009.
[20] Miss. Anuradha S. Parab, Prof. P. N. Gore, “A Review
on Automated Storage and Retrieval System”,
International Research Journal of Engineering and
Technology (IRJET), Vol. 05, Issue 12, 2018.
[21] Tom Meyers,” What to consider for a successful AS/RS
investment white paper“, Intelligent. All rights reserved.
2016.
[22] Liam O'shea,” development of an automated storage and
retrieval system in a dynamic knowledge environment “,
MSC in Waterford institute of technology, 2007.
[23] Mohammed Ruzayqat,” DESIGNING A CELLULAR-
BASED FULLY AUTOMATED CASE PICKING
SYSTEM”, PhD, April 2016.
[24] [BYUNG CHUN PARK,” TECHNICAL NOTE Optimal
dwell point policies for automated storage/retrieval
systems with dedicated storage “,IIE Transactions ,1999.
[25] Jeroen P. van den Berg,” Analytic expressions for the
optimal dwell point in an automated storage/retrieval
system”, Int. J. Production Economics, Vol.76, pp.13–25,
2002.
[26] Ankit Agarwa and A.K. Madan,” A Review of
Analytical Travel Time Models in Automated Storage
and Retrieval Systems”, Journal of Basic and Applied
Engineering Research, Volume 2, Number 16; April-
June, pp. 1372-1376, 2015.
[27] L Min-Hong Han, Leon F. McGinnis, Jin Shen Shieh,
John A. White,” ON SEQUENCING RETRIEVALS IN
AN AUTOMATED STORAGE/RETRIEVAL
SYSTEM”, School of Industrial and Systems
Engineering , Georgia Institute of Technology , FEB, 2
1 , 1985.
[28] S. Kulturel, N. Ozdemirel, C. Sepil, and Z. Bozkurt,”
Experimental investigation of shared storage assignment
policies in automated storage/retrieval systems”, ,” IIE
Trans. (Institute Ind. Eng., vol. 31, no. pp. 739749 ,
March 2015..
[29] Agarwal and A.K. Madan,” A Review of Analytical
Travel Time Models in Automated Storage and Retrieval
Systems”, Journal of Basic and Applied Engineering
Research, Volume 2, Number 16; April-June, pp. 1372-
1376, 2015.
[30] C. J. Malmborg,” Storage assignment policy tradeoffs”,
International Journal Of Production Research,vol. 34,
no. 2, pp. 363378, 1996.
[31] W. H. Hausman, L. B. Schwarz, and S. C. Graves,
“Optimal Storage Assignment in Automatic
Warehousing Systems”, Manage. Sci., vol. 22, no. 6, pp.
629638, 1976.
[32] Sepastian Henn, soren Koch and Gerhard wascher,”
order batching in order picking warehouse a survey of
solution approaches”, http://www.fww.ovgu.de/femm
,Bezug uber den Herausgeber ,Germany , No. 01,2011 .
IJCATM : www.ijcaonline.org
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... Thus, it is a key control policy to improve efficiency and also responsiveness which can minimize cost and maximize customer satisfaction at a time. Since AS/RS is a highly complex system and dynamic in nature, optimizing DPP in AS/RS can be a big challenge (Hameed et al, 2019). Previous studies have proposed analytical approaches to optimize AS/RS DPP (Hwang & Lim, 1993). ...
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Automated storage/retrieval systems (AS/RS) are computer-controlled automated material handling systems, widely used in industry and warehousing systems. They have interesting advantages, the majors of which are a high throughput, efficient use of space and improvement of safety. All the disadvantages of AS/RS are related to economic factors such as high initial investments and difficulty to change layout. This implies the importance of careful design of the AS/RS by taking into account all constraints. The latter may be linked either to the AS/RS dimensions or to products. The evaluation of average travel time that the S/R machine takes to store and retrieve a product is necessary for an optimal design. The present paper is devoted to a particular configuration of AS/RS said flow-rack AS/RS. The flow-rack AS/RS consists of only one deep rack and two machines; the first is used for storing operations and the second for retrieval operations. Our aim is to model mathematically the average travel time of the retrieval machine under random storage. The mathematical model proposed hereunder is certainly function of the physical parameters of the flow-rack AS/RS such as length, width, depth, etc.; but it is also function of the variety of products stored in the system and of their proportions. The mathematical model could be useful for performance evaluation and for design considerations of the flow-rack AS/RS in real world applications. The validation of the model is done by simulation.
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This paper addresses throughput improvement by retrieval sequencing in conventional unit load automated storage/retrieval systems when several retrieval requests are available and dual command cycles are performed. Taking first-come-first-served as the reference sequencing rule, the potential for improvement is identified. A “nearest-neighbor” sequencing rule is proposed as an alternative, an analytic model for its expected performance is developed, and Monte Carlo simulation is used for evaluation. In addition, a lower bound on dual command cycle times is developed, and the dynamic behavior of two heuristic sequencing rules is discussed.