Lecture 17
Nov14, 2011
Discrete event simulation
• discrete time systems
• system changes with time, in discrete steps
• uncertainty (modeled by probability)
• interested in some aspect of outcome
• analytical vs. experimental approach to solution
(latter is the “simulation” technique)
Discrete Event Simulation
•How to generate RV according to a specified
distribution?
• binomial
• geometric
• Poisson etc.
• Examples of a Discrete Event System:
• traffic problem: given the locations of cars and
destinations and traffic rules, what is the expected
time for a specific car to reach its destination?
• repair problem: will be discussed in some detail
and we will do a simulation using Matlab.
Outline
•
•
•
•
•
What is discrete event simulation?
Events
Probability – review through examples
Probability distributions
Some examples
What is discrete event simulation?
“Simulation
is the process of designing a model of a real
system and conducting experiments with this model for
the purpose either of understanding the behavior of the
system or of evaluating various strategies (within the limits
imposed by a criterion or set of criteria) for the operation
of a system.”
-Robert E Shannon 1975
“Simulation is the process of designing a dynamic model of an
actual dynamic system for the purpose either of understanding the
behavior of the system or of evaluating various strategies (within the
limits imposed by a criterion or set of criteria) for the operation of a
system.”
-Ricki G Ingalls 2002
Definitions from http://staff.unak.is/not/andy/Year%203%2Simulation/Lectures/SIMLec2.pdf
What is discrete event simulation?
 A simulation is a dynamic model that replicates
the essential characteristics of a real system.
 Simulations may be deterministic or stochastic,
static or dynamic, continuous or discrete.
 Discrete event simulation (DEVS) is stochastic,
dynamic, and discrete.
 DEVS is not necessarily spatial – it usually isn’t, but
the ideas are applicable to many spatial
simulations
What is discrete event simulation?
• DEVS has been around for decades, and is supported
by a large set of supporting tools, programming
languages (e.g. Simula, Simulink), conventional
practices, etc.
• Like other kinds of simulation, offers an alternative, often
simple way of solving a problem – simulate a system
and observe results, instead of coming up with
analytical model.
• Many other benefits like repeatability, ability to use
multiple parameter sets, cheap compared to physical
experiment, etc. (e.g. crash testing)
• Usually involves posing a question. The simulation
estimates the answer.
What is discrete event simulation?
 Stochastic (probabilistic) uncertainty is
modeled as stochastic
 Dynamic (changes over time)
 Discrete (successive changes are separated
by finite, usually, fixed amounts of time)
Time may be modeled in a variety of ways within
the simulation.
Alternate treatments of time
Time divided into equal increments:
Unequal increments:
Cyclical:
(e.g. traffic light or
bus schedule)
Termination
• Simulation may terminate when a terminating
condition is met. (e.g., when the queue is empty)
• May also be periodic.
• Can also be conceptually endless, like weather,
terminated at some arbitrary time.
Random Variables
• A random variable (“X”) is numerical value associated
with a random event.
• Ex 1: when rolling two dice, the sum of the values on the
top faces)
• Ex 2: keep tossing a coin until you get a Head. The number
of tosses could be the parameter of interest.
• In a stochastic Discrete Event System, the value
associated with event is a random variable, that is its
value has an associated probability.
• In Ex 2 above, the value is 2 with probability 0.25
• By repeating the simulation, we can estimate the
values of critical parameters.
Pseudorandom Number Generators
• A stochastic simulation depends on a pseudorandom
number generator to generate usable random
numbers.
• These generators can vary significantly in quality.
• For practical purposes, it may be important to check
the random number generator you are using to make
sure it behaves as expected.
Using Matlab’s rand(), we can generate random real
numbers between 0 and 1.
Exercise 1: Write a program that distributes a
deck of 52 cards randomly to 4 people so that
each one gets 13 cards. (e.g., hands of Bridge).
Expected output format:
>>[a,b,c,d] = shuffle();
>> a
a
= ‘A Hearts’
‘2 Spades’ ‘8 Hearts’ …
Solution idea:
• More convenient to map cards to integers 1
to 52 so the problem is to divide 52 cards into
four random piles of 13 each.
•If we can generate a vector v that represents
a random permutation of 1:52, then, we can
assign v(1:13) to a, etc.
•So the problem further reduces to: generate a
random permutation of 1:52.
Two possible ideas
1) Start with v = 1:52, then randomly generate two
integers j and k in the range 1:52, exchange v(j) and
v(k) and repeat the process. Question: How many
times should we do this before the deck is well
shuffled? Tricky question to answer.
2) Start with v = 1:52 and randomly generate an integer j
between 1 and 52 and exchange v(j) with v(52). Next
generate an integer j between 1 and 51, and
exchange v(j) with v(51) etc.
We will next show that (2) works. You are to implement
this.
Proof that (2) generates a random permutation of 1:52
Suppose a is the final permutation produced by the above algorithm.
To show that (2) works, we need to show that the probability that any
fixed permutation a = [i1 i2 … i52] is generated by the above method is
1/52!
We will not show this completely. But the main idea is the following.
Prob( a(n) = i52) is 1/52 since in the first step, all numbers from 1 to n
have equal chance of being chosen, and thus i52 has the same
chance of 1/n.
Similarly, we can see that Prob( a(n) = i52 and a(n-1)= i51) = 1/n(n-1)
since in the second step, each of the remaining elements (other than
i52) has equal prob of 1/n-1 and the two events are independent.
Extending this all the way to n steps, we conclude the claim.
Exercise 2: (coupon collector’s problem) When you buy
a can of soda, it contains a coupon with a label in 1:n.
The labels on the can are random in the sense that, at
any point during simulation, the probability that the next
can you buy will contain label j is 1/n. The goal is the
determine the average number of cans you need to
buy before you have collected all the n coupons.
Probability distributions
• In a Discrete Event Simulation, you need to decide what
probability distribution functions best model the events.
in most situations, uniform distribution does not work.
• Pseudorandom number generators generate numbers in
a uniform distribution
• One basic trick is to transform that uniform distribution
into other distributions.
• Some standard probability distributions are convenient to
represent mathematically.
• They may or may not represent reality, but can be useful
simplification.
Normal or Gaussian distribution
 Ubiquitous in statistics
 Many phenomena follow
this distribution
 When an experiment is
repeated, the sum of the
outcomes tend to be
normally distributed.
 We will demonstrate this
experimentally using a
Matlab simulation.
Simulating a Probability distribution
Sampling values from an observational
distribution with a given set of probabilities
(“discrete inverse transform method”).
Generate a random number U
If U < p0 return X1
If U < p0 + p1 return X2
If U < p0 + p1 + p2 return X3
etc.
This can be speeded up by sorting p so that the
larger intervals are processed first, reducing
the number of steps.
Poisson distribution
• Example of algorithm to sample from a distribution.
• X follows a Poisson distribution if:
P{ X  i}  e

i
i!
An algorithm for sampling from a Poisson distribution:
1.
2.
3.
4.
Generate a random number U
If i=0, p=e-l, F=p
If U < F, return I
P = l * p / (i + 1),
F = F + p, i = i + 1
5. Go to 3
There are similar tricks to sampling from other
probability distributions. Some of the distributions (e.g.
Poisson, Normal etc.) can be generated using
Matlab built in functions.
Poisson distribution – Matlab function
>>lambda = 2;
>>random_sample1 = poissrnd(lambda,1,10)
random_sample1 =
1
0
1
2
1
3
4
2
0
0
>>random_sample2 = poissrnd(lambda,[1 10])
random_sample2 =
1
1
1
5
0
3
2
2
3
4
>>random_sample3 = poissrnd(lambda(ones(1,10)))
random_sample3 =
3
2
1
1
0
0
4
0
2
0
Normal distribution – Matlab function
R = normrnd(mu,sigma,m,n,...) or R =
normrnd(mu,sigma,[m,n,...]) generates an m-by-n-by-...
array. The mu, sigma parameters can each be scalars or
arrays of the same size as R.
Examples
n1 = normrnd(1:6,1./(1:6))
n1 =
2.1650 2.3134 3.0250 4.0879 4.8607 6.2827
n2 = normrnd(0,1,[1 5])
n2 =
0.0591 1.7971 0.2641
0.8717
-1.4462
n3 = normrnd([1 2 3;4 5 6],0.1,2,3)
n3 =
0.9299 1.9361 2.9640
4.1246 5.0577 5.9864
A repair problem
n machines are needed to keep an operation.
There are s spare machines. The machines in
operation fail according to some known
distribution (e.g. exponential, Poisson, uniform
etc. with a known mean). When a machine fails,
it is sent to repair shop and the time to fix is a
random variable that follows a known
distribution.
Question: What is the expected time for the
system to crash? System crashes when fewer
than n machines are available.
n = number of working machines needed
S = number of spares available
Matlab implementation