Overview Of Systems Simulation Introduction Uses Popularity Of

overview of systems simulation introduction ============ uses, popularity of simulation ------------------------------ one of

Overview of Systems Simulation
Introduction
============
Uses, Popularity of Simulation
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One of the most popular, widely used of all scientific Techniques
Historical Roadblocks
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Hard to write code for large, complicated models: Now have very good
software—improving all the time
Requires too much computer time: Marginal cost of computing decreases
all the time.
Many simulations (stochastic) don’t give exact “answers”—only
estimates: True, but with one is able to increase the precision of the
estimate.
Modeling
========
System: Physical facility or process, usually evolving through time it
may or may not exist. Usually want to study its performance
Model: An abstraction/simplification of the system used as a proxy
Physical (iconic), Mathematical—quantitative and logical assumptions,
Numerical and simulation.
Relative advantages of studying the model vs. system: May be
impractical or impossible to perform experiments on the real system.
Methods of Studying a System
----------------------------

Types of Models
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Useful dimensions of classification with regard to design/analysis:
Dynamic vs. Static, Stochastic vs. Deterministic, Discrete vs.
Continuous
Some examples:
Deterministic
Stochastic
No randomness
Inputs are exact, no uncertainty
One model needs only one run
Random inputs—uncertain
Inputs are from known distributions
One model needs more than one run
Static
No time element
Use fitted regression model for unobserved independent-variable
combinations
Financial scenarios
“Monte Carlo” simulation
Estimate an intractable integral
Get empirical distribution of a new test statistic for some null
hypothesis
Dynamic
Passage of time is important part of model
Differential-equation models of population growth and decay
Deterministic forecasting over time
Dynamic macroeconomic models
Queueing models representing manufacturing, computer, or
communications systems
Inventory models
Compute (exactly) desired output quantities
Can only estimate desired output quantities
Advantages and Disadvantages of Simulation
------------------------------------------
Compared to experimenting with the actual system:
 Validity uncertainty with simulation (as with all models)
 Much more flexibility in simulation to try things out
 Can control the uncontrollable in simulation
 Can study the physically impossible or non-existent
Compared to exact analytical models:
 Don’t have to make as many simplifying assumptions—get more flexible
models that can be more valid
 Don’t get simple formulas from which insight can be gained
 Don’t get exact answers—only estimates, maybe uncertain—calls for
careful design and analysis of simulation experiments (our concern)
Steps in a Simulation Model
===========================

Simulation Modeling, Input, Output, and Experiments
===================================================
Modeling
--------
The modeling process: two distinct but related activities
Structural modeling
Physical/logical relationships among components
Topology/layout of machines
Possible routings for part flows
Feedback/failure loops
Closed vs. open structure in model of a computer system
Quantitative modeling
Specific numerical/distributional assumptions composing model
How many machines at each workcenter?
Probabilities for branch points on routing decisions?
Cycle times of part type 3 on a machine in group 5 are random variates
drawn from what distribution? With what parameters?
Run model for one hour? One year? Until 5000 parts have been produced?
Building “good” simulation models:
Verification—Code (in whatever language or product) is correct
Validation—Model (as expressed in the verified code) faithfully mimics
the system to study; can use model/code as surrogate for system to
make decisions
Credibility—The valid model is accepted by decision makers; critical
for implementation success
Elements of both structural and quantitative components can become
variables (or factors) in the design of simulation experiments
Structural factors:
Try a different layout of machines
What if part-flow routings changed due to technology?
What if rework were just scrapped instead (no feedback loops)?
What if the computer system went from open (batch jobs) to closed
(interactive)?
Quantitative factors:
What if we added a machine somewhere?
What if quality improvement changed pass/fail branching probabilities?
How effective would it be to reduce cycle times on the bottleneck work
center?
How long will the model operate before becoming unduly congested?
“Machine” View of What a Simulation Does:

A Brief History
===============
1733
Buffon needle problem—estimate 
1920s, 1930s
Random-number schemes used by applied statisticians
Physical methods of generating random numbers (tables)
1940s
Manhattan project, used to estimate multiple integrals and solutions
to systems of differential equations
Electronic random-number generators (more tables)
1950s
Early language development (GPSS, SIMSCRIPT)
Recognition of simulation for complex queueing models
First algorithmic random-number generators
1960s
Early work on probabilistic/statistical methodology
Recognition of need to do analysis of simulation results
Variance reduction for static simulations
Algorithms for variate and process generation
1970s
Advances in probabilistic/statistical methodology
Variance reduction for dynamic simulation
Use of stochastic processes for rigorous output analysis
Simulation languages widened, improved (GASP, SLAM)
1980s
Continued advances in rigorous output analysis
Initialization/termination methods
Adaptation of ranking/selection methods to dynamic simulation
Languages continue to improve (SIMAN)
Implementation on microcomputers
Current
Estimating derivatives and gradients of simulated response
Optimizing simulated systems
Refined, strengthened-output analysis methods
Hybrid analytical/simulation methods
Technology transfer of probabilistic/statistical methodology to
practitioners, languages
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