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282 Business Sustainability I

Simulation of AGVs Miranda, Cunha, Oliveira

ANALYSIS OF THE SIMULATION METHODOLOGY OF PALLETS TRANSPORT

PROJECTS BY AGVS

Pedro Miranda, Universidade do Minho, [email protected] Cunha, Universidade do Minho, [email protected]

José A. Oliveira, Universidade do Minho, [email protected]

Keywords: Technology, simulation, techniques and tools for industrial engineering, supply chain,return on investment, manufacturing systems.

INTRODUCTION

This paper analyzes the benefits of using thesimulation tool in a pallets transport project byusing automatically guided vehicles (AGVs). Thesimulation is currently recognized as a powerfuland flexible tool which is becoming essential to theviability of a variety of existing systems/projects. Inthis paper we examine the specific models of thesimulation created to analyze of AGV systems. Tobetter understand the advantages inherent to thesimulation and in order to understand what causedits development, this paper researches theanalytical and simulation tools.

THE REAL PROBLEM

This project investigates the use of AGVs onthe shop floor instead of transport using stackers,guaranteeing the continuous supply between theprocess supplier and customer. The problem isbased on three machines that continuouslyproduce 30 different kids of materialsautomatically put on pallets and that will be usedby three distinct groups of final customers, all ofthem with different needs and consumptionlocations.

Currently, the transport is performed by twostackers and the management is done with a veryhigh slack coefficient; in other words, for safetyreasons, the amounts of pallets with rubber movedare superior to the demand. This way offunctioning implies two extreme situations: largeamounts of material for the final customers, or, incase of management failure, the customer makesan order for a determined article directly to thestacker collaborator. The simulation model shouldallow elaboration of the best solution to visit everycustomer fulfilling the requirements / needs /priorities of each one of them, appealing to the

minimum number of AGVs in the solution.

The image presented at Figure 1 allows theidentification of the complexity of the model,particularly the resources to model, and the needs

and priorities to consider. It is necessary todetermine the best route to define each AGV,considering the exceptions associated, such asthe production failures of the internal suppliers ormalfunctions of the automatic storage system. Theobstacles (collaborators, AGVs, existingmachines, etc.) are another important aspect inthe route elaboration. The distances betweensuppliers to the warehouse to the customers varybetween a minimum of 60 meters and a maximumof 450 meters.

The model developed will realise the transportof pallets with rubber at a FIFO methodology, and

should readily obtain, among other factors, theoptimum amount of pallets with rubber for eachinternal customer, guaranteeing the success of theproduction process, but also releasing themaximum space for each customer. This projectpresents advantages on the transport optimisation,the exploitation of resources, man power reductionand productive planning.

AGVS – AUTOMATED GUIDED VEHICLES

AGVs are modern equipment for

handling/transporting materials [1]. At a technicallevel, they are characterized by autonomousmovement with batteries that allow periods offunctioning up to 24 hours, reducing the possibleminimum times loading batteries.

The AGVs are controlled by a computer andfollow established routes through physical linesmarked on the pavement (conducting wire orcolorful line) in regular intervals or virtually byradio or laser control.

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Fig. 1. Generic representation of the model to simulate

Regarding safety, the AGVs are equipped with

ultrasound and optic sensors to prevent collisions with obstacles that can appear, such ascollaborators or other AGVs. The navigationsystem follows the magnetic field generated byconductors implanted in the ground and coveredby a sinusoidal electric chain. The magnetic field isdetected by two antennas. This navigation systemis technically known as wire-guided. Thedisadvantages of the wire-guided and opticalguided systems are the difficulties of installationand movement limitations. These restrictionsstimulated the development of techniques without wires (wireless solutions) such as the laser

triangulation, matrix of references, or orientationby gyroscope. In this way, the available options toguide an AGV are the following: guiding systemsby inertia, guiding systems by laser, wire-guidedsystem, guiding systems by magnetic ribbon.

According to the Material Handling Institute ofAmerica (1993) the main benefits of the use ofAGVs are the cost savings with man power,greater flexibility in the materials handling andtransport, better organization of programming WIP(Work In Process), better use of the availablespace, bigger safety of the systems, increase of

the production, and more efficient control ofinventories.

Another important aspect to mention is thegrowth of the market in small systems, i.e.,

projects that do not have more than three to fourAGVs, in comparison to the market of great andcomplex systems which use fifty or more AGVs.This situation is due to the maturity of theequipment and technological advances that allowsuccessful solutions that are economically moreflexible and attractive.

According to the Material Handling Industry ofAmerica, 1,144 AGV systems have been installedsince 1990, resulting in a total of 6,127 vehicles; in2005, there was an increase of 20 percent in theinvestment in AGVs in North America.

THE SIMULATIONWith the development of computers and

software, the simulation is seen today as one ofthe most powerful tools in the analysis of theviability of models that support real projects. Sincethe 1980s, simulation has occupied a prominenceplace among operational research tools.

From the analysis phase of the problem anddefinition of requirements until the conclusion ofthe project, the simulation can make the differencein the elaboration of a project into the most diverseareas of application, with prominence in

production systems, including this researchproject. The growth in the use of simulation in theresolution of large-scale problems only became

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possible because of the great availability of theexisting computational resources.

Pegden [2] defines simulation as the processto project a computational model of a real systemcapable of leading experiences with the intentionof understanding its behaviour, allowing theevaluation of strategies for its operation.

It is important understand what systems andmodels are necessary to know the simulation’scoverage. The first applications of simulation weredeveloped in formal programming languages, asFORTAN [3]. These simulations demanded anenormous effort of modelling, which turned the useof simulation into an impractical use.

The first specific languages for simulationappeared around 1960. These languages supplied

the user with a set of facilities to transform theformal model of the system into a computerprogram, and made available functions androutines destined to sampling, statistics analysisand control of the advance of the time insimulation. Although there is a simplification of theprogramming, the flexibility and the computationefficiency are partially sacrificed. Moreover, themaintenance cost tends to be higher, mainlybecause of the low availability of qualified staff, asa consequence of the reduced diffusion of theselanguages. Built into this scheme were languagessuch as GPSS (General Purpose Simulation

System), GASP (Graph Algorithm and PackageSoftware) and SIMULA.

Although these languages have been thesolution to the problem for a long time, the biggercomplexity of the systems allied to the requirementof showing the production people the real benefitsof this tool compelled the evolution of the softwareto include animations, which are basically softwarethat run simultaneously with the simulators andhave the capacity to graphically reproduce thesystems and the models. From thesetechnological advances appeared software suchas SIMAN / CINEMA and GPSS /H. At this point it

becomes obvious that the users of the simulationmodels were the analysts themselves. This factmakes possible the development of the simulationapplication VIS (Visual Interactive Simulation).This technology is based on the modelling of iconsthat gather commands from the traditionallanguages of simulation and transform thedevelopment work into an easier task with aninterface similar to Windows. In this kind ofsoftware, we have references such as ARENA,PROMODEL and AUTOMOD.

THE AUTOMATICALLY GUIDED VEHICLES

SIMULATION

A system of AGVs is defined as an advancedmaterial handling system through autonomous

vehicles guided by a virtual path and controlled bya computer. In opposition to conventionalmaterials handling systems, the AGV systems arecapable of defining for themselves a better routeor way to reach their destination. The forwardingcommands, such as material to load, origin, anddestination arrive at the AGV through a computer.These systems are known by their raised flexibilityin material handling systems in the most diverseareas of application, particularly in FlexibilityManagement Systems (FMS) and FlexibilityAssembly Systems (FAS).

The project to model the system and thecorrespondent control are two of the points ofinterest and development of the AGV systems.The first one is basically related to the disposal ofthe methods to follow and the amount of vehiclesto use, whereas the control of the system isrelated to subjects connected to the decisions ofthe routes to take, forwarding orders and control.The success of an AGV system is strongly relatedand dependent of the quality of the projectedsystem and of the type of control used. It isnecessary to consider a great variety of factors when projecting an AGV system, such as the

number of AGVs to use, net of paths to configure,type of control to use, rules of forwarding (types ofload, load orders, destinations), definition of theroutes between the origin and destination, and theinterface with other material handling systems.

Partially due to the great complexity anddimension of the AGV systems, the traditionaltechniques of analysis such as analytical tools arenot enough for the project, control, and systemevaluation. These reasons stimulated thedevelopment of the tools associated to thesimulation. The growth and popularity of thesimulation is due to the development of dedicated

tools such as ARENA, and to the competitionbetween diverse software suppliers. One of thegreat intentions of the simulation tools, includingARENA, is to transform the model creationprocess into a simple and fast task.

CRITICAL ANALYSIS OF THE STATE OF

THE ART

The research of the state of the art technologyrelated to the use of the simulation methodology inthe pallets transporting projects where the key

solution is based on the use of AGVs has been thesubject of study and scientific discussion since themid-1950s.

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This analysis has as its main goals thedemonstration of the foundations of methodology,the evaluation of the strengths and weaknesses ofthe previous research, demonstrating that the levelof knowledge in this area is up-to-date and include

the most important research theories in the area.

On the basis of this critical analysis, weidentify two tools used to model systems thatappeal to the use of AGVs—the set of analyticaltools and simulation tools. The first ones aremathematical techniques such as integerprogramming, heuristic algorithms or Markovchains. As for the simulation tools, it is anapproach to the most popular systems on themarket, through the presentation of case studiesas references in the field of simulation of AGVs; inmany cases they were the basis for the

development of the currently available systems,such as ARENA, Promodel, and Automod.

Analytical Tools

Tanchoco et al. [4] compared theeffectiveness of the analytical model based on thetheory of queues for the analysis of work flow in abusiness/manufacturing system, called CAN-Q, with a simulation for the same situation usingAGVSim software [5]. As a result, the analyticaltool, CAN-Q, underestimated the number ofvehicles driven automatically required for the

solution. However, the results obtained throughthe CAN-Q are a basis for the construction of asimulation model.

Mahadevan and Narendran [6] developed ananalytical model to estimate the number of AGVsto use in applications. The suggestion of theseauthors was to begin the processes with ananalytical methods rough-cut, followed by the useof sophisticated mathematical models and onlythen use the simulation in case of high complexityof the AGV system. As the system parts wereincreasing, the problem became complicated in a

way that the analysis by the simulation modelsbecame essential to achieve the solution for theproblem.

In this way, the analytical techniques mayhave shortcomings when applied in actual casesin the industry and can give inaccurate estimates when used in random environments. Inconclusion, the analytical techniques should beunderstood as a good way to a first approach fordrawing a solution of AGVs systems and obtainingthe initial estimates [7].

Recently, Koo and Jang [8] presented a

stochastic model to determine the time of travel ofthe vehicle to solve the transport of materials inthe manufacturing industry. This model shows thetime of transport of empty loads. The model

created was the basis for the creation of the modelto simulate. The joint solution, an analyticaltool/simulation, was formed by a simulation model with the capability to evaluate the performance ofthe AGV system and by an algorithm that

minimizes the number of repetitions of the modelin the search for an optimal solution.

The increased complexity of the problemsmodelling, as well as their physical dimension, when associated with the advances of computingpower (processing and graphic animation times)led to the decrease of solutions based onanalytical tools, through an initial phase in whichintegrated simulation models appeared, to asecond phase used as the basis for specificstudies of the model on which the simulation willnot get very accurate results.

Tools and Simulation Methodologies

The software available for simulating AGVsystems can be grouped into three groups [9]:

• Simulation languages of common use (e.g.,SLAM II, SIMAN IV);

• Simulation packages specific to manufacturingsystems (e.g., SIMPLE++, AutoMod II,ProModel, Arena);

• Simulation software created specifically for

AGV systems analysis, based on the use ofgeneral programming languages such as the Cprogramming language, FORTRAN, BASIC,among others, (e.g., AGVSim, SattControl orMATSIM).

NEXT STEPS OF THE RESEARCH

The new system should be able to ensure thesupply of pallets with rubber to each internalcustomer without causing any stops in theproduction process. For the definition and study ofthe model it will be necessary to address the

following aspects:

• Collect as much information as possible tocharacterize the problem;

• Set the inputs of the model;

• Identify the existing variables and understandthe relation between them;

• Set priorities and exceptions;

• Structure the problem;

• Investigate equivalent practical cases

developed to respond to problems with thesame base, that allow discovery of options tobe taken;

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• Choose the software to use in the developmentof the model.

The choice of the software most appropriate tothe reality of the project, by study and comparison

to other "case studies" addressed in the criticalanalysis of the most advanced technologyconverges on ARENA software. The skills inherentin it, including the graphic and animation ability,ease of programming, verification and disposal /detection of errors tools (debugging tools) and thededicated reports, respond well to the needs of theproject.

CONCLUSIONS AND

RECOMMENDATIONS

The AGV systems are particularly useful

handling materials in manufacturing systems. Thesimulation is used frequently to assess theperformance of existing systems or AGV projectsin the initial phase of the feasibility study.Achieving a simulator flexible enough to modelspecific or general systems is, in our view, thedevelopment area to explore in the future of thesimulation tools systems designed to AGVssystems.

Another area under development is related tothe methods to guide and control AGVs, especially with the development of GPS applications

dedicated to the control and placement of AGVs.The ease of implementing strategies, test layoutsand control resources to raise the problemsinherent to the control of the built model, based onassumptions of a full assessment of all therestrictions attached, shows the importance ofsimulation studies of specifications for AGVprojects in the manufacturing industry.

References

[1] Hammond, L. (1986) AGVs at work, IFSPublications Ltd., UK.

[2] Pegden, C.D., Introduction to Simulation UsingSiman. 1991, McGraw-Hill.

[3] Paiva, F., Geração Automática de Modelos deSimulação de uma Linha de Produção na IndústriaTêxtil. 2005, MSc Thesis, Universidade do Minho.

[4] Tanchoco, J.M.A, Egbelu, P.J., and Taghaboni, F.,Determination of the total number of vehicles in an AGV-based material transport system. MaterialFlow 1987. 4: p. 33-51.

[5] Egbelu, P.J. and Tanchoco, J.M.A, AGVSim User´sManual. 1982, Technical Report Nº 8204.Department of Industrial Engineering andOperations Research. Virginia Polytechnic andState University, Blacksburg, VA.

[6] Mahadevan, B. and Narendran, T.T., Estimation ofnumber of AGVS for an FMS: an analytical model .International Journal of Production Research, 1993.31: p. 1655-1670.

[7] Egbelu, P.J., The use of non-Simulationapproaches in estimating vehicles requirement inan AGV based transport system. Material Flow,1987. 4: p. 17-32.

[8] Koo, P.H. and Jang, J., Vehicle travel time modelsfor AGVs systems under various dispatching rules.International Journal of Flexible ManufacturingSystems, 2002. 14: p. 249-261.

[9] Tanchoco, J.M.A., Material Flow Systems inManufacturing. 1994, Chapman & Hall.

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