SIM-model

MODELING AND SIMULATION -AN INTRODUCTION By, Dr.N.SELVARAJ Asst.Professor Dept. of Mechanical Engg. NIT ,Warangal.

OverviewWhat is simulation ?When simulation is appropriate toolWhen simulation is not appropriateAdvantages and Disadvantages of simulationAreas of applicationSystems and Systems environmentComponents of a systemDiscrete and continuous systemsModel of a systemTypes of modelsDiscrete-event systems simulationSteps in simulation studySimulation Software's and selection of software'sLiteratures

What is model?A model is a representation of real system or process for the purpose of studying the systemA model may incorporate logical, mathematical and structural aspects of the system or process

What is model?Model set of assumptions/approximations about how the system worksStudy the model instead of the real system usually much easier, faster, cheaper, saferCan try wide-ranging ideas with the modelMake your mistakes on the computer where they dont count, rather than for real where they do countOften, just building the model is instructive regardless of results

What is Simulation?Simulation very broad term methods and applications to imitate or mimic real systems, usually via computerApplies in many fields and industriesVery popular and powerful method

What is Simulation ?The representation of real situation in a computer by means of model so that, different conditions can be tested over period of time.It is a particular type of mathematical model of the system or representation (Imitation) of real world system or process over period of time.

System vs. Its Model Simplification Abstraction AssumptionsReal SystemModel

Model of a SystemA model is defined as a representation of a system for the purpose of studying the system.Types of models : Mathematical or physical.A mathematical model uses symbolic notation and mathematical equations to represent a system. A simulation model is a particular type of mathematical model of a system.

Model ClassificationPhysical (prototypes) Analytical (mathematical)Computer (Monte Carlo Simulation)Descriptive (performance analysis)

Prescriptive (optimization)

Discrete and Continuous SystemsDiscrete systems: State variables change only at a discrete set of points in time. Example : bank: the number of customers change when a customer enters or leaves the system

Continuous system: State variables change continuously over time. Example: water level in the dam and body temperaturewater

Physical (Prototypes)

Analytical (Mathematical)Single Stage Queuing Model

Descriptive (Performance analysis)Simulationvs.Real World

Simulation Models

Simulation models: Static or dynamic, Deterministic or stochastic, Discrete or continuous.Static or Monte Carlo simulation represents a system at a particular point in time. Dynamic models: represent systems as they change over time.Deterministic simulation: Known sets of inputs and a unique sets of outputs. If all patients arrive at an appointed time contd

Simulation ModelsStochastic simulation: has one or more random variables as inputs. Random inputs lead to random outputs. Bank: random inter-arrival times and random service times.Output: average number of people waiting, average waiting time of a customer. Discrete-event simulation: Modeling of systems in which the state variables changes at a discrete set of points in time .

Steps in Simulation studyProblem formulation: Statement of the problem. The problem should be clearly understood. Setting of objectives and overall project plan: The objectives indicate the questions to be answered by the simulation. Determination should be made whether the simulation is appropriate methodology or not.

Steps in Simulation studyModel conceptualization: It is an art.Abstract the essential features of the problemSelect and modify basic assumptions that characterize the systemEnrich and elaborate the model until a useful approximation results.It is best to start a simple model and build toward greater complexity.Not necessary to have one-one mapping with real system.Only the essence of the real system is needed.Advisable to involve model userHowever, only experience can teach the model building.contd..

Steps in Simulation studyData collection:Different kinds of data should be identified and collected while building a model.Model translation: The model can be translated into program.Verified: Is the computer program performing correctly ?With complex models it is difficult. Validation: determination that a model is an accurate representation of the real system contd..

Experimental design: Length of the initialization periodThe length of simulation runsThe number of replications to be made for each runDocumentation and ReportingTwo types of documentation: program and progress.Reporting frequent deliverables. Implementation: Completion of previous steps

PERFORMANCE TO BE MEASURESThroughputCycle timeResources utilizationsWIPStaff requirementsQueueing delaysBottleneckEffectiveness of schedulingEffectiveness of control system

When Simulation is appropriate toolSimulation enables the study of internal reactions of a complex systemInformational, organizational, and environmental changes can be simulated.To improve the system performanceImportant variables that affect the system can be identifiedTo experiment with new designs or policies prior to implementation, so as to prepare for what may happen. The modern system is so complex that the interactions can be treated only through simulation.

When simulation is not appropriateIf the problem can be solved with common sense. Average arrival rate 100/hour and service rate is 12/hour, then the number of servers 100/12=8.33. Which means 9 or more servers are needed.If the problem can be solved analytically.If it is easier to perform experimentsIf costs exceed savingsIf the resources or time is not available.If data or estimates are not available Ability to verify the modelIf managers have unreasonable expectationsIf the system is too complex.

Advantages New policies, operating procedures, decision rules, information flows, or organizational procedures, and so on can be explored without disrupting ongoing operations of the real systemNew hardware designs, physical layouts, transportation systems, and so on, without committing resources for their acquisition.Hypotheses about how or why certain phenomena occur can be tested for feasibility.Insight can be obtained about the interaction of variables

contd..

AdvantagesInsight can be obtained about the importance of variables to the performance of the systemBottleneck analysis can be performed where work in progress , information, materials and so on are being delayed.A simulation study can help understanding how the system operates rather than how individuals think the system operates.What-if questions can be asked. This is useful in the design of new systems.

Areas of ApplicationsManufacturing applicationsConstruction EngineeringMilitary applicationsLogistics, transportation, and distributed applicationsBusiness process simulationHuman Systems

Manufacturing ApplicationsAnalysis of electronics assembly operationsDesign and evaluation of a selective assembly static for high-precision scroll compressor bellsComparison of dispatching rules for semiconductor manufacturing using large-facility modelsEvaluation of cluster tool throughput for thin-film head productionDetermining optimal lot size for a semiconductor back-end factorycontd..

Manufacturing ApplicationsOptimization of cycle timer and utilization in semiconductor test manufacturingAnalysis of storage and retrieval strategies in a warehouseInvestigation of dynamics in a service oriented supply chainModel for an Army chemical munitions disposal facility

Human Systems Modeling human performance in complex systemsStudying the human element in air traffic control.

Systems and System EnvironmentA System is defined as a group of objects that are joined together in some regular interaction or interdependence toward the accomplishment of common purpose Example: Production system manufacturing automobiles. The machines, component parts and workers operate jointly along an assembly line to produce a high-quality vehicle. Environment: A system effected by changes occurring outside the system. Such changes are said to occur in the system environment.There is a boundary between the system and environment.

Components of a SystemEntity: object of interests in the systemAttribute: property of an entityActivity: time period of specified lengthExample: bank: Customers might be one of the entities, balance might be an attributes, and making deposit is an activity.

contd..

Components of a SystemState: Collation of variables necessary to describe the system at any time relative to the objectives of the study.Bank: # of busy tellers, # of customers waiting in the queue, arrival time of the next customerEvent: It is defined as an instantaneous occurrence that may change the state of the system.Depending on purpose, the number of components (entities, attributes, activities, states, events) varies.

Example of Systems and Components

Modelling ApproachEvent SchedulingSystem modelled via characteristic eventsEvents have subroutines which update state variables.Process OrientationTime oriented sequence of inter-related events that describes the experience of an entity as it flows through a system.Overlay to an event scheduling system.Approach adopted in most current software.

*SIMULATION SOFTWARE Table 2 : Commercial Simulation Language

1st Category 2nd Category 3rd Category Webbased simulation Channel purpose language Simulation language Simulation Packages FORTRANC, C + +VB, VB+ + . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .many other oriented languagesGPSS (1965)SIMSCRIPT (1963)SIMULAGASP (1961)ALGOLSLAM (1979)SIMAN GPSS/4 (1977)SLAM IIAWESIM (1995)GEMSARENA (1993)AutoMODQUEST EXTEND PROMODEL TaylorED WITNESS. . . . . . . . . . .and many moreJAVASIMWEB-BASED SIMULATION. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .

Simulation SoftwaresGeneral purpose simulation packages:Arena,Extend, Awesim, Symix, GPSS, Micro saint, Modsim, Simple+, Taylor etc..

Simulation Software'sApplication Oriented simulation packagesManufacturing:Automod, Promodel, Quest, Flexsim, Extend, Auto shed, Taylor, etc..Communications Networks:COMNET III, OPNET Modeler, IT Decision guru etc..Process Reengineering:Process model, SIMPROCESS, Extend BPR etc..

Simulation LanguagesGeneral in natureCan model almost any type of system.Frequently include specific modelling constructs (such as material handling systems).Steep learning curve.Significant modelling and programming expertise is necessary.Long(ish) development cycles.

Common FeaturesGenerating random numbers (i.e. ~U(0,1))Generating random variates from a specified probability distribution. Advancing the simulation clock.Determining the next event on the list event and passing control of to the appropriate piece of code.Adding and deleting records from a list.Collecting output statistics and reporting the results of the simulation run.Trapping error conditions.

GPSS CodeAssume an M/M/1system.Interarrival time = 2.0 minutes (exponential)Service time = 1.0 minutes (exponential).Assume an infinite queue.*Simulation of M/M/1 systemSIMULATEGENERATE RVEXPO(1, 2.0)QUEUESERVQSEIZESERVERADVANCERVEXPO(2, 1.0)RELEASESERVERTERMINATE1**CONTROL STATEMENTS*START 1000END

SLAMIBM stopped support and development of GPSS about 1972.A market developed for alternative software that could run on newer machines (VAX & UNIX).In 1979, Alan Pritsker and David Pegden create SLAM (Simulation Language for Alternative Modeling).In the early 80s Pritsker and Pegden develop SLAMII, which ran on engineering workstations.A feature of this new language is a graphical model builder. Users enter their model as a network diagram. When complete, the network is translated into SLAM code.

SLAM-II Code or AWESIMOriginally one of the slowest components of a SLAM model was compiling.Compiling really translates the model into a set of FORTRAN subroutines.To speed up compiles, controls were separated from the main body of the model. Controls were designed to be short and changeable, while models were to be big and relatively fixed.1 RESOURCE,,SERVER,1,{1}; 2 CREATE,EXPON(2,1),0.0,,INF,1; 3 ACTIVITY; 4 AWAIT,1,{{SERVER,1}},ALL,,NONE,1; 5 ACTIVITY,1,EXPON(1,1); 6 FREE,{{SERVER,1}},1; 7 ACTIVITY; 8 TERMINATE,INF;; 1 GEN; 2 LIMITS; 3 INITIALIZE,0.0,1000,YES,,NO; 4 NET; 5 FIN;

Awesim

SIMANAbout 1983 or so, Dennis Pegden develops his own simulation language.SIMAN (SIMulation ANalysis).The language was designed to run on a PC.It is remarkably similar in look, feel, content and style to SLAM. A lawsuit entailed. BEGIN;CREATE,,EX(2,1);QUEUE, 1;SEIZE: SERVER;DELAY: EX(1,1);RELEASE:SERVER:DISPOSE;END;

BEGIN;DISCRETE, 1000, 1, 1;RESOURCES: 1, SERVER;REPLICATE,1;END;

SIMAN AND SLAMSIMAN is tailored for the PC market.SLAM remains focused on workstations.SIMAN introduces an animation package (CINEMA) about 1985 or so.The animation is an add on unit for the model.Originally it required specialized (& expensive) hardware.SLAM responds with a PC version of SLAM in the late 1980s (which also has animation).Both firms develop software to integrate factory scheduling into simulation runs.

WITNESS

WITNESS is offered by the Lanner Group. WITNESS is strongly machine oriented and contains many elements for discrete-part manufacturing. WITNESS models are based on template elements. Elements may be combined into a designer element module to be reused

Witness (Lanner Inc)Simple building block designInteractiveFull range of logic and control optionsElements for discrete manufacture, process industries, BPR, e-commerce, call centers, health, finance and governmentStatistical input and reportsLink system to other software easily (CAD/Excel) Optional 3D/VR viewsModel Optimisation

$13,000-$17,000 ($US)

Witness (Lanner Inc)

ARENAProcess hierarchy. Integrates with Microsoft desktop toolsSpreadsheet interfaceCrystal reportsFree runtime software. Fully graphical environment. No programming required.VBA embedded.Optimization with OptQuest for Arena.Builds reusable modules.$1,000 - $17,000 ($US). Various add-in modules available.

*ARENAArena can be used for simulating discrete and continuous systems Arena employs an object based design for entirely graphical model development. Modules are organized into collections called templates.

*ARENA

GPSS/HSuccessor to the orginal simulation language (GPSS).Was freeware on IBM 360s Makes use of common program blocks.Proven, reliable software.Extremely flexible.Extensive error checking routines.Post-process animations (Proof) can be built.~$5,000 ($US)

*AutoMod

It includes the AutoMod simulation package, AutoStat for experimentation and analysis, and Auto View for making AVI movies of the built-in 3-D animation. The main focus of the AutoMod simulation product is manufacturing and material handling systems.

AutomodCombines Virtual Reality (VR) graphics with a discrete and continuous simulation environment. Manufacturing operations Material handling systems Tanks and pipe networks IC ManufacturingTransportation and logistics systems $15,000 - $100,000 ($US)

*AutoMod

Production flow

QUESTQUEST if offered by Deneb Robotics QUEST models are based on 3-D CAD geometry. A QUEST model consists of elements from a number of element classes. Built-in element classes include AGV and transporters, buffer, conveyor, labour, machine, parts and process. Each element has associated geometric data and parameters that define its behaviour

Flexsim (Ware house)

Flexsim

ProModel

ProModel is offered by ProModel Corporation It is a simulation and animation tool designed to model manufacturing systems. ProModel offers 2-D animation with an optional 3-D like perspective view.

ProModelProModel is offered by ProModel Corporation ,USA.

It is a simulation and animation tool designed to model manufacturing systems. ProModel offers 2-D animation with an optional 3-D like perspective view.

PromodelState-of-the-art simulation engine Graphical user interface Distribution-fitting.Output analysis moduleOptional optimizer.Modules designed for:ManufacturingHealthcareServices $17,000 ($US)

(Run Hours 231.57)

Chart4

2895.182742.53

2233.742157.22

2013.171962.14

1902.961864.56

1836.791806.05

1792.721766.96

1761.221739.17

1737.581718.27

GA

Heuristic

Number of Vehicles

Cycle Time

Graph - 2 : Case Study - 2

Sheet1

MachineInitial solutionImproved solutionMachineInitial solutionImproved solution

by GA ( % )by heuristic ( % )by GA ( % )by heuristic ( % )

M17.137.63M114.9716.24

M21.061.25M221.723.54

M37.588.12M339.6242.98

M411.2612.07M411.8812.88

M525.8427.69M523.8925.92

M63.934.21M620.5122.25

M74.264.56M722.8924.83

M83.283.51M811.1612.1

M91.151.23M916.9918.43

M101.561.67M1020.0421.75

M112.953.16

M121.151.23

M1300

M140.20.22

M151.641.76

M160.90.97

M1783.1489.07

M185.495.88

M1910.6511.88

M200.410.44

M210.410.44

M2247.4250.81

Sheet2

ResourceInitial solutionImproved solution

by GA ( % )by heuristic ( % )

CaseRun hours40.6837.59

study 1

forklift 131.6727.33

forklift 23025.3

CaseRun hours231.57213.45

study 2



Sheet3

12345678

1150.53147.22

2142.84141.18

3140.27139.16

4139.15138.16

5138.22137.55

6137.71137.13

7137.34136.85

8137.07136.66

12895.182742.53

22233.742157.22

32013.171962.14

41902.961864.56

51836.791806.05

61792.721766.96

71761.221739.17

81737.581718.27

Sheet3

00

00

00

00

00

00

00

00

GA

Heuristic

Number of Vehicles

Cycle Time


00

00

00

00

00

00

00

00

GA

Heuristic

Number of Vehicles

Cycle Time


from above table average process time in percentage of total scheduled hours = (39.62+14.97+20.04+11.88+11.16+21.7+16.99+22.89+23.89+20.51)/18 = 11.31%=0.1131average process time = 0.1131*231.57*60=1572.05

Assembly line (Promodel)

Production flow line

METHODOLOGY FOR SELECTION OF SIMULATION SOFTWARENeed for purchasing simulation software

Initial software survey

Evaluation

Software selection

Software contract negotiation

Software purchaseStage 1

Stage 2

Stage 3

Stage 4

Stage 5

Stage 6

Figure 3 : Stages of simulation software selection methodology

Continued in the next slide

Initial software survey

SELECTION OF SIMULATION SOFTWRAES Modeling Building featuresGraphical model buildingInput and out analysis capabilityConditional routingSpecialized components and templatesInterface with general programming

SELECTION OF SIMULATION SOFTWRAESRuntime environmentExecution speed,Model size Number of variables and attributesModel status and statistics

SELECTION OF SIMULATION SOFTWRAES Animation and layout features Type of animation, Dimensions, Movement,Quality of motion, hardware requirements

SELECTION OF SIMULATION SOFTWRAESOut put features Graphical, text, animations etc.. Vendor support and Product documentationTraining, documentation, Tutorials, easy to contact, etc..

4.MAJOR SOURCES OF ERRORS IN SIMULATIONModeling errors

Analysis errors or Programming errors

Sampling errors

Modeling ErrorsThe use of invalid models may results in serious simulation errors, and in fact this the cause for the failure of many simulation errors.A valid model is necessary prerequisite in simulation by using V & V

Analysis errors or Programming errorsCoding errorNumerical errorRandom number errorRandom variate error

Coding errorCoding error. The code wrong, in commercial software or the practitioners specification of the model. Sometimes called verification error (analogous to modeling error being called validation error).

Numerical errorNumerical error. Computer arithmetic is not real number arithmetic; computers can store only a finite set of numbers. Examples include numbers close to zero being denser than numbers close to one, floating-point comparisons being suspect because of rounding, and combinatorial calculations overflowing.

Random-number errorRandom-number error. Pseudorandom numbers are not truly random numbers. As computers become faster, sample sizes become larger, and sensitivity to random-number error increases.

Random-variate errorRandom-variate error. Methods to generate random variates are sometimes approximations. For example,the reasonably good standard normal inverse transformation x = (u0.135 (1u)0.135)/0.1975truncates at about five standard deviations from the mean.

Sampling errors

Sampling error. Monte Carlo simulation analysis is fundamentally a statistical-inference method;therefore, sampling error is unavoidable. Sampling error is typically measured by standard error, the standard deviation of the point estimator, which is often inversely proportional to the square root of the sample size.

Error control in simulationDetection and control of initial bias(warm-up period)Replication of simulation runBatch meanRegenerative method

CONCLUSIONSthe application for simulation to address manufacturing problems. Developments in the area of simulation existing softwares for discrete event simulation and conduction of simulation studies were reviewed. The necessity and importance of simulation for modeling and analyzing the various classes of manufacturing problems was focused in this paper; we hope this paper may encourage the extensive use of simulation in manufacturing and development of simulation technology for addressing the problems which need serious attention.

MANUFACTURING SIMULATIONLABORATORY

List of Experiments1. Performance evaluation of single line multi stage manufacturing system2. Performance evaluation of multi line multi stage assembly manufacturing system3. Break down analysis of manufacturing system4. Study of Just In Time manufacturing system5. Job shop scheduling problem6. Design of Flexible Manufacturing systemAll the above problems are studied with different manufacturing environment, like line balancing, unbalancing, different buffer size, stochastic etc.. Using Awesim, Promodel, Automod, Flexsim and Witness simulation softwares.

MANUFACTURING SIMULATION LABORATORYHardware:1. Pentium III (Wipro make) -----------------------1 No2. Pentium II Server (Compaq make) --------------1 No3. Intel Celeron Computer(Compaq make)--------9 Nos4. Intel Celeron Computer(DTK make)------------2 NosSoftware:1. Automod 10.0 ----------- 11 Network user licenses2. Awesim 3.0--- ----------- 11 Network user licenses3. Promodel 4.22 ----------- 11 Network user licenses4. Flexsim 3.0 --------------- 20 Network user licenses5. Witness 2.1---- ----------- 10 Network user licensesActivities:1. Lab. Classes for M.Tech (Manufacturing Engg) and M.Tech( CIM)2. Design Project works for M.Tech and B.Tech3. Research works

Literatures www.wintersim.orgwww.lionhrtpub.comInt.Jour. of Simulation and ModelingInt.Jour. of Statistical computation and simulationSimulation modeling and analysis by Law and Kelton Discrete event system simulation by Jerry banks and NelsonSystem Simulation by Jordon

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