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Simulation Modeling

- J. M. Akinpelu

What is Simulation

- Simulation is the use of a computer to evaluate a

system model numerically, in order to estimate

the desired true characteristics of the system. - Simulation is useful when a real-world system is

too complex to allow realistic models to be

evaluated analytically.

Systems

- System a collection of entities, e. g., people

or machines, that act and interact together to

accomplish some end - System state the collection of variables

necessary to describe the system at a particular

time - Types of systems
- discrete
- continuous

Types of Simulations

- discrete vs. continuous
- deterministic vs. stochastic
- static vs. dynamic

Advantages of Simulation

- Provides a way to study complex, real-world

systems that cannot be accurately described by a

mathematical model that can be evaluated

analytically. - Allows estimation of the performance of an

existing system under some projected set of

operating conditions. - Allows comparison of alternative proposed system

designs to see which one best meets a specified

requirement. - Allows study of a system with a long time frame

in compressed time, or alternatively, study of

the detailed workings of a system in expanded

time.

Disadvantages of Simulation

- Each run of a stochastic simulation model

produces only estimates of a models true

characteristics for a particular set of input

parameters. Thus several independent runs are

required for each set of input parameters to be

studied. - Simulations models are often expensive and

time-consuming to develop. - The large volume of numbers produced by a

simulation study often creates a tendency to

place greater confidence in a studys results

than is justified.

Discrete-Event Simulation

- Discrete Event System
- a system whose state changes at discrete points

in time due to the occurrence of asynchronous

events - Example M/M/1 queueing system
- State
- number of customers in system
- Events
- customer arrival
- customer departure

Discrete-Event Simulation

- Example M/M/1 queueing system
- Arrival times 0.5, 1, 2.5, 4.5
- Service times 1.5, 1.5, 0.5, 1

Customer 1 arrives and starts service

Customer 2 arrives and joins queue

Customer 1 finishes service customer 2 starts

service

Customer 3 arrives and joins queue

Customer 3 finishes service

Customer 4 arrives and starts service

Customer 2 finishes service customer 3 starts

service

Customer 4 finishes service

Discrete-Event Simulation

- Discrete-Event Simulation
- The operation of a system is represented as a

chronological sequence of events. - Each event occurs at an instant in time and marks

a change of state in the system. - Although discrete-event simulation can

conceptually be done by hand calculations, the

amount of data that must be stored and

manipulated for most real-world systems dictates

that discrete-event simulations be done on a

digital computer.

Discrete-Event SimulationComponents

- System state The collection of state variables

necessary to describe the system at a particular

time - Simulation clock A variable giving the current

value of simulated time - Event list A list containing the next time when

each type of event will occur - Statistical counters Variables used for storing

statistical information about system performance

Discrete-Event Simulation

Discrete-Event Simulation

Discrete-Event Simulation

Discrete-Event Simulation

Discrete-Event Simulation

Discrete-Event Simulation

Discrete-Event Simulation

Discrete-Event Simulation

Discrete-Event Simulation

Discrete-Event Simulation

- Average number in system 6.5/5.5 1.18
- Server utilization 4.5/5.5 0.82

Discrete-Event SimulationComponents

- Initialization routine A subprogram to

initialize the simulation model at time 0 - Timing routine A subprogram that determines the

next event from the event list and then advances

the simulation clock to the time when the next

event is to occur - Event routine A subprogram that updates the

system state when a particular type of event

occurs (there is one event routing for each type

of event) - Library routines A set of subprograms used to

generate random observations from probability

distributions that were determined as part of the

simulation model

Discrete-Event SimulationComponents

- Report generator A subprogram that computes

estimates (from the statistical counters) of the

desired measures of performance and produces a

report when the simulation ends - Main program A subprogram that invokes the

timing routing to determine the next event and

then transfers control to the corresponding event

routine to update the system state appropriately.

The main program may also check for termination

and invoke the report generator when the

simulation is over.

Discrete-Event SimulationLogical Flow

Discrete-Event SimulationStopping Rules

- Number of events of a certain type reached a

pre-defined value - Example stop M/M/1 simulation after the 1000th

departure - Simulation time reaches a certain value this is

usually implemented by scheduling a

end-simulation event at the desired simulation

stop time.

Monte Carlo Simulation

- A scheme employing random numbers which is used

to solve certain stochastic or deterministic

problems where the passage of time plays no

substantive role. - Common problem is estimation of

where f is a function, x is a vector and O is

domain of integration. - Special case Estimate for scalar

x and limits of integration a, b

Monte Carlo Simulation

- Let X be a uniform random variable on the

interval a, b with density - and let x1, , xn be a random sample from X. Then

Monte Carlo Simulation

- Example Estimate
- We approximate this by
- where x1, , xn are a sample from a uniform 0,

b random variable.

Monte Carlo Simulation

- Example Estimate
- There is considerable variability in the quality

of solution accuracy of numerical integration

sensitive to integrand and domain of integration

Monte Carlo Simulation

- Example (Ross 9.27) Consider a two-out-of-three

system of independent components, in which each

components lifetime (in months) is uniformly

distributed over (0, 1). The reliability function

is given by

Monte Carlo Simulation

- Example (Ross 9.27) cont. Use Monte Carlo to

estimate the expected lifetime given by - where Fi(t) is the lifetime distribution for

component i, i 1, 2, 3, and

Monte Carlo Simulation

- Example (Ross 9.27) cont.
- where pi is a sample from a uniform (0,1) R. V.

Sources of Randomness for Common Simulation

Applications

Random Number Generation

- Random variate1 generation plays a key role in

simulation. - The most basic step is the generation of random

variates from a uniform distribution on (0, 1)

(denoted U(0,1) and called random numbers) . - Computer-based random number generators produce

randomness from a precise algorithm initiated

using a seed. - Random variates having other specified

distributions are built from the basic U(0,1)

numbers produced with the random number generator.

1 particular outcome of a random variable

Random Number Generation

- Pseudo random number generators (PRNG) are

algorithms that can automatically create long

runs (for example, millions of numbers long) of

random numbers with good random properties but

eventually the sequence repeats exactly. - One of the most common PRNG is the linear

congruential generator, which uses the recurrence - where m (the modulus), a (the multiplier), c (the

increment), and Z0 (the seed) are suitably chosen

non-negative integers. The quantity Zn/m is taken

as an approximation to a uniform (0, 1) random

variable.

Random Number Generation

- Good PRNGs have several properties
- The numbers produced appear to be distributed

uniformly on (0, 1) and should not exhibit any

correlation with each other. - The generator is fast and avoids the need for a

lot of storage. - We are able to reproduce a given stream of random

numbers exactly. - There is a provision for easily producing

separate streams of random numbers.

Random Number Generation

- Most computer programming languages include

functions or library routines that purport to be

random number generators. Such library functions

often have poor statistical properties and some

will repeat patterns after only tens of thousands

of trials. These functions may provide enough

randomness for certain tasks but are unsuitable

where high-quality randomness is required, such

as in cryptographic applications, statistics or

numerical analysis. - Much higher quality random number sources are

available on most operating systems for example

/dev/random on various BSD flavors, Linux, Mac OS

X, IRIX, and Solaris, or CryptGenRandom for

Microsoft Windows.

Simulating Continuous Random Variables

- The Inverse Transformation Method
- The Acceptance-Rejection Method

The Inverse Transformation Method

- Proposition. Let U be a uniform (0, 1) random

variable and F be a continuous distribution

function. Then the random variable - X F1(U)
- has distribution function F.
- Proof Since F(x) is a monotone function, then

The Inverse Transformation Method

1

F

U

0

X

Uniform Distribution

- If
- where u is a U(0, 1) random variate, then
- is a U(a, b) random variate.

Exponential Distribution

- If
- where u is a U(0, 1) random variate, then
- is an exponential random variate.

Exponential Distribution

- 1000 Exponential Random Numbers ( 2)

Erlang-m Distribution

- If X is an Erlang-m random variable with mean ß,

then - where the Yis are IID exponential random

variables, each with mean ß/m. If we use the

inverse-transform method to generate the

exponential Yis, then

Erlang-m Distribution

- Hence we generate random variate x as follows
- Generate u1, u2, , um as IID U(0,1).
- Return

Acceptance-Rejection Method

- Let X have density function f (x). Let g(x) be

another density function such that there exists a

number c satisfying - To generate a random variate with density

function f (x), - Generate u from U(0,1).
- Generate a random variate y from the density

function g, independent of u. - If u f (x)/cg(x) then set x y. Otherwise, go

to step 1.

Normal Distribution

- Note that if Z has distribution N(0, 1) then its

distribution function F satisfies - Therefore, to generate a random variate z, we can

first generate a nonnegative random variate x

with density function - and then assign x a random sign (positive or

negative with equal probability) to get z.

Normal Distribution

Normal Distribution

- If we choose g(x) e-x, x gt 0, and

then - Hence, to simulate X we
- Generate u from U(0,1).
- Generate a random variate y from the density

function g, independent of u. - If u exp(y 1)2 /2, then set x y.

Otherwise, go to step 1. - We now generate z by letting z be equally x or x.

Simulating Discrete Random Variables

- Inverse Transformation Method

mapped to x3

Bernoulli Distribution

- Let X be a Bernoulli random variable with success

probability p. To generate a Bernoulli random

variate, - Generate u from U(0,1).
- If u p, then set x 1. Otherwise set x 0.

Discrete Uniform Distribution

- Let X be a discrete uniform random variable

taking values i, i 1, , j. To generate a

discrete uniform random variate, - Generate u from U(0,1).
- Set x i (j i 1) u.

Geometric Distribution

- Let X be a geometric random variable with success

probability p (and q 1 p). Note that - Setting this equal to u and solving for n yields
- To generate a geometric random variate,
- Generate u from U(0,1).
- Set x ln u / ln q.

Binomial Distribution

- Let X be a binomial random variable with

parameters n and p. Note the sum of n IID

Bernoulli(p) random variables is a Binomial(n, p)

random variable. - To generate a binomial random variate,
- Generate y1, y2, , yn as IID Bernoulli(p) random

variates. - Set x y1 y2 yn .

Poisson Distribution

- Let X be a Poisson random variable with parameter

and probability mass function p(n). Now

consider an exponential random variable Y with

rate . Note that - If Y is the interarrival distribution for some

event, then the probability that n events occur

in 0, 1 is given by - The number of events that occur in the interval

0, 1 is n if and only if the sum of the first n

interarrival times is no more than one, but the

sum of the first n 1 interarrival times is

greater than one.

Poisson Distribution

- Hence, if yi is the ith interarrival time, and ui

is the random number used to generate it, then - This implies that we should multiply independent

U(0,1) random variates until the product falls

below e.

Poisson Distribution

- To generate a Poisson random variate,
- Let a e, b 1, and n 0.
- Generate un1 U(0,1) and replace b by bun1. If

b lt a, then let x n. Otherwise go to step 3. - Replace n by n 1 and go to step 2 .

Estimation of Distribution Parameters Maximum

Likelihood Estimation

- Suppose that Y1, , Yn are continuous random

variables with respective densities fi(y ) that

depend on some common parameter (which can be

vector-valued). Assume that - is unknown
- we observe y1, , yn.
- We want to estimate the value of associated

with Y1, , Yn . Intuitively, we want to find the

value of that is most likely to give rise to

the data sample y1, , yn.

Estimation of Distribution Parameters Maximum

Likelihood Estimation

- Assume that the observations are independent. We

define the likelihood function - In maximum likelihood estimation, we choose the

value of that maximizes the likelihood

function.

Estimation of Distribution Parameters Maximum

Likelihood Estimation

- Furthermore, since logarithm is a monotone

increasing function, then the value of that

maximizes () also maximizes the log of the

likelihood function - A similar technique applies to discrete R.V.s

employing the probability mass function.

Estimation of Distribution Parameters Maximum

Likelihood Estimation

- Example Suppose that we assume that some data

y1, , yn associated with our system is

exponentially distributed with parameter . To

estimate we calculate the likelihood function - and the log-likelihood function
- Solving for gives

Tests for Determining How Representative the

Fitted Distributions Are

- Histograms/Frequency Comparisons
- Probability Plots
- Quantile-quantile (Q-Q) plots
- Probability-probability (P-P) plots

Tests for Determining How Representative the

Fitted Distributions Are

- Histograms/Frequency Comparisons
- Probability Plots
- Quantile-quantile (Q-Q) plots graph of the

quantiles of the fitted distribution vs. the

quantiles of the sample distribution - Probability-probability (P-P) plots graph of the

fitted cumulative distribution vs. the sample

cumulative distribution

Tests for Determining How Representative the

Fitted Distributions Are

- Quantile-quantile (Q-Q) plots
- A graph of the quantiles of the fitted

distribution vs. the quantiles of the sample

distribution - If the two sets of quantiles come from

populations with the same distribution, then the

points should fall approximately along a

45-degree reference line. - The greater the departure from this reference

line, the greater the evidence for the conclusion

that distribution for the sample data is

different from the fitted distribution.

Quantile-Quantile Plot

Tests for Determining How Representative the

Fitted Distributions Are

- Probability-probability (P-P) plots
- A graph of the fitted cumulative distribution vs.

the sample cumulative distribution - The P-P plot is formed by comparing, for each

value in the ordered sample - The proportion of points in the sample that are

less than or equal to that sample value - The probability of a value less than or equal to

the sample value based on the fitted distribution

- Departures from a straight line indicate

departures from the specified distribution.

Tests for Determining How Representative the

Fitted Distributions Are

- Probability-probability (P-P) plots
- Example
- A sample of 1000 values is fitted to an

exponential distribution with parameter 2. - The 500th value in the ordered sample is 0.3644,

i.e., ½ of the data points do not exceed 0.137. - For the fitted exponential distribution, the

probability of seeing a value less than or equal

to 0.3644 is 0.5175. - Hence (0.5, 0.5175) is a point on the P-P plot.

Probability-Probability Plot

Homework

- Show how to use the inverse-transform method to

generate random variates from a Weibull

distribution. - Use the method discussed in class to generate 500

N(0,1) random variates. Use a P-P plot to show

that the random variates match a normal

distribution. - Use the relationship between the negative

binomial and geometric distributions to give an

algorithm for generating negative binomial

variates.

Homework

- For an M/M/1 queueing system with interarrival

rate 2 and service rate 3 - Generate arrival times and service times for 5

customers. - Simulate this system until all customers depart,

showing the system clock, system state and

scheduled events. - Use your simulation to calculate the average

customer delay. - Use Monte Carlo simulation to estimate the

expected life of a three-component parallel

system in which each component has a U(0, 5)

lifetime distribution.

References

- Sheldon M. Ross, Introduction to Probability

Models, Ninth Edition, Elsevier Inc., 2007. - Averill M. Law, W. David Kelton, Simulation

Modeling and Analysis, Third Edition, 2000. - James C. Spall, Introduction to Stochastic Search

and Optimization Estimation, Simulation, and

Control, Wiley Interscience, 2003.

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