55. Material Handling Automation in Production Material Hand …extras.springer.com/2009/978-3-540-78830-0/11605119/... · 2006, new orders of material handling equipment ma-chines

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Material Hand55. Material Handling Automation in Productionand Warehouse Systems

Jaewoo Chung, Jose M.A. Tanchoco

This chapter presents material handling automa-tion for production and warehouse managementsystems that process: receipt of parts from ven-dors, handling of parts in production lines, andstoring and shipping in warehouses or distri-bution centers. With recent advancements ininformation interface technology, innovative sys-tem design technology, and intelligent systemcontrol technology, more sophisticated systemsare being adopted to enhance the productivityof material handling systems. Information inter-face technology utilizing wireless devices such asradiofrequency identification (RFID) tags and mo-bile personal computers significantly simplifiesinformation tracking, and provides more accuratedata, which enables the development of more re-liable systems for material handling automation.Highly flexible and efficient automated mater-ial handling systems have been newly designedfor various applications in many industries. Re-cently these systems have been connected intolarge-scale integrated automated material

55.1 Material Handling Integration ............... 96255.1.1 Basic Concept and Configuration .. 962

55.2 System Architecture .............................. 96455.2.1 Material Management System ...... 965

55.3 Advanced Technologies ......................... 96955.3.1 Information Interface Technology

(IIT) with Wireless Technology ...... 96955.3.2 Design Methodologies for MHA ..... 97155.3.3 Control Methodologies for MHA .... 97255.3.4 AI and OR Techniques for MHA ..... 975

55.4 Conclusions and Emerging Trends .......... 977

References .................................................. 977

handling systems (IAMHS) that create synergy withmaterial handling automation by proving speedyand robust infrastructures. As a benefit of high-level material handling automation, the modernsupply chain management (SCM) successfully syn-chronizes sales, procurement, and production inenterprises.

In today’s competitive environment, suppliers must beequipped with more cost-effective and faster supplychain systems to remain in the market. Companies areinvesting in material handling automation (MHA) notonly to reduce labor cost, delivery time, and productdamage, but also to increase throughput, transparency,and integratability in production and warehouse man-agement systems. The material handling industry hasgrown consistently over many years. The Material Han-dling Industry of America (MHIA) estimates that, in2006, new orders of material handling equipment ma-chines (MHEM) grew 10% compared with 2005 and seta new record high at US$ 26.3 billion in the USA [55.1].

In the past, labor cost was the most important el-ement for estimating the return on investment (ROI)

of a stand-alone automated material handling system(AMHS), and the system was a relatively small partof the production or warehouse facility. Nowadays,the impact of the system throughout the supply chainis becoming larger and more complicated; for ex-ample, a radiofrequency identification (RFID) systemenhances customer satisfaction by providing conve-nience in data tracking as well as reducing order pickingtimes and shipping errors in warehouse. AMHSs arenot alternatives selected after prudent economic ana-lysis, but are rather major components in a productionand warehouse facility. Also, the sizes of systemsand the complexities of their operations are increas-ing. Multiple AMHSs consisting of RFID systems,automated guided vehicles (AGVs) systems, and au-

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962 Part F Industrial Automation

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a) Warehouse system for pharmaceutical industry

b) Material flows in warehouse system above

Fig. 55.1a,b IAMHS for pharmaceu-tical industry (courtesy of MurataMachinery). (a) Warehouse systemfor pharmaceutical industry, (b) ma-terial flows in warehouse systemabove

tomated storage and retrieval systems (AS/RSs) aretypically installed in a production and warehouse fa-cility as a connected system. As its complexity hasincreased, optimization of the design and operation ofthese systems has become of interest to both AMHSvendor companies and their customers. Many exam-ples of these integrated systems can be observed in thesemiconductor [55.2], automotive [55.3], and freight in-dustries [55.4, 5].

This chapter introduces practical applications ofMHA for production and warehouse systems. It startsby introducing a concept of the IAMHS that uses sev-eral types of the AMHS in a single integrated system(Figs. 55.1 and 55.2). The focus is particularly on whatan IAMHS consists of and how it collaborates withother systems in SCM. Based on this introduction, com-ponents of the IAMHS and their recent technologyadvancement in the MHA will be reviewed.

55.1 Material Handling Integration

55.1.1 Basic Concept and Configuration

An IAMHS integrates different types of automatedmaterial handling equipment in a single control en-

vironment. A simple type of IAMHS was used forseaport or airport cargo terminals, which are servedby stacker cranes and AGV systems [55.5]. The mainissue of the simple IAMHS is how to reduce wait-

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Material Handling Automation in Production and Warehouse Systems 55.1 Material Handling Integration 963

Fig. 55.2 New IAMHS design for next-generation semi-conductor fab (courtesy of Middlesex)

ing time during job transition between two differentAMHSs to increase throughput; for instance, an AGVhas to wait after arriving at the load position if thecrane is not ready to unload a container to the AGV.If their jobs are poorly synchronized, the waiting timewill be longer, and as a consequence throughput willdrop.

Recently IAMHSs with more complicated com-ponent systems have been implemented for manycompanies in different industries. Figure 55.1 showsan example IAMHS used in a warehouse system inthe pharmaceutical industry [55.6]. In this configura-tion, there are five different types of the AMHS. First,a pallet AS/RS is installed and the temperatures ofeach shelf in the AS/RS can be controlled accordingto the characteristics of the products stored to main-tain product quality. Second, a free-size AS/RS is usedfor storage of individual orders and items that are fre-quently replenished. It can store items regardless oftheir size, shape, or weight since it uses a hoisting car-riage that can handle a wide range of products. Third,an automated overhead traveling vehicle is installed toreplenish items with minimum labor cost and waitingtime. It uses overhead space to increase space effi-ciency. Another AMHS used is a digital picking system,providing convenience for picking tasks by displayingdirections on a digital panel installed on the shelves.

Finally, the IAMHS is operated by handheld termi-nals providing many applications in the warehouse.It is equipped with an RFID or barcode reader thatallows flexible adaptation to changes in distributionquantity.

The semiconductor industry is equipped with oneof the most complex IAMHSs for wafer fabrication(fab) lines (Fig. 55.3). A fab line may consist of morethan 300 steps and 500 process tools. Material trans-portation between tools in a wafer fab line is fullyautomated by the overhead hoist transporter (OHT)system, which is a type of rail-guided vehicle (RGV)system, an AS/RS called the stocker, a lifting systemthat transfers wafer carriers between different floors,and a mini-environment that is used for a standardinterface of machines with the AMHS. For the next gen-eration of IAMHS in a fab line, Middlesex has proposeda new concept using conveyor systems (Fig. 55.2) in-stead of OHT systems and stockers, which guaranteeslarger-capacity transfers and quick response times fordeliveries. Middlesex has focused on high-end conveyorsystems for many years. More reviews of the IAMHS inthe semiconductor industry are provided by Montoya-Torres [55.2].

These IAMHSs are generally highly flexible indesign for customized usage, and some of them areeven unique and revolutionary. Kempfer [55.7] in-troduced an order picking system utilizing a voicerecognition system and RFID system in a large-scale automated distribution center. The article reportsthat the average order picking performance was im-proved from 150 cases per man-hour to 220 casesper man-hour by reducing operators’ information han-dling time. A few companies also achieved similar

Master planning

Transfer command A

Transfer command B

ERP systemEDI server

MES

MMSIAMHS

Dataserver

AS/RScontroller

AGVcontroller

Other systemsin SCM

Conveyorcontroller

RFIDsystem

SDSWMS

Fig. 55.3 IAMHS in hierarchical system architecture

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improvements by adopting an integrated RFID andvoice recognition system [55.8, 9]. Chang et al. [55.9]proposed an integrated multilevel conveying devicefor an automated order picking system that transfersarticles between two different levels of a multi-

storey building to improve the operational and spa-tial efficiency of the warehouse system. The systememploys a specially designed device comprised ofa stacker crane, a vehicle-based transporter, and con-veyor system.

55.2 System Architecture

In the design of a large-scale IAMHS, a well-structuredsystem reduces the redundancies of functions in differ-ent modules, unnecessary transactions between mod-ules, and system errors caused by large and complexindividual functions of these modules. An algorithmignoring the system architecture sometimes tends tocreate many problems during implementation, mainlybecause of the lack of necessary information and dif-ficulty in interacting with existing systems [55.10].Examples of this limitation can be found in the liter-ature. An AGV scheduling algorithm under an FMSenvironment determines a sequence of the AGV routewithin a certain time horizon, considering the informa-tion from both the work centers and AGV systems ona shop floor. However, under this system architecture, itis very difficult for an AGV controller to take into con-sideration complex constraints of work centers such asmachine status, processing times, and setup times be-cause of the long calculation time. Therefore, generally,job sequencing and scheduling are performed indepen-dently by the scheduling and dispatching system, whichis then connected to the AGV controller using a se-quence of protocols. An AGV controller only takescare of requested transfer commands, which specifysource and destination locations, priorities, and com-mand trigger times. There is already too much loadon the AGV controller in its original tasks, which in-clude path planning for a vehicle, job dispatch fora newly idle vehicle, vehicle dispatch for a new jobrequested, error recovery, etc. [55.11]. Therefore, thedeveloped AGV scheduling algorithm should be mod-ified based on the structure of the system architecture.One way to carry out this modification is to break upthe algorithm for different modules in the system struc-ture. During this break-up process, it is unavoidableto change the algorithm depending on the availabil-ity of information to the module, which sometimescauses significant performance degradation comparedwith the original algorithm. As the number of subsys-tems being used in production and warehouse systemscontinues to increase, a well-structured system will be

beneficial for facilitating collaborations between dif-ferent departments as well as these systems. However,it is an open challenge to construct a well-designedsystem structure that accommodates all the differenttypes of AMHS regardless of the size of the sys-tem and the type of business on which the IAMHS iscentered.

Various types of system architecture can be used todesign an IAMHS with other application systems, de-pending on the manufacturing type of the shop floor,the size of the total system, the number of transactionsper second, etc. Figure 55.3 illustrates a design exam-ple of the system architecture for the IAMHS presentedin Fig. 55.1. The focus of this figure is on software mod-ularity. Each AMHS has its own controller (the fourcontrollers at the bottom of Fig. 55.3), which is respon-sible for its own tasks and communication with thematerial management system (MMS), which is a high-level integrating system that will be explained later inmore detail; for example, an AGV controller addressesjob allocation, path planning, and collision avoidance,receives a transfer command from the MMS (trans-fer command B in Fig. 55.3), and reports necessaryactivities such as vehicle allocation and job comple-tion to the MMS so that data are kept for tracking inthe future. Each controller also has to process error-recovery routines for robustness of the system control.The MMS manages multiple controllers of differentAMHSs, and has a database server to store all trans-actions of the subsystems in the IAMHS. It receivestransfer commands or short-term scheduling results ofprocessing machines from the scheduling module ina higher-level system in the SCM (transfer command Ain Fig. 55.3). In this structure, long-term optimization ofprocessing machines is responsible for the higher-levelsystem, and the MMS focuses on efficiencies duringthe transportation of unit loads within production andwarehouse facilities. Details of the MMS are explainedin the next section. As shown in Fig. 55.3, the higher-level systems of the MMS can be a manufacturingexecution system (MES), warehouse management sys-

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Material Handling Automation in Production and Warehouse Systems 55.2 System Architecture 965

tem (WMS), and enterprise resource planning (ERP)system. The WMS can be substituted by the MMSif the warehouse is composed of relatively simplesystems.

Understanding high-level decision-support systemsin SCM helps to understand the scope of controltasks performed by the IAMHS. The advanced plan-ning and scheduling (APS) system generally consistsof planning and scheduling modules. Sometimes thescheduling module is again broken down into schedul-ing and dispatching modules (SDS in Fig. 55.3). TheERP system generally includes the planning module;however, the scheduling and dispatching modules canbe included in any other systems such as the MES andWMS. In Fig. 55.3, it is assumed that the modules arerunning in a stand-alone system called the SDS thatcommunicates with the ERP system, MES, and MMS.The planning module makes a long-term production orprocurement plan based on customer orders, demandforecasting results, and capacity constraints. Its timehorizon varies from weeks to months. Practically, ithardly optimizes complex factors of resources on theshop floor because of the long computation time, butconstructs highly aggregated planning. Its results in-clude production quantities for each product type andtime bucket, or production due dates for each producttype or product group. Detailed resource requirementplans are not specified by the planning module due tothe uncertainties and complexities of operations. Thescheduling module is responsible for delineating moreconcrete plans for the shop floor to meet the target pro-duction plan from the planning module. It typically triesto optimize various resource constraints with severalobjectives such as due-date satisfaction and through-put maximization. Detailed resource requirement plansover time buckets within a time horizon are created bythe scheduling module. It sometimes takes into accountconstraints in the AMHS for more robust scheduling.The time horizon of the scheduling module varies froma few hours to days. The dispatching module determinesthe best unit load for a machine in real time followinga trigger event from the machine or unit load. It triesto follow up closely the scheduling results, which areglobally optimized. The MMS in the IAMHS receivestransfer commands from either the scheduling moduleor dispatching module based on its system architec-ture. These transfer commands are the result of thescheduling, machine assignment or job sequencing onprocessing machines. The MMS manages the processof the given transfer command by creating more de-tailed transfer commands to the AMSHs in the IAMHS.

The dispatching module is sometimes included in theMMS, and creates the transfer commands based on thescheduling results and its own dispatching rules for thereal-time status of the shop floor. The IAMHS takescharge of the final execution of the SCM in an enter-prise and also provides useful information as describedabove.

The higher-level systems of the IAMHS auto-mate information processing throughout an enterprise.The MES in Fig. 55.3 is a tracking system that col-lects important data from processing machines andstores them in well-structured database tables for ana-lysis of quality and process controls; however, it hasexpanded its role into many other areas based ona powerful open architecture. It has been popularlyused in the electronic industry such as in semicon-ductor fabs and surface-mounting technology (SMT)lines, and has recently spread into other industries.The warehouse management system (WMS) is gen-erally used in mid- or large-size warehouse facilities,similar to MES for a production shop floor; it tracksevery movement of materials and support operationsfor material handling in the warehouse. Its focus is oninformation processing automation. The objective ofimplementing an ERP system in a company is infor-mation sharing for rapid and correct decision-making,and implementation throughout the enterprise by us-ing an integrated database system [55.12]. Chapter 90provides a more thorough discussion of ERP and re-lated concepts. The whole procedures of order entry,production planning, material procurement, order de-livery, and corresponding cash flow are managed bythe system. All the data from different applications inan enterprise or between different enterprises are ex-changed by an electronic data interchange (EDI) server,which allows automated exchange of data between ap-plications. Based on the EDI technology, applicationsfreely exchange purchase orders, invoices, advance shipnotices, and other business documents directly fromone business system to the other without human sup-port. Figure 55.4 illustrates the connectivity of IAMHSto other systems in SCM, which is used in an actualindustry.

55.2.1 Material Management System

The role of the MMS is very important in a complexIAMHS for high-level automation. The main functionsof the MMS are summarized below, in increasing or-der of importance. This summary does not discuss thedispatching module that assigns unit loads to process-

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Production line

Genetic algorithm (GA)palletizing system

Wirelessmobile phones

Wirelessin-vehicle terminals

Transport planningsupport system

Vehicle dispatchsystemLogistics

data server

Wireless terminalsystem

Terminals Printer

ERPHOST

EDI

Fig. 55.4 Connectivity of IAMHS to other systems in SCM (courtesy of Murata Machinery)

ing machines because this involves so many topics;however, the dispatching functions bounded to AMHSs(i. e., dispatching unit loads while not considering pro-cess machines) will be discussed here.

The roles of the MMS in a complex IAMHS are:

1. Determining the best destination among several pos-sible AMHS alternatives

2. Determining the best route to get to the destinationfrom a source location via several AMHSs

3. Determining a proper priority for the transfercommand

4. Storing and reporting various data using a databaseserver

5. Transfer command management between differentAMHSs

6. Error detection and recovery for the transfercommand

7. Providing a user interface for control, monitoring,and reporting.

Conveyor #1

Machine #9

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OP0201

OP0201AS/RS #1OP3

Fig. 55.5 Example of IAMHS

For a unit load to be transferred, its source anddestination locations are mainly determined by the dis-patching or scheduling module; however, when thecandidate destinations are AMHSs, it is sometimesmore efficient for the MMS to determine the final des-tination than for the scheduling or dispatching moduleto do so. Consequently, the dispatching and schedulingsystem provides a destination group to the MMS. Fig-ure 55.5 illustrates the execution process of a transfercommand. In the figure, if a unit load from Machine #2has just finished its processing and has to be trans-ferred to an AS/RS to be processed next, on one ofthe machines connected to AGVS #3 (dashed arrow inthe figure), there are two candidate AS/RSs connectedto AGVS #3: AS/RS #3 and AS/RS #4. The AS/RSgroup connected to AGVS #3 is named AS Group #3.The MMS will receive a transfer command from thedispatching module, specifying the source location asMachine #2 and the final destination as AS Group #3.Since there are two alternative destinations, the MMSmay consider the product type of the unit load, the fullrate of each AS/RS, the load port status of each AS/RS,the shortest distances from the unit load to the AS/RSsconsidering current active jobs in each system, and soon. It will determine the best AS/RS amongst the two al-ternatives and trigger a transfer command to AGVS #1,which will first move the load to AS/RS #1 from Ma-chine #2.

The MMS is also responsible for determiningthe destination subsystem in an AMHS, such as theload/unload port (or pickup/drop-off port) in an AS/RS,because there are generally multiple load/unload portswith different types and numbers of buffers. The ports

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Material Handling Automation in Production and Warehouse Systems 55.2 System Architecture 967

may differ, being load only, unload only or of unifiedtype. Assume that a unit load in AS/RS #2 in Fig. 55.5has to be moved to Machine #5, connected to AGVS #2(solid arrow in Fig. 55.5). First, the unit load has to bemoved to one of the output ports in the AS/RS. TheAS/RS controller may not know which output port willbe the best among the three possible ones in the figurebecause it does not know the next destination of the unitload. The MMS may determine a load port connected toAGVS #2, OP0201 in Fig. 55.5. In practical application,the problems are generally much more complicated thanthis illustration due to the increased instances in the sys-tem. Few studies have addressed this type of problem.Sun et al. [55.13] and Jimenez et al. [55.14] stress theimportance of this problem in the literature and intro-duce a few ideas being used in practical applications;however, their methods leave much room for improve-ment in that they use static approaches and considerlimited factors.

Obtaining the best route to get to the destinationis another important task of the MMS. In a complexIAMHS, there are many possible routes consisting ofdifferent AMHS types. The IAMHS in Fig. 55.5 is rep-resented by the graph in Fig. 55.6. A graph can beencoded in database tables by using an adjacency ma-trix or incidence matrix for use by a computer program.The adjacency matrix is a simple from–to chart be-tween a pair of vertices, in which the value of an edgeis the distance between the vertex pair, being zero ifthe pair are not connected. The incidence matrix rep-

AS02 AS

03

M02

M02

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CS01

AS01

AS04

M02

M03

Naming convention of vertices in the graph:Two-digit number in a vertex followed by alphabets represents the index number of machines or AMHS. For example, M01 is the machine #1 connected to AGVS #1 and CS 01 is the conveyor #1. Acronyms areas follows. M: Machine, AS: AS/RS, CS: Conveyor System

Fig. 55.6 Graphical representation of the IAMHS in Fig. 55.5

resents the connectivity of vertices by edges. Usinga graphical representation of the IAMHS, many pre-defined properties and algorithms of graph theory canbe applied to develop algorithms for the MMS; forexample, Dijkstra’s algorithm can be used to deter-mine the shortest path from a source to destinationlocation.

The time intervals between the arrivals of transfercommands are sometimes completely random in thatthere are significant fluctuations in the number of ar-rivals during different time periods. When the queuesize increases on AMHSs, use of different priorities fortransfer commands often provides a very useful solutionto improve overall system performance. It is reportedthat a good priority algorithm can improve the through-put of a production facility [55.15].

There are two types of tables in the database ofthe MMS. One type of table stores parameters for con-trol algorithms and status user interfaces (UIs). Theseneed a minimum number of entities to achieve a shortertransaction time when they are queried. The other typeof table stores data for movement histories based oncommunication messages between component systemsof the IAMHS. The accuracy of these historical datahave been significantly improved by material handlingautomation with advanced information interface tech-nology (IIT) by using RFID technology or barcodesystems. A large amount of information can be ex-tracted from the historical data, including the standardoperating time of a machine, the processing routes of

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a unit load over machines in different process stages,the lead time of the unit load from start to finish, etc.

These data provide useful information for var-ious purposes. Most of all, without accurate datafrom production and warehouse systems, it is hard toachieve successful realization of the enterprise-level de-cision support systems explained above. Good planningstrongly depends on accurate data. In practice, manycompanies have invested in expensive ERP systems ca-pable of automated production planning for their shopfloors; however, many of them do not use the modulebecause of poor planning and scheduling quality fromthe system. One of the main reasons for this poor qualityis sometimes due to the lack of good data from the ma-terial handling system, which relies on manual jobs andoperator paperwork. Accurate data from AMHSs alsohelps to achieve lean manufacturing on the shop floorby providing precise measures. For a complicated shopfloor, it is often difficult to define a bottleneck stage orperformance measures of the bottleneck machines. Leanmanufacturing starts from well-defined and accurateperformance measures. Many details of the machinescan be analyzed by data relating to material movementsfrom machine to machine, examples of which includemachine throughput, product lead time, and the work-in-process (WIP) for each processing stage. Sometimesthey also provide benefits for engineering analysis forthe improvement of quality control. The performanceof the IAMHS can also be measured and improved byusing these historical data. A new algorithm under testcan be easily tracked to assess how it performs in an ac-tual application. Since there are many data transactions,summarized tables are sometimes used for long-termanalysis. Data-mining approaches are helpful in design-ing these tables.

AS/RS #2 MMS AGVS #2 AS/RS #3

Move request #1

Pick-up report #1

Job completion report #2

Port status change request #2

Port status change request #3


Job assign report #1 Move request #2

Move request #3


Port status change #1

Fig. 55.7 Message sequence for a simple transfer command

Another important task of the MMS is the pathmanagement function, which controls a sequence oftransportation jobs. Let us consider the following sim-ple transfer request as an example. A transfer requestis sent to the MMS from the dispatching module tomove a unit load from a rack in AS/RS #2 to AS/RS #3through AGVS #2 in Fig. 55.5. Figure 55.7 showsa message sequence illustrating communication be-tween the MMS and AS/RS controllers involved in thistransfer, and between the MMS and AGV controller.A few more messages might be used in actual systems.As seen in the figure, although this is a relatively sim-ple transferring task, more than 13 messages are usedto complete the task. First, transfer request #1 is trig-gered by the MMS (it can be triggered by either thedispatching module or a procedure of the MMS itself).This request message transmits the source location asAS/RS #2, the destination as the unload port of theAS/RS, and the unit load identity to the AS/RS con-troller #2. If there are other high-level systems such asa WMS or MES, the MMS will send additional mes-sages to these systems. In this case, the status of theunit load possibly needs to be updated from Waiting toBusy or Transferring for the WMS and MES. To sendthis message to AS/RS controller #2, the MMS has tomake at least two major decisions: it has to select anunload port among several idle ports, and to determineto which among the many other AMHSs this messageshould be sent. For the former decision, the closest idleport to the next destination (i. e., AGVS #2) is selectedbased on the MMS algorithm. After receiving the trans-fer request from the MMS, AS/RS controller #2 will putthe job into its queue, if it is performing other tasks. If itis its turn, the controller will send the job assign reportto the MMS so that it triggers another transfer requestcommand to AGV controller #2. This transfer commandcould be sent later after the job completion messagefrom the AS/RS controller #2 has been received; how-ever, by sending before the completion message, it cansynchronize the transfer activities of two systems andthereby reduce the waiting time of the unit load for thevehicle at the unload port of AS/RS #2. The explanationof the rest of the messages in the figure is omitted.

The MMS integrates not only systems but alsohuman operators in the system environment. A userinterface (UI) plays a major role in this integration. Op-erators can monitor the number of AMHSs using the UI.Also, parameters to control an individual AMHS andIAMHS are changed through the UI. Another importantfunction provided is reporting. Various reports can bequeried directly from the database of the MMS.

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Material Handling Automation in Production and Warehouse Systems 55.3 Advanced Technologies 969

55.3 Advanced Technologies

This section surveys advanced technologies enablingthe IAMHS to achieve the high-level MHA. First, theIIT utilizing wireless devices will be reviewed, then thefocus will move onto design and control issues of theMHA. A wide range of methodologies across artificialintelligence (AI) and operations research (OR) tech-niques have been adopted to solve challenging problemsin design and control of MHA. The design and controlissues with AMHS types will be briefly described in-cluding different points of interest. The review focuseson the technical issues of the MMS, which is the mostimportant element of the IAMHS. Finally, AI and ORtechniques are compared according to several criteria inMHA.

55.3.1 Information Interface Technology(IIT) with Wireless Technology

Benefits of wireless communication systems includemobility, installation flexibility, and scalability. Ap-plications of the wireless communication used forMHA are radiofrequency identification (RFID), wire-less local-area network (LAN) (i. e., Ethernet), andwireless input/output (I/O). The wireless sensor net-work has also great potential for many applications ofthe MHA to collect data or form a closed-loop controlsystem.

Radiofrequency Identification (RFID)RFID enhances information tracking with a wide va-riety of applications for material handling [55.16]; forexample, it prevents loss of boxes and incorrect ship-ping in a distribution center and reduces time forreading tags in boxes or carriers on a manufactur-ing shop floor. Its greatest advantages over barcodesystems are its long read range, flexibility of locat-ing tags in boxes, multitasking for reading many tagsat the same time, and robustness against damage. Fi-nally, RFID systems increase the accuracy of data frommaterial handling systems and reduce time for datacollection. With more reliable and faster informationtracking, more sequential operations can be automatedand integrated without affecting system performanceor requiring human interventions. It also enables thedevelopment of higher-level MHA in production andwarehouse systems.

An RFID system consists of tags and readers. AnRFID tag has two components, a semiconductor chipand antenna, and there are basically two types of RFIDtags, passive and active tags, based on the source ofthe power. A passive tag does not have a battery andis powered by the backscattered RF signal from thereader, while an active tag has a battery and is there-fore more reliable. Although the read range of a tagdepends strongly on its power level, antenna, and fre-quency, and the environment in which it is used, anactive tag can have a range of up to 30 m or more whilea passive tag can be read reliably over a few meters.In between these two types, there is a semiactive tagthat is powered by RF from the reader and consumespower from a battery while communicating with thereader. The lifetime of the battery is about 7 years ormore. Another classification of RFID tags is based onthe ability to write information to tags. Some tags areclassified as read-only and can be written only oncebut read many times; these are generally passive tags.Information can be written by both users and produc-ers. There are also rewritable passive tags in whichthe program can be rewritten by users. Most activetags are rewritable. RFID readers send RF signals totags, receive signals from tags, and communicate witha central system. Their functions varies from a simpleon/off check for data collection to control of a largesystem. Popularly used tags are as large as an elec-tronic card, being installed in a larger computer systemwith network capability; however, they can be as smallas 0.05 × 0.05 mm2, as shown in Fig. 55.10. On theright-hand side of Fig. 55.8, powder-type RFID chipsdeveloped by Hitachi are compared with a human hair.

Fig. 55.8 Mu-chips and powder type RFID chips (courtesy of Hi-tachi)

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These powder-type RFID tags are 64 times smaller thanthose in current use (0.4 × 0.4 mm2 mu-chips, on theleft, produced by the same company), which can al-ready be embedded into paper currency, gift certificates,and identification documents. For more information inRFID see Chap. 49.

Wireless LANA wireless LAN establishes a network environment byusing wireless devices instead of wired ones withina limited space. One popular application that adoptswireless LAN in MHA is AGV systems, which use itfor communication between vehicles and controllers.Each vehicle has a network interface card (NIC) thatis connected to the wireless LAN. An access point isa gateway to connect to a wired LAN and similar toa LAN hub, connecting 25–50 vehicles within a rangeof 20–150 m. The infrastructure network is always con-nected to an access point, which connects the wiredLAN with the wireless LAN. In the infrastructure net-work, the basic service set (BSS) is formed and actsas a base station connecting all vehicles in the cell tothe LAN. BSSs that use nonoverlapping channels canbe part of an extended service set (ESS). The vehi-cles within the ESS but in different BSSs are connectedthrough roaming. Lee and Lee [55.17] develop an in-tegrated communication system that connects Profibusand IEEE 802.11, which are wired and wireless LANcommunication protocols, for a container terminal auto-mated by an AGV system. Using this protocol converter,the wireless LAN can be connected to the existing wiredfieldbus for soft real-time data exchange that loses someof its usefulness after a time limit.

Wireless I/OA wireless I/O device is a small circuit card with anantenna installed in a material handling system or itscontroller; it can be used for both data-acquisition andclosed-loop control applications. It receives microwaveradio data from I/O points, and sends those data toa central processing device such as a programmablelogic controller (PLC), data loggers, supervisory con-trol, and data-acquisition system (SCADA), or a generalPC [55.18]. Since it does not use wireless LAN ora fieldbus, implementation is much easier than aforwireless LAN. It can be simply regarded as removingthe necessity for wires; however, by itself, it offers manyadvantages such as broader connectivity, increased mo-bility and flexibility, reduced installation time, andreduced points of failures. One of the disadvantages of

wireless I/O is that, since it uses a relatively narrowrange of wireless signals, a smaller number of wire-less I/Os can be used in a certain area. Therefore, as thenumber of points in an area grows, a wireless or wiredLAN will become more appropriate.

Wireless Sensor NetworksSensor networks [55.19] are currently limited to novelsystems. Many sensors, distributed in a system orarea, can be used to build a network for monitor-ing a space shuttle, military equipment unit or nuclearpower plant. Wireless sensor networks conceptually usesmall, smart, cheap sensors that consist of a sensingmodule, a data-processing module, and communica-tion components; however, conventional sensors canalso be used. The network is mainly used for mon-itoring systems that requires highly autonomous andintelligent decision-making in a dynamic and uncer-tain environment. They have a great deal of potentialto be adopted in MHA even though few researchershave studied these applications. There are two ar-eas of wireless sensor network applications for theMHA.

First, reliability is often very important for the MHAbecause, in a highly automated system, the failure ofan AMHS causes the breakdown of multiple machinesor a whole area operated by the system. This may bemore critical than the failure of an individual process-ing tool in production systems. Therefore, monitoringand diagnosing the AMHS lead to some important is-sues; for instance, vibration sensors and optical sensorsattached to the crane of an AS/RS collaborate to de-tect a potential problem that might cause positioningor more critical errors. By detecting the problem beforethe AS/RS actually breaks down, engineers can recog-nize the problem more precisely and prepare requiredparts and tools in advance; hence, repair time can besignificantly reduced.

Second, most AMHSs use a closed feedback sys-tem that controls the system based on feedback fromcomponent systems or sensors. Walker et al. [55.20]studied a method to control an industrial robot that han-dles flexible materials such as wires and rubber hoses. Itutilizes feedback from sensor network cameras to pre-dict the motion of the robot with the better vision. Sincethe feedback can be created from many different pointssuch as grasps, paths, and goal points, it reduces blindspots of unpredictable motions and greatly enhancescontrol precision. Chapter 20 provides additional infor-mation on sensor networks.

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Table 55.1 Design issues and related studies on MHA

Reference AMHS type Design issue Criteria Solution approach

Cho and Egbelu [55.21] IAMHS MHS equipment Qualitative factors, Fuzzy logic and

selection problem equipment variety knowledge-based rule

(minimizing)

Nadoli and IAMHS Design and Design lead time Expert system,

Rangaswami [55.22] modeling for a new computer simulation

semiconductor fab

Jimenez et al. [55.23] IAMHS Performance Delivery time, Computer simulation

evaluation of AMHS transport time,

throughput

Huang et al. [55.24] General Location of MHS Total distance, Lagrangian relaxation

MHS fixed cost of MHS and heuristic method

Jang et al. [55.25] AS/RS Estimation of AS/RS Delivery rate, Queuing network

performance in-process inventory model

Lee et al. [55.26] AS/RS Optimal design of Space utilization Modular cells,

rack structure with (lost space) heuristic

various sized cells

Ting and Tanchoco AGV Location of the Total rectilinear MIP

[55.27] central path distance

Gaskins and Tanchoco AGV Guide path design: Total flow distance Integer programming,

[55.28] direction of path heuristic

segments

Tanchoco and Sinriech AGV Guide path design: Total flow distance Integer programming

[55.29] optimal design of

a single-loop

Bozer and Srinivasan AGV Guide path design: Balanced workload Integer programming,

[55.30] tandem guide path set partition

Caricato and Grieco AGV Guide path design Flow distance, Simulated annealing

[55.31] computation time

Nazzal and McGinnis AGV Estimation of Vehicle utilization, Queuing network

[55.32] performance blocking time, model

measures empty vehicle

interarrival time

Vis et al. [55.33] AGV Estimation of the Service level Network flow

number of vehicles (waiting time)

55.3.2 Design Methodologies for MHA

MHA design studies largely deal with strategicdecision-making, which includes optimal selection ofautomated material handling equipment, locating stor-age and vehicle paths for new facility planning, rackdesign for AS/RSs, flow path design for AGV sys-

tems, and capacity estimation of the system. Table 55.1briefly summarizes studies related to design issues. TheMHA design problem is sometimes closely related tothe layout design problem in that both consider issuesat a very early stage of system implementation. Also,they share performance measures in many areas. Pe-ters and Yang [55.34] integrate these two methods into

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a single procedure using the space-filling curve (SFC)method. Ting and Tanchoco [55.27] propose a new lay-out design method for a semiconductor fab. They usean integer programming model to determine the optimallocation of the AGV track. Chung and Jang [55.35] alsosuggest a new layout alternative called integrated roomlayout for better material handling in a semiconductorfab and scrutinize the benefits of the layout comparedwith existing layout alternatives in the industry usingqualitative and quantitative analysis.

One of the difficulties in design of large-scaleIAMHSs is estimation of system capacity. Althoughcomputer simulation has been used, its feedback cyclefrom modeling to results analysis is very slow for a largeproblem, which is an issue as timing of the solutionis sometimes very important. Also, a simple determin-istic analysis using from–to charts of material flowscannot provide a precise estimation of variances in thesystem. As an alternative approach studied for capac-ity analysis, the queuing network approach shows goodperformance [55.32]. Rembold and Tanchoco [55.36]explore a framework that evaluates and improves a se-quence of modeling tasks for material flow systems.They aim to develop a more fundamental solution tothe problems while encountered while designing anIAMHS. The framework addresses the following ques-tions of designers: selection of the software applicationfor solving a problem, organizing the data sets requiredfor the design, incorporation of the design into partsthat cannot be automated, and diagnosing problems inmaterial flow systems. Those authors use an open archi-tecture for the framework, since advance identificationof all factors and cases for evaluation and redesign ofthe material flow processes are limited. With the openarchitecture for the framework, users can easily findtheir own methods by incorporating ad hoc situationsinto the framework.

55.3.3 Control Methodologies for MHA

Extensive research has been performed on the controlof the AMHS. Especially, AGV control problems havebenefited from strong research streams in academia andthe MHA industry, since AGVs have been popular foruse in many industries. Figure 55.9 shows an inter-esting AGV design with many storage racks that isused in a hospital. Recently, two well-organized liter-ature surveys on the AGV system were published byVis [55.37], and by Le-Anh and De Koster [55.38]. Oneof the characteristics of control algorithms of MHAis that minimizing flow distance in time is a dom-

Fig. 55.9 AGV used in a hospital (courtesy of Egemin)

inant criterion, among others. Other criteria such asresource utilization, throughput, and load balance havefrequently been subgoals to achieve the minimum flowtime. Necessity for a very short response time is an-other characteristic of control algorithms for the MHA;for example, a vehicle dispatch algorithm for the AGVcontroller should respond within a few seconds or less,otherwise the vehicle will have to wait for a job com-mand on the path. For a short response time, the timehorizon of the control algorithms is zero or very short,because a longer time horizon often causes an explo-sion of the search space. The minimum control horizonalso helps to yield a reliable solution because uncer-tain parameters will be used less. If a control algorithmmalfunctions, the result will be more serious than justa performance drop. It sometimes causes a detrimentalfailure in the shop floor. Hence, a conservative approachtends to be used in real applications.

A big challenge in AGV control problems is thatusers want to use a larger loop with many vehicles in or-der to reduce transportation time and investment. AGVsystems implemented earlier generally used a modularstructure to avoid heavy load on one AGV loop and hadmany loops, with a maximum of about five vehicles ina loop; however, these days, a large loop with a maxi-mum about 40 vehicles is used. Therefore, the vehicledispatch, scheduling, routing, and deadlock avoidanceproblems are becoming more complicated and impor-tant. Table 55.2 summarizes control issues and theirstudies in the MHA.

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Table 55.2 Control issues and related studies on MHA

Researchers AMHS type Control issue Criteria Solution approach

Dotoli and Fanti [55.39] IAMHS Integrated AS/RS and Throughput, Colored Petri nets

RGV control computation time

Mahajan et al. [55.40] AS/RS Job sequencing Throughput Heuristic

(nearest neighborhood)

Lin and Tsao [55.8] AS/RS Crane scheduling for Total fulfillment time Heuristic

batch job in CIM of batch (dynamic availability

environment oriented controller)

Lee et al. [55.41] AS/RS Rack assignment for Expected travel time Heuristic (storage

cargo terminals reservation policy),

stochastic demand stochastic

Chetty and Reddy [55.42] AS/RS Job sequencing 10 criteria (mean flow Genetic algorithm

time, mean waiting

time, min/max

completion time, etc.)

Sinriech and Palni [55.43] AGV Vehicle scheduling Optimality of MIP, heuristic (branch

scheduling solution and bound)

Correa et al. [55.44] AGV Vehicle scheduling Solution time, MIP and CP hybrid

job processing time method

Jang et al. [55.45] AGV Vehicle routing in AGV utilization, Heuristic, look-ahead

clean bay WIP level control procedure

Koo et al. [55.46] AGV Vehicle dispatching Production Heuristic, bottleneck-

throughput, lead time machine first

Kim et al. [55.47] AGV Vehicle dispatching Production throughput Heuristic

in floor shop (balanced work load)

Jeong and Randhawa [55.48] AGV Vehicle dispatching Vehicle travel time, Heuristics, multi-

blocking time, WIP attribute dispatching

Moorthy et al. [55.49] AGV Deadlock avoidance Number of AGVs in Heuristic,

in large-scale AGVS a loop, state prediction

(cycle deadlock) number deadlocks

Bruno et al. [55.50] AGV Empty vehicle Response time Heuristic (location

parking model (MIP) and

shortest path algorithm)

IAMHS ResearchResearchers recently started to study complicated is-sues of the IAMHS. A major concern is routingstrategies from source to destination location in a com-plicated IAMHS, in which there are multiple routesfrom one location to the others. The routes consistof not only physical paths such as an AGV path orconveyor track but also AMHS themselves, such asAS/RSs, AGVSs, and buffer stations. Practical appli-cations generally store predetermined static shortestroutes in a database for all pairs of source locations anddestinations; however, when the number of componentsincreases, maintenance problems for the parameters in-

volved become much more difficult since there are toomany combinations of nodes. The shortest-distance al-gorithm using graph theory with an adjacency matrixmight be a better approach.

A new concept called flow diversion is proposedto determine dynamic routing based on the load rateof the routes in automated shipment handling sys-tems by Cheung et al. [55.51]. The authors utilize themulticommodity flow models using linear program-ming (LP) to solve this problem. In this model, thetransfer time for a route is a function of the loads as-signed to all pairs of unit loads in the system, whichgenerates a nonlinear function in the objective func-

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tion; those authors transform this nonlinear functionto a piecewise-linear function to make the problemtractable. Lau and Zhao [55.4] study a joint job schedul-ing problem for the automated air cargo terminal atHong Kong, which is mainly composed of AGV sys-tems, AS/RSs, cargo hoists, and conveyors. In themodel, activities between different AMHSs are trig-gered by communication between the systems. Thescheduling algorithm constructs a cooperative sequen-tial job served by different AMHSs, employing themaximum matching algorithm of the bipartite graph.A task for an AGV is assigned or matched to an stackercrane (SC) to reduce the SC delay time. A similar prob-lem is solved by Meersmans and Wagelmans [55.5].Their research focuses on the scheduling problem of theIAMHS in seaport terminals employing a local beamsearch algorithm. The nodes explored in the search al-gorithm are represented by a sequence of container IDsto be processed by different AMHSs, and the nodes inbranches are cut based on the beam width determined byan evaluation function. Those authors prove that thereexists an optimal sequence of tasks for one AMHS whenthe sequence is assigned to the other AMHS.

Sujono and Lashkari [55.52] study another inte-grating method allocating a part type to a processingmachine and material handling (MH) equipment typesimultaneously in a flexible manufacturing system(FMS). In that research, there are nine different typesof the material handling systems in the experimentalmodel. The method improves the algorithms proposedby Paulo et al. [55.53] and Lashkari et al. [55.54] anduses a 0/1 mixed integer programming model. Two ob-jective functions are modeled: one minimizes operatingcosts related to machine operations, setup, and MHoperations; the other maximizes the compatibility ofthe part types using MH equipment types. To measurecompatibility, parameters are quantified from the sub-jective factors defined by Ayres [55.55]. Some of theconstraints are: balance equations between parts andprocess plans, machines and process plan, processingmachines, and MH equipment types. The other impor-tant constraint sets are capacity constraints: the totalload of the allocated tasks for an MH equipment typecannot exceed its capacity, and a machine cannot beallocated more than its capacity. A test problem con-sisting of 1356 constraints and 3036 binary variableswas solved in about 9.2 s by using LINGO in a Pen-tium 4 PC. Since this model considers many details ofthe practical factors in the FMS, and showed a suc-cessful calculation result, it can be used for many otherpractical applications.

In addition to the examples shown above, large-scale optimization problems such as the vehicle routingproblem (VRP), vehicle scheduling problem (VSP), andintegrated scheduling problem of IAMHS with con-sideration of processing machines have been modeledto increase MHA efficiency. However, to be used foractual applications and thereby achieve a higher-levelMHA, shorter computation times are urgently required.In a complicated IAMHS, integrating software pack-ages such as the MMS need sophisticated algorithms;however, it also needs high reliability in a dynamic en-vironment. For most tasks, real-time decision-makingthat requires response times within a few seconds isa precondition for IAMHS algorithms.

MMS-related IssuesThe MMS is a key component to integrate differentAMHSs in an IAMHS. Destination allocation, routingalgorithm, and prioritizing algorithm are essential rolesof the MMS, among others. Graph theory is popularlyused to represent components and relationships in theIAMHS. In Fig. 55.6, nodes represent the AMHSs andtheir subcomponents, such as load/unload ports. Edgesrepresent the connection and distance between nodes.As mentioned above, this graph is stored in databasetables using the adjacency and incidence matrices. Theshortest-path algorithm is the most important and funda-mental algorithm for an MMS since it is used for severalpurposes in the system such as destination assignmentand best routing determination. Dijkstra’s algorithm ispopularly used [55.56]. The Bellman–Ford algorithmcan be used if there are negative weights of the edges.

To determine the final destination of a unit load,the MMS has to evaluate various factors on the samescale. More specifically, to determine an AS/RS as thefinal destination among several alternatives, the shortestdistance is generally the most important criterion; how-ever, the full rates of the AS/RSs are sometimes alsoimportant to make the loads balanced between differentAS/RSs. There are two applicable ways to standard-ize different scales of factors on the same scale. First,different weight values can be applied for each factorto find the best alternatives. Second, a priority and itsthreshold value can be given to each factor, and the mostimportant alternative is selected if it is within the thresh-old, otherwise the next alternative will be considered.

Determining the best route from a source to desti-nation location via several AMHSs is relatively simplewhen compared with the vehicle routing problem (VRP)or vehicle scheduling problem (VSP), because the graphgenerally has a smaller number of nodes than those of

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the general VRP or VSP. However, the problem can becomplicated when the load level of systems has to betaken into account. A flow cost function determines theweight value of an edge based on the queue size andsystem processing time for one unit in an AMHS, i. e.,the load level is measured by these factors. It convertsthe weight value to a distance value by using the speedfactor of the system. In an actual problem, this task canbe considerably more complicated.

Prioritizing for the unit load is sometimes very use-ful for vehicle-initiated dispatching rules [55.11] whenthe IAMHS becomes a bottleneck in an FMS for a cer-tain period of time. The priority determined by theMMS can be used by an AGV controller to determinea job priority for a vehicle that has just become idle;for example, the first-come first-served rule picks upthe job with the longest waiting time for all unit loadsin the queue. If a priority is given to each job from 1to 5, the priority unit can be treated as a certain timescale, e.g., 10 min, for each unit. Together with the ac-tual waiting time, the controller can prioritize the unitloads; for instance, if a unit load waits for a vehicle as-signment for 5 min and its priority given by the MMSis 3, then its final priority can be 10 × 2+5 min, whichis equal to 25 min. The prioritizing methods used by theMMS generally address problems of how to avoid ma-chine starvation. While various MMS prioritizing rulescan be used based on the constraints of the shop floor,the importance of considering bottleneck machines todetermine transfer priorities of unit loads is emphasizedby Koo et al. [55.46] and Li et al. [55.15].

55.3.4 AI and OR Techniques for MHA

It is worthwhile to compare AI search and OR optimiza-tion techniques with respect to several different criteriaof logical flexibility, computation time, and applicationareas in MHA. In general, AI search algorithms defineproblems with four instances: initial state, successorfunction, goal test, and path cost function [55.57]. Theinitial state is a state in which the given problem starts.The successor function receives a state as a param-eter and returns a set of actions and successors. Andthe successors are new states reachable from the givennew state. The definition of the state together with thesuccessor function is very important to determine theoverall search space of the given problem and informa-tion necessary for the solution. The goal test determineswhether a given state satisfies all the conditions of thegoal state. The path cost function calculates a numericalcost for each path explored by the successor function.

There are two types of AI search algorithms: unin-formed and informed search. Uninformed search doesnot use prior information to explore a solution. Exam-ples of uninformed search algorithms are the depth-firstsearch, breadth-first search, and bidirectional search.Informed search utilizes given information for newstates that will be opened during the search. Informedsearch is also called heuristic search, which includesgreedy best-first search, A* search, memory-bounded,and local beam search, which again includes simu-lated annealing, tabu search, and genetic algorithm.Constraint programming (CP) is one of the AI searchmethods that uses a standard structured representationconsisting of the problem domain and constraint. Fig-ure 55.10 explains the main procedure used by theILOG CP solver [55.58]. The domain is a set of pos-sible values of the variable representing the problem,and the constraint is a rule that imposes a limitation onthe variable. The most powerful aspect of this method isthat it utilizes the concept of constraint propagation. Itnarrows down the search space by imposing a constrainton variables and the constraint imposed further reducesdomains of other variables based on the constraints al-ready posted on the variables. Among reduced domainsof variables, the method uses a branching process witha backtracking algorithm to find the best solution. Be-cause of high modeling flexibility, AI techniques havebeen popularly used for a wide variety of control appli-cations such as robotics and automated planning.

The following studies illustrate the use of AItechniques for MHA. Cho and Egbelu [55.21] use

Decision variables and domains

Initial constraint propagation

Create a search tree

Search strategy

Fail

Backtrack Constraint propagationduring search

Solution

Search space Constraints

Fig. 55.10 Main solution procedure of CP (courtesy ofILOG)

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Table 55.3 Comparison of AI and OR techniques

Comparison items AI approaches OR approaches Hybrid approaches

Modeling flexibility High Low High

Time horizon Short Long Long

Response time Short Long Medium

Problem size Small Large Large

Illustrations IAMHS design: IAMHS design: AGV vehicle scheduling

AMHS equipment type Performance evaluation [55.25, 32], [55.44, 60]

selection [55.21] AGV: IAMHS:

AS/RS: Guide path design [55.28], Integrated scheduling

Job sequencing [55.40], AMHS location [55.24, 27] of AMHS and FMS [55.52]

Stacker scheduling [55.8],

Rack design [55.26]

AGV:

Deadlock avoidance [55.49]

knowledge-based rules, fuzzy logic, and decision al-gorithms to address AMHS equipment type selectionproblems. Their procedures consist of three phases:material handling equipment selections for each ma-terial flow connections, redundancy and excess capac-ity check, and budget constraint consideration. Chanet al. [55.59] also solve a similar problem using an ex-pert system. An order picking sequence problem in anAS/RS is addressed by Mahajan et al. [55.40] by us-ing an AI technique. In their procedure, the state isrepresented by a sequence of the orders, and a suc-cess function providing a selection criterion of theorder sequence is developed by a nearest-neighborhoodstrategy.

Operations research techniques mainly focus on theoptimization problems based on linear programming(LP) [55.61]. LP is extended to integer program-ming (IP) and mixed integer programming (MIP), thatdeal with integral variables, quadratic programmingthat uses a nonlinear objective function, and nonlin-ear programming that allows nonlinear functions inboth the constraint and objective function. Stochasticprogramming, which incorporates uncertainties in itsmodeling, is also a variant of the LP. OR techniques usewell-structured mathematical models of linear, integer,quadratic or nonlinear models. Simulation and queu-ing analysis form another important technical area ofstochastic OR, mainly used for performance analysis.

OR techniques are also popular for solving prob-lems in MHA. Gaskins and Tanchoco [55.28] andTanchoco and Sinreich [55.29] formulate AGV guidepath design problems using 0/1 MIP. Nazzal andMcGinnis [55.32] estimate the system capacity require-ment of the large-scale AMHS in a semiconductor

fab by utilizing a queuing network model. Ting andTanchco [55.27] and Huang et al. [55.24] addressthe location problems of the AMHSs in facility lay-outs using MIP formulations. Huang et al. further usea heuristic approach employing the Lagrangian relax-ation method.

AI and OR have their backgrounds in computerscience and industrial engineering, respectively. AIapproaches utilize knowledge representation to solvea problem; however, OR techniques use mathemati-cal modeling of the problem. Knowledge representationconsists of symbols and mathematical equations withrelationships. OR techniques generally use dedicatedsolvers such as CPLEX and LINDO to solve mathemat-ical models of the problems, whereas AI techniques usetheir own languages such as list processing (LISP), andprogramming in logics (Prolog). Constraint program-ming (CP) uses a solver similarly to the OR solversbut has much greater flexibility in using procedures andalgorithms. A widely known CP solver is the ILOGsolver. One advantage of AI over OR techniques istheir flexibility in expressing problems. Since the AItechniques listed above do not use strict mathemati-cal formulations to represent problems, there is a greatdeal of flexibility to deal with instances and activitiesin the problems. On the other hand, OR techniquesmodel problems with strict mathematical proceduresthat are generally used repeatedly in different problems.While OR techniques find optimal or near-optimal so-lutions, AI techniques find good solutions for the givenproblems. OR techniques have focused on large-scaleoptimization problems for decision support systemswhile AI techniques are rooted in control problems thathave shorter horizons but need reliable solutions. How-

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Material Handling Automation in Production and Warehouse Systems References 977

ever, it is also true that there has been some overlapbetween AI and OR techniques, especially for localbeam search algorithms. Also, a group of researchershas tried to take advantage of the two techniques by in-tegrating procedures in the techniques [55.44]. For morecomplete reviews on the history and state of the art com-paring AI and OR techniques, refer to Gomes [55.61],Kobbacy et al. [55.62], and Marcus [55.63].

Table 55.3 compares AI, OR, and their hybridapproaches with several different characteristics. Anapplication area that needs great logical flexibility,such as the selection problem of material handlingsystems, tends to use AI techniques more frequently.Problems with shorter time horizon use AI heuristicsearch approaches (second row in the table), and prob-

lems with a longer time horizon tend to use the ORapproaches. Hybrid approaches focus on reducing com-putation times. OR techniques are frequently used forAMHS design problems because response time is lessimportant for them and they consider a large num-ber of instances in the system. The AGV dispatchingand routing problems tend to use both heuristic andOR approaches to similar degrees. Approaches inte-grating AI and OR approaches pursue both flexibilityand optimality and have been applied to very compli-cated problems for the MHA [55.44], which deal withAGV scheduling problems and integrated scheduling ofIAMHS with FMS. Examples on application areas andtheir approaches used are listed in the last row of Ta-ble 55.3.

55.4 Conclusions and Emerging Trends

Material handling automation (MHA) in production andwarehouse management systems provides speedy andreliable infrastructure for information systems in SCMsuch as ERP system, FMS, WMS, and MES. Most ofall, it enhances accurate data tracking during mater-ial handling in shop floors and warehouses. Relyingon these accurate data, high-level automation such asproduction and procurement planning, scheduling, anddispatching in the SCM systems can be made muchmore reliable; consequently, more intelligent functionsin their decision-making procedures can be added. An-other trend in MHA, the integrated and automatedmaterial handling system (IAMHS), has been increas-ingly implemented in various applications to helpever-complicated material handling operations in large-scale production and warehouse systems. The importantissues of the IAMHS reviewed in this chapter can belargely broken down into design and control issues. Thedesign issues cover material handling equipment selec-tion, capacity estimation, innovative equipment design,

and system design optimization. The control issues thathave been hard constraints for the higher-level MHAtend to involve domain-specific problems for each com-ponent system in the IAMHS such as the AS/RS,AGVS, or MMS.

Several potential routes for further increasing thelevel of intelligence in the MHA are recognized inthis chapter. While the number of components in theIAMHS continues to increase, long response time isregarded as a major limitation during implementa-tions of new control algorithms. Continuous effortsto reduce the computation time of algorithms in thefuture are desired. It is also pointed out that newlydeveloped algorithms should take into account systemarchitecture for practical applications. As another pos-sibility, a sensor network might be used for diagnosisof AMHSs since their reliability is becoming critical,and also its closed feedback mechanism can poten-tially be used for more precise controls, as seen in eachcontext.

References

55.1 Material Handling Industry of Americahttp://www.mhia.org/ir/, (last accessed February15, 2009)

55.2 J.R. Montoya-Torres: A literature survey on thedesign approaches and operational issues of au-tomated wafer-transport systems for wafer fabs,Prod. Plan. Control 17(7), 648–663 (2006)

55.3 T. Feare: GM runs in top gear with AS/RS sequenc-ing, Mod. Mater. Handl. 53(9), 50–52 (1998)

55.4 H.Y.K. Lau, Y. Zhao: Joint scheduling of mater-ial handling equipment in automated air cargoterminals, Comput. Ind. 57(5), 398–411 (2006)

55.5 P.J.M. Meersmans, A.P.M. Wagelmans: DynamicScheduling of Handling Equipment at Automated

PartF

55

http://www.mhia.org/ir/


Container Terminals, Econometric Institute ReportEI 2001-33 (Erasmus University, Rotterdam 2001)

55.6 Murata Machinery,http://www.muratec-l-system.com/en/example/deliver/medical.html (last accessed February 15,2009)

55.7 L. Kempfer: Produce delivered fresh and fast,Mater. Handl. Manag. March, 40–42 (2006)

55.8 C.W.R. Lin, Y.Z. Tsao: Dynamic availability-orientedcontrol of the automated storage/retrieval system.A computer integrated manufacturing perspective,Int. J. Adv. Manuf. Technol. 29(9-10), 948–961(2006)

55.9 T.H. Chang, H.P. Fu, K.Y. Hu: The innovative con-veying device application for transferring articlesbetween two-levels of a multi-story building, Int.J. Adv. Manuf. Technol. 28(1-2), 197–204 (2006)

55.10 B. Rembold, J.M.A. Tanchoco: Modular frameworkfor the design of material flow systems, Int. J. Prod.Res. 32(1), 1–21 (1994)

55.11 P.J. Egbelu, J.M.A. Tanchoco: Characterization ofautomated guided vehicle dispatching rules, Int.J. Prod. Res. 22(3), 359–374 (1984)

55.12 L. Hossain, J.D. Patrick, M.A. Rashid: EnterpriseResource Planning: Global Opportunities and Chal-lenges (Idea Group, Hershey 2002)

55.13 D.S. Sun, N.S. Park, Y.J. Lee, Y.C. Jang, C.S. Ahn,T.E. Lee: Integration of lot dispatching and AMHScontrol in a 300 mm wafer FAB, IEEE/SEMI Adv.Semiconduc. Manuf. Conf. Workshop – Adv. Semi-conduct. Manuf. Excellence (2005) pp. 270–274

55.14 J. Jimenez, B. Kim, J. Fowler, G. Mackulak,Y.I. Choung, D.J. Kim: Operational modeling andsimulation of an inter-bay AMHS in semiconduc-tor wafer fabrication, Winter Simul. Conf. Proc. 2,1377–1382 (2002)

55.15 B. Li, J. Wu, W. Carriker, R. Giddings: Factorythroughput improvements through intelligent in-tegrated delivery in semiconductor fabricationfacilities, IEEE Trans. Semiconduct. Manuf. 18(1),222–231 (2005)

55.16 S.S. Garfinkel, B. Rosenberg: RFID Applications,Security, and Privacy (Addison-Wesley, New York2006)

55.17 K.C. Lee, S. Lee: Integrated network of Profibus-DPand IEEE 802.11 wireless LAN with hard real-timerequirement, IEEE Int. Symp. Ind. Electron. 3, 1484–1489 (2001)

55.18 A. Herrera: Wireless I/O devices in process controlsystems, Proc. ISA/IEEE Sensors Ind. Conf. (2004)pp. 146–147

55.19 S. Phoha, T. LaPorta, C. Griffin: Sensor NetworkOperations (Wiley, Piscataway 2006)

55.20 I. Walker, A. Hoover, Y. Liu: Handling unpredictedmotion in industrial robot workcells using sensornetworks, Ind. Robot. 33(1), 56–59 (2006)

55.21 C. Cho, P.J. Egbelu: Design of a web-based inte-grated material handling system for manufacturingapplications, Int. J. Prod. Res. 43(2), 375–403 (2005)

55.22 G. Nadoli, M. Rangaswami: Integrated modelingmethodology for material handling systems de-sign, Winter Simul. Conf. Proc. (1993) pp. 785–789

55.23 J.A. Jimenez, G. Mackulak, J. Fowler: Efficientsimulations for capacity analysis and automatedmaterial handling system design in semiconduc-tor wafer fabs, Winter Simul. Conf. Proc. (2005)pp. 2157–2161

55.24 S. Huang, R. Batta, R. Nagi: Variable capacity sizingand selection of connections in a facility layout, IIETrans. 35(1), 49–59 (2003)

55.25 Y.J. Jang, G.H. Choi, S.I. Kim: Modeling and analysisof stocker system in semiconductor and LCD fab,IEEE Int. Symp. Semiconduct. Manuf. Conf. Proc.ISSM 2005 (2005) pp. 273–276

55.26 Y.H. Lee, M.H. Lee, S. Hur: Optimal design of rackstructure with modular cell in AS/RS, Int. J. Prod.Econ. 98(2), 172–178 (2005)

55.27 J.-H. Ting, J.M.A. Tanchoco: Optimal bidirectionalspine layout for overhead material handling sys-tems, IEEE Trans. Semiconduct. Manuf. 14(1), 57–64(2001)

55.28 R.J. Gaskins, J.M.A. Tanchoco: Flow path design forautomated guided vehicle systems, Int. J. Prod.Res. 25(5), 667–676 (1987)

55.29 J.M.A. Tanchoco, D. Sinriech: OSL – optimal singleloop guide paths for AGVS, Int. J. Prod. Res. 30(3),665–681 (1992)

55.30 Y.A. Bozer, M.M. Srinivasan: Tandem AGV system:a partitioning algorithm and performance compar-ison with conventional AGV systems, Eur. J. Oper.Res. 63, 173–191 (1992)

55.31 P. Caricato, A. Grieco: Using simulated annealingto design a material-handling system, IEEE Intell.Syst. 20(4), 26–30 (2005)

55.32 D. Nazzal, L.F. McGinnis: Analytical approach to es-timating AMHS performance in 300 mm fabs, Int. J.Prod. Res. 45(3), 571–590 (2007)

55.33 I.F.A. Vis, R. de Koster, K.J. Roodbergen,L.W.P. Peeters: Determination of the number ofautomated guided vehicles required at a semi-automated container terminal, J. Oper. Res. Soc.52(4), 409–417 (2001)

55.34 B.A. Peters, T. Yang: Integrated facility layout andmaterial handling system design in semiconduc-tor fabrication facilities, IEEE Trans. Semiconduct.Manuf. 10(3), 360–369 (1997)

55.35 J. Chung, J. Jang: The integrated room layout forsemiconductor facility plan, IEEE Trans. Semicon-duct. Manuf. 20(4), 517–527 (2007)

55.36 B. Rembold, J.M.A. Tanchoco: Material flow systemmodel evaluation and improvement, Int. J. Prod.Res. 32(11), 2585–2602 (1994)

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http://www.muratec-l-system.com/en/example/deliver/medical.html

Material Handling Automation in Production and Warehouse Systems References 979

55.37 I.F.A. Vis: Survey of research in the design and con-trol of automated guided vehicle systems, Eur. J.Oper. Res. 170(3), 677–709 (2006)

55.38 T. Le-Anh, M.B.M. De Koster: A review of designand control of automated guided vehicle systems,Eur. J. Oper. Res. 171(1), 1–23 (2006)

55.39 M. Dotoli, M.P. Fanti: A coloured Petri net model forautomated storage and retrieval systems servicedby rail-guided vehicles: a control perspective, Int.J. Comput. Int. Manuf. 18(2-3), 122–136 (2005)

55.40 S. Mahajan, B.V. Rao, B.A. Peters: A retrievalsequencing heuristics for miniload end-of-aisleautomated storage/retrieval system, Int. J. Prod.Res. 36(6), 1715–1731 (1998)

55.41 C. Lee, B. Liu, H.C. Huang, Z. Xu, P. Golds-man: Reservation storage policy for AS/RS at aircargo terminals, Winter Simul. Conf. Proc. (2005)pp. 1627–1632

55.42 O.V.K. Chetty, M.S. Reddy: Genetic algorithms forstudies on AS/RS integrated with machines, Int. J.Adv. Manuf. Technol. 22(11-12), 932–940 (2003)

55.43 D. Sinriech, L. Palni: Scheduling pickup anddeliveries in a multiple-load discrete carrier envi-ronment, IIE Trans. Inst. Ind. Eng. 30(11), 1035–1047(1998)

55.44 A.I. Corréa, A. Langevin, L.M. Rousseau: Schedulingand routing of automated guided vehicles: a hy-brid approach, Comput. Oper. Res. 34(6), 1688–1707(2007)

55.45 J. Jang, J. Suh, P.M. Ferreira: An AGV routing policyreflecting the current and future state of semicon-ductor and LCD production lines, Int. J. Prod. Res.39(17), 3901–3921 (2001)

55.46 P.H. Koo, J. Jang, J. Suh: Vehicle dispatching forhighly loaded semiconductor production consider-ing bottleneck machines first, Int. J. Flex. Manuf.Syst. 17(1), 23–38 (2005)

55.47 C.W. Kim, J.M.A. Tanchoco, P.-H. Koo: AGV dis-patching based on workload balancing, Int. J.Prod. Res. 37(17), 4053–4066 (1999)

55.48 B.H. Jeong, S.U. Randhawa: A multi-attributedispatching rule for automated guided vehicle sys-tems, Int. J. Prod. Res. 39(13), 2817–2832 (2001)

55.49 R.L. Moorthy, W. Hock–Guan, W.-C. Ng, T. Chung–Piaw: Cycle deadlock prediction and avoidance forzone controlled AGV system, Int. J. Prod. Econ. 83,309–324 (2003)

55.50 G. Bruno, G. Ghiani, G. Improta: Dynamic position-ing of idle automated guided vehicles, J. Intell.Manuf. 11(2), 209–215 (2000)

55.51 R. Cheung, A. Lee, D. Mo: Flow diversion ap-proaches for shipment routing in automaticshipment handling systems, Proc. – IEEE Int. Conf.Robot. Autom. (2006) pp. 695–700

55.52 S. Sujono, R.S. Lashkari: A multi-objective model ofoperation allocation and material handling systemselection in FMS design, Int. J. Prod. Econ. 105(1),116–133 (2007)

55.53 J. Paulo, R.S. Lashkari, S.P. Dutta: Operation al-location and materials-handling system selectionin a flexible manufacturing system: a sequentialmodeling approach, Int. J. Prod. Res. 40, 7–35(2002)

55.54 R.S. Lashkari, R. Boparai, J. Paulo: Towards anintegrated model of operation allocation and ma-terials handling selection in cellular manufacturingsystem, Int. J. Prod. Econ. 87(2), 115–139 (2004)

55.55 R.U. Ayres: Complexity, reliability and design:manufacturing implications, Manuf. Rev. 1(1), 26–35 (1988)

55.56 R.K. Ahuja, T.L. Magnanti, J.B. Orlin: Networkflows: theory, algorithms, and applications (Pren-tice Hall, Upper Saddle River 1993)

55.57 S. Russell, P. Norvig: Artificial Intelligence: a Mod-ern Approach (Prentice Hall, New York 2003)

55.58 ILOG Solver 5.3 user manual55.59 F.T.S. Chan, R.W.L. Ip, H. Lau: Integration of ex-

pert system with analytic hierarchy process for thedesign of material handling equipment selectionsystem, J. Mater. Process. Technol. 116(2-3), 137–145(2001)

55.60 D. Naso, B. Turchiano: Multicriteria meta-heuristicsfor AGV dispatching control based on computa-tional intelligence, IEEE Trans. Syst. Man Cybern.B 35(2), 208–226 (2005)

55.61 C.P. Gomes: Artificial intelligence and operationsresearch: challenges and opportunities in planningand scheduling, Knowl. Eng. Rev. 15(1), 1–10 (2000)

55.62 K.A.H. Kobbacy, S. Vadera, M.H. Rasmy: AI and orin management of operations: history and trends,J. Oper. Res. Soc. 58(1), 10–28 (2007)

55.63 R. Marcus: Application of artificial intelligence tooperations research, Commun. ACM 27(10), 1044–1047 (1984)

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