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by: Ms. Dorian Wiegand


Ms. Dorian Wiegand
GPA 3.66


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Class Notes
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This 6 page Class Notes was uploaded by Ms. Dorian Wiegand on Friday September 18, 2015. The Class Notes belongs to ESI 4523 at University of Florida taught by Staff in Fall. Since its upload, it has received 18 views. For similar materials see /class/206805/esi-4523-university-of-florida in Industrial Engineering at University of Florida.

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Popular in Industrial Engineering




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Date Created: 09/18/15
Simulation ls What Is Slmulatlon Simulation very broad term methods and applications to imitate or mimic real systems usually via computer Chapte Applies in many fields and industries Very popular and powerful method By Lian Qi This course covers simulation in general and the Arena simulation software in particular Chaptell rvvnal ls slnulalum Slldel um Chaplell rWhat ls slnulalum Sllde 2 mm Systems Work With the System System faciity or process actual or planned big orsmall study the system measure improve design Examples abound Manmactunng laclllty contro39 Eankuperatlun Alrpurtupelatluns passengers secunty planes crews baggage 39 maybe 51 play mm the adual sygem TrarlspunatlDnluglstlEsdlstrlbu lun upelatlurl Adyantage 7 unquestionably loolltlrlg at the rlgnt tnlrlg Hespltal lacllltles emergency ruum uperatlng mum agmlssluns a umpmernetwulk But it s o en impossible to do so in reality with the actual Freeway system Euslness process lrlsurarlce emce sygem cnmlnallustlcesystem 7 Sygtem doegm emt cnemleal plant Fastmuu restaurant 7 Would be dlsl uptlve expenslve or dangerous Supermarket Tneme park Emergencyrrespurlse system Chaptell rWhal ls Slmulatlul Sllde 3 ml 22 Chaplell rWhat ls Slmulatlul Sllde 6 ml 22 Chapt 1 Handout 1 Models Types of Models 39 Model set of assumptionsapproximations about how the system works Physical iconic models Flight simulators Study the model instead ofthe real system usually much eaSierr faster Cheaper safer 39 Logical mathematical models Can try wideranging ideas with the model Approximations and assumptions about a systems 7 Make your mistakes on the computer where they don t count rather Operation than f r real Where they do mum Often represented via computer program in appropriate Model validity any kind of model notjust simulation SOftWare Care in building to mimicreamyfaithfuny Exercise the program to try things get results learn about Level ordeta model behaVIor 7 Get same conclusions from the model as you would from system t 339 ed Chapter 4 Slide 5 of 22 h t 339rl ed Chapter 4 Slide 6 of 22 Studying Logical Models By Simulation If mOde39 i5 Simple enf ugh use traditional Can be used to study simple models but should mathematical analySIs get exact results lots of insight into model not use It If an analytical solution Is available Queueing theory Real power of simulation is in studying complex Linearnonlinear programming But complex systems can seldom be validly m dels represented by a simple analytic model Simulation can tolerate complex models since we often a complex System requires a complex don t even aspire to an analytical solution model and analytical methods don t apply what to do Simualiun wrm Arena 3w ed Chapter 4 Slide 7 or a 39 Simulaliun wrm Arena 3m ed Chapter 4 Slide 8 or a Chapt 1 Handout 2 Computer Simulation 39 Broadly interpreted computer simulation refers to methods for studying a wide variety of models of systems Numerically evaluate on a computer Use software to imitate the system s operations and characteristics often over time Key points in Simulation v t t 339 ed Chapter 1 Sllde 9 of 22 Know how to use Arena Remember the purpose of simulation Understand clearly about the system to be imitated Make sure model s validity Know how to play the model finding the bottlenecks potential critical improvements 339rl ed Chapter 1 Sllde10 of 22 Popularity of Simulation 39 Consistently ranked as the most useful popular tool in the broader area of operations research management science 1979 Survey 137 large firms which methods used 1 Statistical analysis 93 used it 2 Simulation 84 3 Followed by LP PERT0PM inventory theory NLP 1980 AE OR division members 7 First in utility and interest simulation 7 First in familiarity LP simulation was second 1983 1989 1993 Longitudinal study of corporate practice 1 Statistical analysis 2 Simulation Advantages of Simulation Simulallun wrm Arena 3n1 ed Chapter 4 Slide 11 org Flexibility to model things as they are even if messy and complicated Allows uncertainty nonstationarity in modeling Advances in simulation software The only thing that s for sure nothing is for sure Danger of ignoring system variability Model validity Far easier to use GUls No longer as restrictive in modeling constructs hierarchical down 0 C Statistical design amp analysis capabilities 39 Simulallun wrm Arena 3m ed Chapter 4 Slide12 eta Chapt 1 Handout 3 The Bad News Different Kinds of Simulation Don t get exact answers only approximations Static vs Dynamic eS mates Does time have a role in the model Also true of many other modern methods Continuouschange vs Discrete change Can the state change continuously or only at discrete Get random output RIRO from stochastic points in ms simulations 0 Can bound errors by machine roundoff Deterministic vs Stochastic Statistical design analysis of simulation experiments is everything for sure or is here uncertainty Exploit noise control replicability sequential sampling variancereduction techniques M St operational mOdeIS Dynamic Discretechange Stochastic Chapteil rWhal ls swulalmm Slide 13 um chamw rWhat ls Simulalmm snag m m 22 Simulation by Hand 7 The Buffon Needle Problem Why Toss Needles39 Buffon needle problem seems silly now but it has Ill important simulation features Experiment to estimate something hard to compute exactly in 1733 Estimate 1 George Louis Leclerc c 1733 Toss needle of length Ionto table with stripes d gtI apart 2 Pneedle crosses a line E Randomness so estimate will not be exact estimate the error in the estimate Replication the more the better to reduce error Sequential sampling to control error keep tossing until Repeat tallyp proportion of times a line is probable error in estimate is small enoughquot crosse Variance reduction Buffon Cross Estimate 1 by 2 Chapteil rWhal ls swulalmm Slide 15 um chamw rWhat ls Simulalmm snug we at 22 Chapt 1 Handout 4 Using Computers to Simulate Where Arena Fits In Generalpurpose languages FORTRAN Tedious lowlevel errorpro 39 Hierarchical structure ultlp e levels of modelmg But almost complete exibility 5 togetherm the Sam mo el s39mulatlon languages otteh start hlgh then go lower GPSS SIMSCRIPT SLAM SIMAN on which Arena is Needed sed and is included in Arena 39 Get easeofuse advantage Popular still in use Of s39muat rs wquotth Highlevel simulators Very easy graphical interface Domainrestricted manufacturing communications Limited exibility model validity Chaptell rWhat lsswulatlum Sllde 17 Hill Chaptell rWhal ls almulatlum SlldelBulZZ When Simulations are Used When Simulations are Used cont d Uses of simulation have evolved with hardware software The formative years 1970searly 1980s Computers got faster cheaper The early years 1950s1960s Very expensive specialized tool to use Required big computers special training Mostly in FORTRAN or even Assembler Value of simulation more widely recognized Simulation soltware improved but they were still languages to be learned typed batch proce sed O en used to clean up disasters in auto aerospace industries Processing cost as hlgh as 1000hourfor a sub286 level e al plal lt heavy demahd forcertalrl model machlne e Llrle underpel formll lg e Slmulate problem ldehtme d 7 But demand had dl led up 7 Slmulatlol l Was too late Chaptell rWhat lsswulatlum Sllde la ulZZ Chaptell rWhal ls almulatlum Sllde 2 ml 22 Chapt 1 Handout 5 When Simulations are Used mm The recent past late 1980519905 Microcomputer power Software expanded into GUls animation Wider acceptance across more areas 7 Traditional manufacturing applications 7 Services 7 Health care 7 Business processes Still mostly in large firms When Simulations are Used mm t 339 ed Chapter 4 Slide 21 of 22 39 The present Proliferating into smaller firms Becoming a standard tool Being used earlier in design phase Realtime control The future Exploiting interoperability of operating systems Specialized templates for industries firms Automated statistical design analysis Networked sharing of data in real time Integration with other applications Distributed model building execution 339rl ed Chapter 4 Slide 22 of 22 Chapt 1 Handout 6


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