simulation chapter simulation chapter 1 info

Chapter 1 – What Is Simulation
Simulation with Arena, 3rd
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Chapter 1
What is
Simulation?
Last revision June 7, 2003

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Simulation Is …
• Simulation – very broad term – methods and
applications to imitate or mimic real systems,
usually via computer
• Applies in many fields and industries
• Very popular and powerful method
• Book covers simulation in general and the Arena
simulation software in particular
• This chapter – general ideas, terminology,
examples of applications, good/bad things, kinds
of simulation, software options, how/when
simulation is used

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Systems
• System – facility or process, actual or planned
 Examples abound …
– Manufacturing facility
– Bank operation
– Airport operations (passengers, security, planes, crews, baggage)
– Transportation/logistics/distribution operation
– Hospital facilities (emergency room, operating room, admissions)
– Computer network
– Freeway system
– Business process (insurance office)
– Criminal justice system
– Chemical plant
– Fast-food restaurant
– Supermarket
– Theme park
– Emergency-response system

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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

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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 for 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 in Chapter 13

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Types of Models
• Physical (iconic) models
 Tabletop material-handling models
 Mock-ups of fast-food restaurants
 Flight simulators
• Logical (mathematical) models
 Approximations and assumptions about a system’s
operation
 Often represented via computer program in appropriate
software
 Exercise the program to try things, get results, learn about
model behavior

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Studying Logical Models
• If model is simple enough, use traditional
mathematical analysis … get exact results, lots of
insight into model
 Queueing theory
 Differential equations
 Linear programming
• But complex systems can seldom be validly
represented by a simple analytic model
 Danger of over-simplifying assumptions … model validity?
 Type III error – working on the wrong problem
• Often, a complex system requires a complex
model, and analytical methods don’t apply …
what to do?

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Computer Simulation
• 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
• 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

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Popularity of Simulation
• Consistently ranked as the most useful, popular
tool in the broader area of operations research /
management science
 1978: M.S. graduates of CWRU O.R. Department … after
graduation
1. Statistical analysis
2. Forecasting
3. Systems Analysis
4. Information 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, …

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Popularity of Simulation (cont’d.)
 1980: (A)IIE O.R. division members
– First in utility and 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

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Advantages of Simulation
• Flexibility to model things as they are (even if
messy and complicated)
 Avoid looking where the light is (a morality play):
• Allows uncertainty, nonstationarity in modeling
 The only thing that’s for sure: nothing is for sure
 Danger of ignoring system variability
 Model validity
You’re walking along in the dark and see someone on hands and knees
searching the ground under a street light.
You: “What’s wrong? Can I help you?”
Other person: “I dropped my car keys and 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.”

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Advantages of Simulation (cont’d.)
• Advances in computing/cost ratios
 Estimated that 75% of computing power is used for 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 (hierarchical,
down to C)
 Statistical design & analysis capabilities

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The Bad News
• Don’t get exact answers, only approximations,
estimates
 Also true of many other modern methods
 Can bound errors by machine roundoff
• Get random output (RIRO) from stochastic
simulations
 Statistical design, analysis of simulation experiments
 Exploit: noise control, replicability, sequential sampling,
variance-reduction techniques
 Catch: “standard” statistical methods seldom work

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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 for sure or is there uncertainty?
• Most operational models:
 Dynamic, Discrete-change, Stochastic
– Though Chapter 11 discusses continuous and combined discrete-
continuous models

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Simulation by Hand:
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

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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)
 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
probable error in estimate is “small enough”
 Variance reduction (Buffon Cross)

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Using Computers to Simulate
• General-purpose languages (FORTRAN)
 Tedious, low-level, error-prone
 But, almost complete flexibility
• Support packages
 Subroutines for list processing, bookkeeping, time advance
 Widely distributed, widely modified
• Spreadsheets
 Usually static models
 Financial scenarios, distribution sampling, SQC

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Using Computers to Simulate (cont’d.)
• Simulation languages
 GPSS, SIMSCRIPT, SLAM, SIMAN (on which Arena is
based, and is included in Arena)
 Popular, still in use
 Learning curve for features, effective use, syntax
• High-level simulators
 Very easy, graphical interface
 Domain-restricted (manufacturing, communications)
 Limited flexibility — model validity?

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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 for a sub-286 level
machine

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When Simulations are Used (cont’d.)
• The formative 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 demand for certain model
– Line underperforming
– Simulated, problem identified
– But demand had dried up — simulation was too late

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• The recent past (late 1980s-1990s)
 Microcomputer power
 Software expanded into GUIs, animation
 Wider acceptance across more areas
– Traditional manufacturing applications
– Services
– Health care
– “Business processes”
 Still mostly in large firms
 Often a simulation is part of the “specs”

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• The present
 Proliferating into smaller firms
 Becoming a standard tool
 Being used earlier in design phase
 Real-time 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

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