What is Simulation (Chapter 1) Simulation Is Simulation – very broad term – m

What is Simulation (Chapter 1) Simulation Is Simulation – very broad term – m www.phwiki.com

What is Simulation (Chapter 1) Simulation Is Simulation – very broad term – m

Cashen, Bob, News Director has reference to this Academic Journal, PHwiki organized this Journal What is Simulation (Chapter 1) Simulation Is Simulation – very broad term – methods in addition to applications to imitate or mimic real systems, usually via computer Applies in many fields in addition to industries Very popular in addition to powerful method Book covers simulation in general in addition to the Arena simulation software in particular For Chapter 1: general ideas, terminology, examples of applications, good/bad things, kinds of simulation, software options, how/when simulation is used

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A System Materials Systems System – facility or process, actual or planned Examples abound Manufacturing facility Bank or other personal-service operation Transportation/logistics/distribution operation Hospital facilities (emergency room, operating room, admissions) Computer network Freeway system Business process (insurance office) Chemical plant Criminal justice system Fast-food restaurant Supermarket Theme park Emergency-response system Work With the System Study the system – measure, improve, design, control Maybe just play with the actual system Advantage — unquestionably looking at the right thing But it’s often impossible to do so in reality with the actual system System doesn’t exist Would be disruptive, expensive, or dangerous

Models Model – set of assumptions/approximations about how the system works Study the model instead of the real system usually much easier, faster, cheaper, safer Can try wide-ranging ideas with the model Make your mistakes on the computer where they don’t count, rather than as long as real where they do count Often, just building the model is instructive – regardless of results Model validity (any kind of model not just simulation) Care in building to mimic reality faithfully Level of detail Get same conclusions from the model as you would from system More on model validity later in the course Types of Models Physical (iconic) models Tabletop material-h in addition to ling models Mock-ups of fast-food restaurants Flight simulators Logical (mathematical) models Often represented via computer program in appropriate software Exercise the program to try things, get results, learn about model behavior Approximations in addition to assumptions about a system’s operation Schematic models Physical Models

Logical Models E = mc2 — Q0 = EOQ = 2DS/H Y = mX + b Schematic Model Studying Logical Models If model is simple enough, use traditional mathematical analysis get exact results, lots of insight into model Queuing theory Linear programming (i.e., produce mix problem) Differential equations (i.e., EOQ inventory model) But complex systems can seldom be validly represented by a simple analytic model Danger of over-simplifying assumptions model validity Project Management (i.e., PERT vs. Network Sim.) Often, a complex system requires a complex model, in addition to analytical methods don’t apply what to do

Computer Simulation Broadly interpreted, computer simulation refers to methods as long as studying a wide variety of models of systems Numerically evaluate on a computer Use software to imitate the system’s operations in addition to characteristics, often over time Can be used to study simple models but should not use it if an analytical solution is available Real power of simulation is in studying complex models Simulation can tolerate complex models since we don’t even aspire to an analytical solution Popularity of Simulation Consistently ranked as the most useful, popular tool in the broader area of operations research in addition to management science 1978: M.S. graduates of CWRU O.R. Department after graduation 1. Statistical analysis 2. Forecasting 3. Systems Analysis 4. In as long as mation systems 5. Simulation 1979: Survey 137 large firms, which methods used 1. Statistical analysis (93% used it) 2. Simulation (84%) 3. Followed by LP, PERT/CPM, inventory theory, NLP, Popularity of Simulation (cont’d.) 1980: (A)IIE O.R. division members First in utility in addition to interest — simulation First in familiarity — LP (simulation was second) 1983, 1989, 1993: Longitudinal study of corporate practice 1. Statistical analysis 2. Simulation 1989: Survey of surveys Heavy use of simulation consistently reported

Advantages of Simulation Flexibility to model things as they are (even if messy in addition to complicated) Avoid looking where the light is (a theatrical play): You’re walking along in the dark in addition to see someone on h in addition to s in addition to knees searching the ground under a street light. You: “What’s wrong Can I help you” Other person: “I dropped my car keys in addition to can’t find them.” You: “Oh, so you dropped them around here, huh” Other person: “No, I dropped them over there.” (Points into the darkness.) You: “Then why are you looking here” Other person: “Because this is where the light is.” Allows uncertainty, nonstationarity in modeling The only thing that’s as long as sure: nothing is as long as sure Danger of ignoring system variability Model validity Advantages of Simulation (cont’d.) Advances in computing/cost ratios Estimated that 75% of computing power is used as long as various kinds of simulations Dedicated machines (e.g., real-time shop-floor control) Advances in simulation software Far easier to use (GUIs) No longer as restrictive in modeling constructs Statistical design & analysis capabilities The Bad News Don’t get exact answers, only approximations, estimates Also true of many other modern methods Can bound errors via statistical methods Get r in addition to om output (RIRO) from stochastic simulations Statistical design, analysis of simulation experiments Exploit: noise control, replicability, sequential sampling, variance-reduction techniques Catch: “st in addition to ard” statistical methods seldom work

Different Kinds of Simulation Static vs. Dynamic Does time have a role in the model Continuous-change vs. Discrete-change Can the “state” change continuously or only at discrete points in time Deterministic vs. Stochastic Is everything as long as sure or is there uncertainty Most operational models: Dynamic, Discrete-change, Stochastic Simulation by H in addition to : The Buffon Needle Problem Estimate p (George Louis Leclerc, c. 1733) Toss needle of length l onto table with stripes d (>l) apart P (needle crosses a line) = Repeat; tally = proportion of times a line is crossed Estimate p by Why Toss Needles Buffon needle problem seems silly now, but it has important simulation features: Experiment to estimate something hard to compute exactly (in 1733) R in addition to omness, 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 probable error in estimate is “small enough” Variance reduction (Buffon Cross) (example is like flipping a fair coin leading to the conclusion that heads or tails is fifty-fifty (i,e., probability as long as H or T is .5)

Using Computers to Simulate General-purpose languages (FORTRAN) Tedious, low-level, error-prone But, almost complete flexibility Support packages Subroutines as long as list processing, bookkeeping, time advance Widely distributed, widely modified Spreadsheets Usually static models Financial scenarios, distribution sampling, SQC Using Computers to Simulate (cont’d.) Simulation languages GPSS, SIMSCRIPT, SLAM, SIMAN (These are names of simulation languages) Popular, still in use (Not all of them are supported) Learning curve as long as features, effective use, syntax High-level simulators Very easy, graphical interface Domain-restricted (manufacturing, communications) Limited flexibility — model validity Where Arena Fits In Hierarchical structure Multiple levels of modeling Can mix different modeling levels together in the same model Often, start high then go lower as needed Get ease-of-use advantage of simulators without sacrificing modeling flexibility

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When Simulations are Used Uses of simulation have evolved with hardware, software The early years (1950s-1960s) Very expensive, specialized tool to use Required big computers, special training Mostly in FORTRAN (or even Assembler) Processing cost as high as $1000/hour as long as a sub-286 level machine When Simulations are Used (cont’d.) The as long as mative years (1970s-early 1980s) Computers got faster, cheaper Value of simulation more widely recognized Simulation software improved, but they were still languages to be learned, typed, batch processed Often used to clean up “disasters” in auto, aerospace industries Car plant; heavy dem in addition to as long as certain model Line underper as long as ming Simulated, problem identified But dem in addition to had dried up — simulation was too late Military applications When Simulations are Used (cont’d.) The recent past (late 1980s-1990s) Microcomputer power Software exp in addition to ed into GUIs, animation Wider acceptance across more areas Traditional manufacturing applications Services Health care “Business processes” Still mostly in large firms

When Simulations are Used (cont’d.) The present Proliferating into smaller firms Becoming a st in addition to ard tool Being used earlier in design phase Real-time control (based on complexity of the model) The future Exploiting new operating systems ( as long as modeling more complex systems in addition to integrating with features such as spreadsheets, databases, in addition to word processors) Specialized “templates” as long as industries, firms Automated statistical design, analysis

Cashen, Bob News Director

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