queuing theory. 2 queuing theory each of us has spent a great deal of time waiting in lines. in this...
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QUEUING THEORY
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QUEUING THEORY 2
Queuing Theory
• Each of us has spent a great deal of time waiting in lines.• In this chapter, we develop mathematical models for
waiting lines, or queues.
• To describe a queuing system, an input process and an output process must be specified.
• Examples of input and output processes are:
Situation Input Process Output Process
Bank Customers arrive at bank
Tellers serve the customers
Pizza hut Request for pizza delivery are received
Pizza parlor send out scooters to deliver pizzas
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QUEUING THEORY 3
Queuing Costs vs. Level of Service
Cost of operation
Level of Service
Total expected cost
Cost of waiting customers per unit time
Cost of operating the service facility per unit time
Optimum service level
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QUEUING THEORY 4
QUEUING SYSTEM
Input source
Queuing Process
Service Process
Balk Renege Jockey
Queuing discipline
Arrival Process
Waiting Customers
Departure
Service System
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QUEUING THEORY 5
Input Source Characteristics
• Input source is characterized by size of calling population, behavior of the arrivals and pattern of arrival of customers.
• Size of calling population – Homogeneous, sub-classes, finite or infinite.
• Behavior of arrivals – Patient customer, balking, reneging or jockeying.
• Pattern of arrivals – Static or dynamic, pattern is further subdivided based on nature of arrival rate and control on the arrival.
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QUEUING THEORY 6
Pattern of Arrival Process
• In static systems the arrivals can be random or at constant rate.
• Random arrivals can be varying with time.• To analyze the queuing system we should know
the probability distribution of the arrivals.• We obtain distribution for the arrivals and also for
the inter-arrival time.• In dynamic system, both service facility and
customers are controlled.• The arrival pattern can be approximated by
Poisson, Erlang or Exponential distribution.
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QUEUING THEORY 7
QUEUING PROCESS
• Queuing process refers to length of queues and number of queues.
• There can be single server (facility) or multiple facility.
• Queues can be finite (cinema hall) or infinite (sales orders).
• When customers experience long queues then they may not enter the queue.
• Queue discipline controls the service for the customers
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QUEUING THEORY 8
QUEUE DISCIPLINE
• The queue discipline describes the method used to
determine the order in which customers are served.
• The most common queue discipline is the FCFS discipline
(first come, first served), in which customers are served in
the order of their arrival.
• Under the LCFS discipline (last come, first served), the
most recent arrivals are the first to enter service.
• If the next customer to enter service is randomly chosen
from those customers waiting for service it is referred to as
the SIRO discipline (service in random order).
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QUEUING THEORY 9
QUEUEING DISCIPLINE
• Finally we consider priority queuing disciplines. • A priority discipline classifies each arrival into
one of several categories.• Each category is then given a priority level, and
within each priority level, customers enter service on an FCFS basis.
• Another factor that has an important effect on the behavior of a queuing system is the method that customers use to determine which line to join.
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QUEUING THEORY 10
SERVICE PROCESS
• Service process is characterized by following arrangement of service facility. distribution of service times. server’s behavior management policies.
• Arrangement of facilities can be single queue and single server single queue and multiple server mixed arrangement
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QUEUING THEORY 11
Service Process Distribution
• Time taken be server from the commencement to completion of service for a customer is called service time.
• To describe the output process of a queuing system, we usually specify a probability distribution – the service time distribution – which governs a customer’s service time
• The most commonly distribution used for service time distribution is negative exponential.
• Servers are in parallel if all servers provide the same type of service and a customer needs only pass through one server to complete service.
• Servers are in series if a customer must pass through several servers before completing service.
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QUEUING THEORY 12
Performance measures of Queuing theory
• Time related questions What is the average time an arriving customer has to
wait in the queue (Wq) before being served. What is the average time an arriving customer spends
in the system (Ws) including waiting and service.
• Quantitative questions related to no of customers Expected no of customers in the queue for service (Lq) Expected no of customers who are in the system
waiting in queue or being serviced (Ls)
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QUEUING THEORY 13
• Questions involving time both for customers and servers Probability that an arriving customer has to wait before
being served Pw. Probability that a server is busy at any particular point
(). It is the proportion of the time that a server spends actually with a customer.
Probability of having ‘n’ customers in the system when it is in steady state. Pn, n= 0,1,…n.
Probability of customer denied service because of queue being full. Pd.
Performance measures of Queuing theory
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QUEUING THEORY 14
Poisson Distribution• Customers arrive at a bank or grocery in a completely
random fashion.
• The probability density function for describing such arrivals during a specified period follows Poisson distribution.
• Let x be the arrivals that take place during a specified time units.
• Poisson pdf is given by
• Mean and variance is given by
1,2,..k ,!
][
k
ekxP
k
}var{
}{
x
xE
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QUEUING THEORY 15
Exponential Distribution• If the no of arrivals at a service facility during a specified
period occurs according to Poisson distribution then the distribution of the intervals between successive arrivals must follow the negative exponential distribution.
• If is the rate at which Poisson events occur then the distribution of the time ,x, between successive arrivals is given as
AA
x
x
edxeAxPCDF
Var
xEMean
exf
1 }{
1 {x}
1}{
)(
0
2
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QUEUING THEORY 16
Analysis of a Simple Queue
Single server
Arrivals with an average arrival rate of
service rate at the server
Infinite no of waiting customers.
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QUEUING THEORY 17
Steady State & Transient State
• At the start of the system the queue is influenced by no of customers, and the elapsed time.
• This is referred as transient state.• After sufficient time the system returns to steady
state.• We analyse the steady state behavior as transient
state is complex and beyond our scope.• Arrival rate has to be less than service rate else the
system cannot reach steady state.
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QUEUING THEORY 18
Modeling Arrival Process
• We assume that the arrival process can be approximated by Poisson distribution.
• Let us consider a Poisson process involving the number of arrivals n over a time period t.
• If is the arrival rate (traffic intensity) then the no of customers expected in time t will be t.
• The probability mass function Pn is given by
..2,1,0 , !
)()|(
n
n
ettPnnxP
tn
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QUEUING THEORY 19
• The probability mass function of no arrival (n=0) in time t is given by
• Let us define the random variable T as the time between successive arrivals.
• T will be a continuous random variable.
• The assumption that each inter-arrival time is governed by the same random variable implies that the distribution of arrivals is independent of the time of day or the day of the week.
• This is the assumption of stationary inter-arrival times.
Modeling Arrival Process
tt
eet
tPnxP
!0
)()|0(
0
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QUEUING THEORY 20
Modeling Arrival Process
• Stationary inter-arrival times is often unrealistic, but we may often approximate reality by breaking the time of day into segments.
• A negative inter-arrival time is impossible. This allows us to write
• We define1/λ to be the mean or average inter-arrival time.
t
tdttatTPdttatTP
0)()(and)()(
0
)(1
dttta
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QUEUING THEORY 21
• The distribution of the random variable T is called the exponential distribution.
• Its probability density function is given by f(t) = λe-λt. for t 0 and is o for t<0.
• We can show that the average or mean inter-arrival time is given by .
Modeling Arrival Process
1
)( TE
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QUEUING THEORY 22
Derivation of Exp Distribution
• The exponential distribution is based on three axioms: Given N(t), the number of events during the interval
(0,t), the probability process describing N(t) has stationary independent increments, in the sense that the probability of an event occurring in interval (T, T+S) depends only on the length of S.
The probability of an event occurring in a sufficiently small time interval h> 0 is positive but less than 1
In a sufficiently small time interval h>0, at most one event can occur-that is P{N(h)>1}=0
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QUEUING THEORY 23
Derivation of Exponential Distribution
• Define Pn(t) as the probability of n events occurring during t.
• We have seen earlier that probability of no arrival in time t is given by P0(t) = e -t
• Define f(t) as the pdf of the interval t between successive events, t>0 . We then have P(inter-event time >T) = P(no event during T)
ondistributi lexponentia is This 0. t,)(
yield willsidesboth of derivative Taking
0T ,1)(
grearranginafter which 0T ),()(
0
0
t
Tt
T
etf
etf
TPtf
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QUEUING THEORY 24
No Memory Property of Exp Distribution
• If T has an exponential distribution, then for all nonnegative values of t and h,
• A density function that satisfies the equation is said to have the no-memory property.
• The no-memory property of the exponential distribution is important because it implies that if we want to know the probability distribution of the time until the next arrival, then it does not matter how long it has been since the last arrival.
)()|( hTPtThtTP
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QUEUING THEORY 25
Markov Process
• Markov Processes X(t) satisfies the Markov Property (memoryless) which
states that -for any choice of time instants ti, i=1,……, n where tj>tk for j>k
P{X(tn+1)=xn+1| X(tn)=xn ……. X(t1)=x1} =P{X(tn+1)=xn+1| X(tn)=xn}
• Memoryless property as the state of the system at future time tn+1 is decided by the system state at the current time tn and does not depend on the state at earlier time instants t1,…….., tn-1
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QUEUING THEORY 26
Modeling the Service Process• We assume that the service times of different customers
are independent random variables and that each customer’s service time is governed by a random variable S having a density function s(t).
• We let 1/µ be the mean service time for a customer.• The variable 1/µ will have units of hours per customer, so
µ has units of customers per hour. For this reason, we call µ the service rate.
• Unfortunately, actual service times may not be consistent with the no-memory property.
• In certain situations, inter-arrival or service times may be modeled as having zero variance; in this case, inter-arrival or service times are considered to be deterministic.
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QUEUING THEORY 27
• For example, if inter-arrival times are deterministic, then each inter-arrival time will be exactly 1/λ, and if service times are deterministic, each customer’s service time is exactly 1/µ.
• Negative exponential distribution is used to describe the service times distribution.
• The pdf of the service times is
• The probability of n service completion in time t is given by
Modeling the Service Process
0 , )( tetf t
..2,1,0 ,!
)( t)in time completion service (
n
n
etnP
tn
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QUEUING THEORY 28
Basic Notations
wait. tohascustomer arrivingan y that probabilit
queue. in the time waiting(expected) average
service)in & (waiting system in the time waiting(expected) average
queueempty -non oflength (expected) average
queue in the customers of no (expected) average
served) & queue(in system in the customers of no (expected) average
system in the allowed fcustomers o no maximum
channels service of no
busy) isserver timeof(fraction factor n utilisatioserver or intensity traffic
)(1/ timearrival-inter )/average(1/ timecompletion service average
time.ofunit per served customers of no averageor rate service (expected) average
unit timeper rate arrival averageor rate arrivalcustomer (expected) average
system in the customersn ofy probabilit
service)in & (waiting system in the customers of no
Pw
Wq
Ws
Lb
Lq
Ls
N
s
Pn
n
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QUEUING THEORY 29
Relationship among Performance measures
• We have the following relationships
• Average no of customers in the system is equal to the expected no of customers in queue plus in service
• Average waiting time of the customer in the system is equal to waiting time in queue plus in service
sn
n
n
n PsnLqnPLs )( 0
LqELLs
1WqWs
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QUEUING THEORY 30
• Average no of customers served per busy period is given by
• Average length of queue during busy period is given by
Relationship among Performance measures
busy being systemy that probabilit)( where
)(
snP
snP
LsLb
1
)( snP
WqWb
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QUEUING THEORY 31
• Little's law gives a very important relation between Ls, the mean number of customers in the system, Ws, the mean sojourn time and , the average number of customers entering the system per unit time. Little's law states that
• The probability , Pn of n customers in the queuing system at any time can be used to determine all the basic measures of performance in the following order
Relationship among Performance measures
LsLqWqWqLq
LsLsWsWsLs
or 1
or
1or
1or
WqLqWsWqLs
WsnPnLsn
1
0
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QUEUING THEORY 32
Basic Axioms
• Let Pn(t) denote the probability that there are n customers in the system at time t.
• The rate of change of Pn(t) with respect to time t is denoted by derivative of Pn(t) with respect to t.
• In case of steady state we have
tn
nn
t
nt
n
tPLim
Pdt
dtP
dt
dLim
tPtPLim
0)('
)()}({
oft independen )(
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QUEUING THEORY 33
Basic Axioms
• The no of arrivals in non-overlapping intervals are statistically independent.
• The probability that two or more customers arrive in time interval (t, t+t) is negligible and is denoted by 0 t.
• The probability that a customer arrives in the time interval (t, t+t) is P1(t) = t + 0 t.
is inter-arrival time and is independent of total no of arrivals up to time t. As t 0, the qty 0 t is negligible.
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QUEUING THEORY 34
Probability Analysis
• Assume that, as t 0
• P{one arrival in time t} = t
• P{no arrival in time t} =1- t
• P{more than one arrival in time t} = O(( t)2) = 0
• P{one departure in time t} = t
• P{no departure in time t} =1- t
• P{more than one departure in time t} = O(( t)2) = 0
• P{one or more arrival and one or more departure in time t} = O(( t)2) = 0
• Arrival Process, Mean Inter-arrival time = 1/ • Service Process, Mean Service time = 1/
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QUEUING THEORY 35
Waiting Time Paradox
• Suppose the time between the arrival of buses at the student center is exponentially distributed with a mean of 60 minutes.
• If we arrive at the student center at a randomly chosen instant, what is the average amount of time that we will have to wait for a bus?
• The no-memory property of the exponential distribution implies that no matter how long it has been since the last bus arrived, we would still expect to wait an average of 60 minutes until the next bus arrived.
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QUEUING THEORY 36
Birth Death Process
• We subsequently use birth-death processes to answer questions about several different types of queuing systems.
• We define the number of people present in any queuing system at time t to be the state of the queuing systems at time t.
• We call Pn the steady state, or equilibrium probability, of state n.
• The behavior of Pij(t) before the steady state is reached is called the transient behavior of the queuing system.
• A birth-death process is a continuous-time stochastic process for which the system’s state at any time is a nonnegative integer.
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QUEUING THEORY 37
Birth Death Process
• Law 1 With probability λnΔt+o(Δt), a birth occurs between time t and
time t+Δt. A birth increases the system state by 1, to n+1. The variable λn is called the birth rate in state n. In most queuing systems, a birth is simply an arrival.
• Law 2 With probability µnΔt+o(Δt), a death occurs between time t and
time t + Δt. A death decreases the system state by 1, to n-1. The variable µn is the death rate in state n. In most queuing systems, a death is a service completion. Note that µ0 = 0 must hold, or a negative state could occur.
• Law 3 Births and deaths are independent of each other.
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QUEUING THEORY 38
Steady State Birth Death Process
• We now show how the Pn’s may be determined for an arbitrary birth-death process.
• The key role is to relate (for small Δt) Pij(t+Δt) to Pij(t).
• The above equations are often called the flow balance equations, or conservation of flow equations, for a birth-death process.
,...)2,1()(1111 nPPP nnnnnnn
0011 PP
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QUEUING THEORY 39
Flow Balancing Approach
• In the “rate diagram” given below, think of the following:
• Each circle representing a state (i.e., number of customer in the system) has an unknown probability Pn, n= 0, 1, 2, … associated with it
0 1 2 3
4
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QUEUING THEORY 40
Flow Balancing Approach• We obtain the flow balance equations for a birth-death
process:
• In general we can write
• Value of P0 is determined from the equation
1,2,...n ,)......(
).....(0
11
021
PPn
nn
nn
1111
3311222
2200111
1100
)()equationth (
...
)()2(
)()1(
)0(
nnnnnnn PPPn
PPPn
PPPn
PPn
0
1n
Pn
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QUEUING THEORY 41
The Kendall-Lee Notation for Queuing Systems
• Standard notation used to describe many queuing systems.
• The notation is used to describe a queuing system in which all arrivals wait in a single line until one of s identical parallel servers is free. Then the first customer in line enters service, and so on.
• To describe such a queuing system, Kendall devised the following notation.
• Each queuing system is described by six characters: 1/2/3/4/5/6
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QUEUING THEORY 42
• The first characteristic specifies the nature of the arrival process. The following standard abbreviations are used: M = Inter-arrival times are independent, identically
distributed (iid) and exponentially distributed D = Inter-arrival times are iid and deterministic Ek = Inter-arrival times are iid Erlangs with shape
parameter k. GI = Inter-arrival times are iid and governed by some
general distribution
The Kendall-Lee Notation for Queuing Systems
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QUEUING THEORY 43
• The second characteristic specifies the nature of the service times: M = Service times are iid and exponentially distributed D = Service times are iid and deterministic Ek = Service times are iid Erlangs with shape parameter
k G = Service times are iid and governed by some
general distribution
• The third characteristic is the number of parallel servers.
The Kendall-Lee Notation for Queuing Systems
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QUEUING THEORY 44
• The fourth characteristic describes the queue discipline: FCFS = First come, first served LCFS = Last come, first served SIRO = Service in random order GD = General queue discipline
• The fifth characteristic specifies the maximum allowable number of customers in the system.
• The sixth characteristic gives the size of the population from which customers are drawn.
• M/E2/8/FCFS/10/∞ might represent a health clinic with 8 doctors, exponential interarrival times, two-phase Erlang service times, a FCFS queue discipline, and a total capacity of 10 patients.
The Kendall-Lee Notation for Queuing Systems
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QUEUING THEORY 45
(M/M/1):(FCFS// )• The models has following assumptions
Poisson arrival rate and exponential distribution of inter-arrival times.
Single waiting line with no restriction on queue length Queuing discipline is First come first served Single server with exponential service time.
• The solution is obtained by using flow balancing approach. The three states possible in small time Δt before t are The system is in state n and no arrivals or service completions
occurred. The system is in state (n+1) and a service completion occurs,
reducing the customers to n. The system is in state (n-1) and an arrival occurs, bringing the no
of customers to n.
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QUEUING THEORY 46
Single Server (M/M/1)• Obtaining system of equations
• Taking the limits as t 0, and subject to the same normalisation, we get
1...1Pcondition ion normalisat tosubjected
0nfor ]1)[()(
0nfor )(]1)[()(
210
0n
n
11
100
nPPPP
tPntPntttPnttPn
ttPttPttP
0nfor )()()()()(
0nfor )()()(
11
100
tPntPntPndt
tdPn
tPtPdt
tdP
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QUEUING THEORY 47
Single Server Model• For equilibrium conditions we require
• Let =/. Then we get following results
• Applying normalisation condition of Pn=1 we get
0,1,2,..nfor 0)}({ and )(
tPndt
dLimPntPnLimtt
001 PPnPP N
)1( and 10 hence and 1
1
is sum whoseseries geometric infinitean isr denominato The
1
0N
0
0
NN
N
N
PnP
P
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QUEUING THEORY 48
Performance Measures – Single Server• Average no of customers in the system
• Average no of customers in queue
1
})1(
){1()1(
10 ,)1(
2
Ls
nLs
nnPLs
n
nn
)(
)(]1[][
) to1(n )1(
2
2
0
Lq
PoLsPoPnLsLq
PnnPnPnnLq
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QUEUING THEORY 49
• Average time a customer spends waiting in the queue
• Average time a customer spends in system
• Probability of customers in system greater than or equal to k
Performance Measures – Single Server
)(
LqWq
LsWs
LsWqWs
Ws
)(11
timeservice queuein timeWaiting
1
)( ;)(
kk
knPknP
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QUEUING THEORY 50
• Variance (fluctuations of queue length)
• Probability that a queue is non-empty
• Expected length of non-empty queue
Performance Measures – Single Server
22 )()1()(
nVar
2
1 1)1(
PPonP
)1(1)ob(nPr
line waitingoflength Expected
nP
LqLb
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QUEUING THEORY 51
P a r a m e t e r s S y s t e m Q u e u e A v e r a g e n o o f c u s t o m e r s i n
1Ls
)(
2
Lq
A v e r a g e w a i t i n g t i m e p e r u n i t i n
LsWs
P r o b a b i l i t y o f h a v i n g k o r m o r e u n i t s i n t h e s y s t e m
k
knP
)(
E x p e c t e d l e n g t h o f n o n e m p t y q u e u e
Lb
Lb
P r o b a b i l i t y t h a t a q u e u e i s n o n e m p t y
2
)1(
nP
P r o b a b i l i t y t h a t w a i t i n g t i m e i s m o r e t h a n t te )(
te )(
)(
LqWq
001 PPnPP N
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QUEUING THEORY 52
Example (M/M/1)
• At a cycle repair shop on an average a customer arrives every five minutes and on an average, the service time is 4 minutes per customer. Suppose that the inter-arrival time follows Poisson distribution and service time is exponentially distributed. Find the various performance characteristics
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QUEUING THEORY 53
M/M/1:N/FCFS/• Limited no of customers are allowed in the system.
• Service rate need not be greater than arrival rate.
• Steady state equations are
Nnfor
1.-1,2,...Nnfor )(
0nfor
1
11
10
NN-
nnn
μPλP
PPP
PP
Nn 0 and Nnfor 0
get we,/ Using
1,2,...nfor
1,..N N, nfor 0
1-N 0,1,2...,nfor
forPnnPPn
μ μn
n
n
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QUEUING THEORY 54
Performance characteristics
• Service rate meed not be more than arrival rate as arrivals are controlled.
eff, rather than ,, is the rate that matters.• eff is calculated as arrival rate minus the
probability of losing customers. eff = - lost = - Pn= (1-Pn)• Other parameters are calculated from the eff.
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QUEUING THEORY 55
Performance Characteristics
1for 1
1
1 and 1for ,1
11
NPo
PoN
1for 1
1
1for ,1
)1(1
NPo
PnN
n
Parameters System Queue Average no of customers in
N
n
nPnLs0
)1( NP
Lq
Average waiting time per unit in
)1( NP
LsWs
)1( NP
LqWq
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QUEUING THEORY 56
Example
• Consider a single server queuing system with Poisson arrival, exponential input, exponential service times. Suppose the mean arrival rate is 3 calling units per hour, the expected service time is 0.25 hour and the maximum permissible calling units in the system is 2. Derive the steady state probability distribution of the number of calling units in the system, and then calculate the expected no in the system.
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QUEUING THEORY 57
M/G/1: GD/ /
• Queuing models in which the arrival and departure rate do not follow Poisson are complex.
• The above is a model with Poisson arrival rate and service time t, is represented by any general distribution.
• The mean of the distribution is E(t) and variance is Var(t).
• Let be the arrival rate. If E(t) < 1, then we can calculate the Length of the system queue.
1)( ,])[1(2
])var[)(2(2)(
tEtE
ttEtELs
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QUEUING THEORY 58
Example