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QUEUING THEORY[M/M/C MODEL]
Student Adviser:-Assist.Prof. Sanjay Kumar
Student:-Ram Niwas Meena
Semester:-Fourth
“Delay is the enemy of efficiency” and “Waiting is the enemy of utilization”
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OVERVIEW
What is queuing theory? Examples of Real World Queuing Systems? Components of a Basic Queuing Process A Commonly Seen Queuing Model Terminology and Notation Little’s Formula The M/M/1 – model Example M/M/c Model
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Mathematical analysis of queues and waiting times in stochastic systems. Used extensively to analyze production and service processes
exhibiting random variability in market demand (arrival times) and service times.
Queues arise when the short term demand for service exceeds the capacity Most often caused by random variation in service times and
the times between customer arrivals. If long term demand for service > capacity the queue will
explode!
Queuing theory is the mathematical study of waiting lines (or
queues) that enables mathematical analysis of several related
processes, including arriving at the (back of the) queue, waiting in the queue, and being served by the Service Channels at the front of the queue.
WHAT IS QUEUING THEORY?
What is Transient & Steady State of the system?Queuing analysis involves the system’s behavior over time. If the operating characteristics vary with time then it is said to be transient state of the system.If the behavior becomes independent of its initial conditions (no. of customers in the system) and of the elapsed time is called Steady State condition of the system
What do you mean by Balking, Reneging, Jockeying?BalkingIf a customer decides not to enter the queue since it is too long is called BalkingRenegingIf a customer enters the queue but after sometimes loses patience and leaves it is called RenegingJockeyingWhen there are 2 or more parallel queues and the customers move from one queue to another is called Jockeying
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QUEUING MODELS CALCULATE: Average number of customers in the system waiting and being
served Average number of customers waiting in the line Average time a customer spends in the system waiting and being
served Average time a customer spends waiting in the waiting line or
queue. Probability no customers in the system Probability n customers in the system Utilization rate: The proportion of time the system is in use.
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Commercial Queuing Systems Commercial organizations serving external customers Ex. , bank, ATM, gas stations…
Transportation service systems Vehicles are customers or servers Ex. Vehicles waiting at toll stations and traffic lights, trucks
or ships waiting to be loaded, taxi cabs, fire engines, elevators, buses …
Business-internal service systems Customers receiving service are internal to the organization
providing the service Ex. Inspection stations, conveyor belts, computer support …
Social service systems Ex. Judicial process, hospital, waiting lists for organ
transplants or student dorm rooms …
Examples of Real World Queuing Systems?
Prabhakar Car Wash
enter exit
Population ofdirty cars
Arrivalsfrom thegeneral
population …
Queue(waiting line)
Servicefacility Exit the system
Exit the systemArrivals to the system In the system
Arrival Characteristics•Size of the population•Behavior of arrivals•Statistical distribution of arrivals
Waiting Line Characteristics•Limited vs. unlimited•Queue discipline
Service Characteristics•Service design•Statistical distribution of service
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The calling population The population from which customers/jobs
originate The size can be finite or infinite (the latter is
most common) Can be homogeneous (only one type of
customers/ jobs) or heterogeneous (several different kinds of customers/jobs)
The Arrival Process Determines how, when and where customer/jobs
arrive to the system Important characteristic is the customers’/jobs’
inter-arrival times To correctly specify the arrival process requires
data collection of inter arrival times and statistical analysis.
Components of a Basic Queuing Process (II)
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The queue configuration Specifies the number of queues
Single or multiple lines to a number of service stations
Their location Their effect on customer behavior
Balking and reneging Their maximum size (# of jobs the queue can
hold) Distinction between infinite and finite
capacity
Components of a Basic Queuing Process (III)
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The Service Mechanism Can involve one or several service facilities with one or several
parallel service channels (servers) - Specification is required The service provided by a server is characterized by its service
time Specification is required and typically involves data
gathering and statistical analysis. Most analytical queuing models are based on the assumption
of exponentially distributed service times, with some generalizations.
The queue discipline Specifies the order by which jobs in the queue are being
served. Most commonly used principle is FIFO. Other rules are, for example, LIFO, SPT, EDD… Can entail prioritization based on customer type.
Components of a Basic Queuing Process (IV)
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A Commonly Seen Queuing Model (I)
C C C … CCustomers (C)
C S = Server
C S
•
•
•
C S
Customer =C
The Queuing System
The Queue
The Service Facility
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Service times as well as inter arrival times are assumed independent and identically distributed If not otherwise specified
Commonly used notation principle: (a/b/c):(d/e/f)
a = The inter arrival time distribution b = The service time distribution c = The number of parallel servers d= Queue discipline e = maximum number (finite/infinite) allowed in the system f = size of the calling source(finite/infinite)
Commonly used distributions M = Markovian (exponential/possion) –arrivals or departurs distribution
Memoryless D = Deterministic distribution G = General distribution
Example: M/M/c Queuing system with exponentially distributed service and inter-arrival
times and c servers
A Commonly Seen Queuing Model (II)
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Example – Service Utilization Factor
• Consider an M/M/1 queue with arrival rate = and service intensity =
• = Expected capacity demand per time unit• = Expected capacity per time unit
μλ
CapacityAvailableDemandCapacity
ρ
*cCapacityAvailable
DemandCapacity
• Similarly if there are c servers in parallel, i.e., an M/M/c system but the expected capacity per time unit is then c*
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The state of the system = the number of customers in the system
Queue length = (The state of the system) – (number of customers being served)
n =Number of customers/jobs in the system at time t
Pn(t) =The probability that at time t, there are n customers/jobs
in the system.
n =Average arrival intensity (= # arrivals per time unit) at n
customers/jobs in the system
n =Average service intensity for the system when there are n customers/jobs in it.
=The utilization factor for the service facility. (= The expected
fraction of the time that the service facility is being used)
Terminology and Notation
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Pn = The probability that there are exactly n customers/jobs in the system (in steady state, i.e., when t)
L = Expected number of customers in the system (in steady state)
Lq = Expected number of customers in the queue (in steady state)
W = Expected time a customer spends in the system
Wq= Expected time a customer spends in the queue
Notation For Steady State Analysis
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Assume that n = and n = for all n
Assume that n is dependent on n
Little’s Formula
WL qq WL
WL qq WL
0nnnPLet
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Assumptions - the Basic Queuing Process Infinite Calling Populations
Independence between arrivals The arrival process is Poisson with an expected arrival
rate Independent of the number of customers currently in the
system The queue configuration is a single queue with
possibly infinite length No reneging or balking
The queue discipline is FIFO The service mechanism consists of a single server
with exponentially distributed service times = expected service rate when the server is busy
The M/M/1 - model
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n= and n = for all values of n=0, 1, 2, …
The M/M/1 Model
0
1 nn-1
2 n+1
L=/(1- ) Lq= 2/(1- ) = L-
W=L/=1/(- ) Wq=Lq/= /( (- ))
Steady State condition: = (/) < 1
Pn = n(1- )P0 = 1- P(nk) = k
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Situation Patients arrive according to a Poisson process with
intensity ( the time between arrivals is exp() distributed.
The service time (the doctor’s examination and treatment time of a patient) follows an exponential distribution with mean 1/ (=exp() distributed)
Þ The SMS can be modeled as an M/M/c system where c=the number of doctors
Example – SMS Hospital
Data gatheringÞ = 2 patients per hourÞ = 3 patients per hour
Questions– Should the capacity be increased from 1 to 2
doctors?– How are the characteristics of the system (, Wq, W,
Lq and L) affected by an increase in service capacity?
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Interpretation To be in the queue = to be in the waiting room To be in the system = to be in the ER (waiting or under treatment)
Is it warranted to hire a second doctor ?
Summary of Results – SMS Hospital
Characteristic One doctor (c=1) Two Doctors (c=2) 2/3 1/3
P0 1/3 1/2
(1-P0) 2/3 1/2
P1 2/9 1/3
Lq 4/3 patients 1/12 patients
L 2 patients 3/4 patients
Wq 2/3 h = 40 minutes 1/24 h = 2.5 minutes
W 1 h 3/8 h = 22.5 minutes
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In steady state the following balance equation must hold for every state n (proved via differential equations)
Generalized Poisson queuing model
The Rate In = Rate Out Principle:
Mean entrance rate = Mean departure rate
0ii 1P
• In addition the probability of being in one of the states must equal 1
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0011 PP
State Balance Equation
0
1
n
11112200 PPPP
nnn1n1n1n1n P)(PP
Generalized Poisson queuing model
01
01 PP
1
2
12 PP
1nn
1nn PP
11PP:ionNormalizat0i 321
210
21
10
1
00i
C0 C2
23
Steady State Probabilities
Expected Number of customers in the System and in the Queue Assuming c parallel servers
Steady State Measures of Performance
10P 0PP nn
0n
nPnL
cn
iq PcnL )(
COMPONENTS OF A QUEUING SYSTEM
Arrival Process
ServersQueue or Waiting Line
Service Process
Exit
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The M/M/c Model (I)
1c1c
0n
n
0 )c/((11
!c)/(
!n)/(
P
,2c,1cnforPc!c
)/(
c,,2,1nforP!n)/(
P
0cn
n
0
n
n
0
2 (c-1)
c
1 cc-2
2 c+1
c
c-1
(c-2)
• Generalization of the M/M/1 model– Allows for c identical servers working independently from
each other
Steady State Condition:
=(/c)<1
26
W=Wq+(1/)
Little’s Formula Wq=Lq/
The M/M/c Model (II)
02
c
cnnq P
)1(!c
)/(...P)cn(L
• A Condition for existence of a steady state solution is that = /(c) <1
Little’s Formula L=W= (Wq+1/ ) = Lq+ /
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An M/M/c model with a maximum of K customers/jobs allowed in the system If the system is full when a job arrives it is denied
entrance to the system and the queue. Interpretations
A waiting room with limited capacity (for example, the ER at County Hospital), a telephone queue or switchboard of restricted size
Customers that arrive when there is more than K clients/jobs in the system choose another alternative because the queue is too long (Balking)
The M/M/c/K – Model (I)
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The state diagram has exactly K states provided that c<K
The general expressions for the steady state probabilities, waiting times, queue lengths etc. are obtained through the balance equations as before (Rate In = Rate Out; for every state)
The M/M/c/K – Model (II)
0
2 (c-1)
c
1 K-1c-1
2 Kc cc
3
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An M/M/c model with limited calling population, i.e., N clients
A common application: Machine maintenance c service technicians is responsible for keeping N service
stations (machines) running, that is, to repair them as soon as they break
Customer/job arrivals = machine breakdowns Note, the maximum number of clients in the system = N
Assume that (N-n) machines are operating and the time until breakdown for each machine i, Ti, is exponentially distributed (Tiexp()). If U = the time until the next breakdown
Þ U = Min{T1, T2, …, TN-n} Uexp((N-n))).
The M/M/c//N – Model (I)
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• The State Diagram (c service technicians and N machines)– = Arrival intensity per operating machine– = The service intensity for a service technician
• General expressions for this queuing model can be obtained from the balance equations as before
The M/M/c//N – Model (II)
0 N (N-1) (N-(c-1))
2 (c-1)
c
1 N-1c-1
2 Nc c3
Reference:-
1. Operations research: an introduction
By:- Hamdy A. Taha2 . Operations research By:-P.Sankar Iyar