Queueing Theory Dr. Ron Lembke Operations Management.
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Transcript of Queueing Theory Dr. Ron Lembke Operations Management.
![Page 1: Queueing Theory Dr. Ron Lembke Operations Management.](https://reader034.fdocuments.net/reader034/viewer/2022042514/56649efd5503460f94c106bd/html5/thumbnails/1.jpg)
Queueing Theory
Dr. Ron Lembke
Operations Management
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Queues
In England, they don’t ‘wait in line,’ they ‘wait on queue.’
So the study of lines is called queueing theory.
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Cost-Effectiveness
How much money do we lose from people waiting in line for the copy machine? Would that justify a new machine?
How much money do we lose from bailing out (balking)?
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We are the problem Customers arrive randomly. Time between arrivals is called the “interarrival
time” Interarrival times often have the “memoryless
property”: On average, interarrival time is 60 sec. the last person came in 30 sec. ago, expected time
until next person: 60 sec. 5 minutes since last person: still 60 sec.
Variability in flow means excess capacity is needed
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Memoryless Property
Interarrival time = time between arrivals Memoryless property means it doesn’t matter how long
you’ve been waiting. If average wait is 5 min, and you’ve been there 10 min,
expected time until bus comes = 5 min Exponential Distribution Probability time is t =
tetf )(
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Poisson Distribution
Assumes interarrival times are exponential
Tells the probability of a given number of arrivals during some time period T.
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Ce n'est pas les petits poissons.Les poissons Les poissons How I love les poissons Love to chop And to serve little fish First I cut off their heads Then I pull out the bones Ah mais oui Ca c'est toujours delish Les poissons Les poissons Hee hee hee Hah hah hah With the cleaver I hack them in two I pull out what's inside And I serve it up fried God, I love little fishes Don't you?
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Simeon Denis Poisson "Researches on the probability
of criminal and civil verdicts" 1837
looked at the form of the binomial distribution when the number of trials was large.
He derived the cumulative Poisson distribution as the limiting case of the binomial when the chance of success tend to zero.
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Binomial Distribution
The binomial distribution tells us the probability of having x successes in n trials, where p is the probability of success in any given
attempt.
xnx ppx
npnxb
1),,(
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Binomial Distribution The probability of getting 8 tails in 10 coin
flips is:
b(8,10,0.5)10
8
(0.5)8 1 0.5 10 8
10*92*1
*0.0039062*0.254.4%
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Poisson Distribution
x
k
k
x
k
eCUMPOISSON
x
ePOISSON
0 !
!
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POISSON(x,mean,cumulative)
X is the number of events. Mean is the expected numeric value. Cumulative is a logical value that determines
the form of the probability distribution returned. If cumulative is TRUE, POISSON returns the cumulative Poisson probability that the number of random events occurring will be between zero and x inclusive; if FALSE, it returns the Poisson probability mass function that the number of events occurring will be exactly x.
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Larger average, more normal
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Queueing Theory Equations
Memoryless Assumptions: Exponential arrival rate = = 10
• Avg. interarrival time = 1/ • = 1/10 hr or 60* 1/10 = 6 min
Exponential service rate = = 12• Avg service time = 1/ = 1/12
Utilization = = /• 10/12 = 5/6 = 0.833
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Avg. # in System
Lq = avg # in line =
Ls = avg # in system =
Prob. n in system = Because We can also write it as
2
qL
qs LL
Pn 1
n
nnP 1
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Example
Customers arrive at your service desk at a rate of 20 per hour, you can help 35 per hr. What % of the time are you busy? How many people are in the line on average? How many people are there, in total on avg? What are the odds you have 3 or more
people there?
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Queueing Example
λ=20, μ=35 so ρ=20/35 = 0.571 Lq = avg # in line =
Ls = avg # in system = Lq + ρ = 0.762 + 0.571 = 1.332
762.0525
400
203535
2022
qL
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Prob. Given # in System
Prob. n people in system, ρ = 0.571
Prob 0-3 people = 0.429 + 0.245 + 0.140 + 0.080 = 0.894
Prob 4 or more = 1-0.894 = 0.106
n
nP
1 nnP 1
n (1-ρ)*ρ n Value
0 0.429 * 0.5710 =0.429 * 1 0.429
1 0.429 * 0.5711 = 0.429*0.571 0.245
2 0.429 * 0.5712 = 0.429*0.326 0.140
3 0.429 * 0.5713 = 0.429*0.186 0.080
4 0.429 * 0.5714 = 0.429*0.106 0.045
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Probability of n in systemn Pn Pr(<=n) Pr(>n)0 0.429 0.429 0.571 1 0.245 0.674 0.326 2 0.140 0.814 0.186 3 0.080 0.894 0.106 4 0.046 0.939 0.061 5 0.026 0.965 0.035 6 0.015 0.980 0.020 7 0.008 0.989 0.011 8 0.005 0.994 0.006 9 0.003 0.996 0.004
10 0.002 0.998 0.002 11 0.001 0.999 0.001 12 0.001 0.999 0.001
0 1 2 3 4 5 6 7 8 9 10 11 12 -
0.20
0.40
0.60
0.80
1.00
1.20
Pn Pr(<=n) Pr(>n)
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Average Time
Wq = avg wait in line
Ws = avg time in systemq
q
LW
s
s
LW
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How Long is the Wait?
Time waiting for service =• Lq = 0.762, λ=20
• Wq = 0.762 / 20 = 0.0381 hours
• Wq = 0.0381 * 60 = 2.29 min
Total time in system =• Ls = 1.332, λ=20
• Ws = 1.332 / 20 = 0.0666 hours
• Ws = 0.0666 * 60 = 3.996 = 4 min
• μ=35, service time = 1/35 hrs = 1.714 min• Ws = 2.29 + 1.71 = 4.0 min
q
q
LW
s
s
LW
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What did we learn? Memoryless property means exponential
distribution, Poisson arrivals Results hold for simple systems: one line,
one server Average length of time in line, and system Average number of people in line and in
system Probability of n people in the system