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QUEUEING THEORY
Muthuganesan N
Prakash S.P
Murphy's Law
If you change queue, the one that you
left will start to move faster than
the one you are in now.
Your queue always goes the slowest.
Whatever queue you join, no matter
how short it looks, it will always take
the longest to you to get served.
History of queueing theory
 Developed to provide models for forecasting
behaviors of systems subject to random demand
 The first problems addressed concerned congestion
of telephone traffic
 Erlang observed that a telephone system can be
modeled by Poisson customer arrivals and
exponentially distributed service times
 Molina, Pollaczek, Kolmogorov, Khintchine, Palm,
Crommelin followed the track
Importance of Queueing system
Queues/ waiting line
▪Common phenomenon of everyday life
▪Line maybe People / Items
▪Examples
– Grocery shop, Bank, Petrol refilling units, Automobile Service
station, Airplane, Train etc.
Queue
The line that forms in front of
service facilities is called a queue or
a waiting line
It involves arriving customers (or
items) who wait to utilize the
services at the facility which provide
the service they require
Departure of served
customers
Departure of impatient customers
Customer
arrivals
Queueing theory
A branch of the operations research providing probabilistic modelling tools
for Queueing Systems
 Defined as the construction of mathematical models of various types of
queuing system so that predictions may be made about how the
systems will cope with demands made upon it
Objective of queueing theory
▪Minimize total waiting time and service costs
WAITING COST AND SERVICE LEVEL TRADE-OFF
Queueing system
 A very simple mechanism
 Customers arrive at service counter and are attended by
one or more of the service counters
 As soon as a customer is served, he departs from the
system
 Arrivals
 Queue
 Service
 Outlet
Components/Characteristics of queueing system
1.The input process or arrival process
2.The Queue discipline or Waiting line
3.The Service mechanism
4.The Outlet of the queue
The input process or arrival process
Arrival from the input population may be classified based on
a.Size of the calling population
b.Pattern of arrivals at the system
c.Behavior of the arrivals
a. Size of the calling population
▪Can be finite / infinite
▪Infinite
–Cars arriving at toll booth
–Shoppers arriving at a supermarket
▪Finite
–Fleet of 4 cars visiting a repair shop
–A milling unit with three mills which require service from time to time
b. Pattern of arrivals at the system
▪Organized pattern/ random order
▪No. of arrivals per unit time estimated by Poisson
distribution
P(x) = e-λ λx
x!
◦ λ = the average arrival rate per time unit
◦ P(x) = the probability of exactly x arrivals occurring during one
time period
Arrival rate
▪The average number of customers arriving per time
period
▪λ
c. Behavior of the arrivals
▪Customer attitude about getting into line differs
▪Balking customer refuse to join the line because it is too long to suit
their needs or interest
▪Reneging customer enters the queue but they become impatient and
leave without completing their transaction
▪Jockeying customers move from one queue to another hoping to
receive service more quickly
2. The Queue discipline or Waiting line
▪Length of line unlimited/ limited
▪A queue is limited when it cannot by law or physical
restrictions increase to an infinite length. Ex. Small
restaurant
▪A queue is unlimited when its size is unrestricted, as in case of
the toll booth serving arriving automobiles
Order of service
▪FCFS
▪LCFS
▪SIRO
▪Priority service
▪Pre-emptive priority
▪Non pre-emptive priority
3. The Service mechanism
◦ Two aspects
a.Structure of the service system
b.Speed of service
Structure of the service system
How the service facilities are arranged
i.A single facility
ii.1 queue – several service facility
iii.Multichannel facility
iv.Multi-stage channel facility
b. Speed of service
Speed of service can be expressed in 2 ways:
i.Service rate
–No. of customers during a particular period of time
ii.Service time
–The amount of time required to service a customer
–Generally assumed to be exponentially distributed about
average service time
Service rate
▪The average number of customers that can be served
per time period
▪μ
4. The Outlet of the queue
▪No problem in single channel facility whereas,
▪Problem in multistage channel facility
▪Service station breakdown can have effect on the queues
▪Line before the breakdown will lengthen and the line
following the breakdown will diminsh
Queuing Models
▪Grouped as
▪Deterministic (Fixed arrival and fixed service time)
▪Probabilistic (Random arrivals)
– Use assumptions
Kendall’s Notation
A/B/m/K/N/D
A Arrival Process M: Markovian D: Deterministic Er:
Erlang G: General
B Service Process M: Markovian D: Deterministic Er:
Erlang G: General
m Number of servers m=1,2,…
K Storage Capacity K= 1,2,… (if œ †hen i† is omified)
N Number of customers N= 1,2,…
(for closed networks otherwise it is omitted)
D Queueing Discipline FCFS,LCFS,SIROetc. Default is FCFS
Commonly used Queueing models
Model Queue characteristic Example
M/M/1/∞ Single server, discouraged
Arrivals
Single help Desk or counter in
store
M/M/m/∞ m servers, infinite number of
waiting positions
Airline ticket counter
M/M/m/m m server loss system, no waiting Automated car wash facility
M/M/1/K Single server queue with K
waiting positions
Mechanic shop
M/M/1/-/N Single server, infinite number of
waiting positions, finite customer
population N
Receiving degree in
Convocation
THANK YOU

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Queueing theory

  • 2. Murphy's Law If you change queue, the one that you left will start to move faster than the one you are in now. Your queue always goes the slowest. Whatever queue you join, no matter how short it looks, it will always take the longest to you to get served.
  • 3. History of queueing theory  Developed to provide models for forecasting behaviors of systems subject to random demand  The first problems addressed concerned congestion of telephone traffic  Erlang observed that a telephone system can be modeled by Poisson customer arrivals and exponentially distributed service times  Molina, Pollaczek, Kolmogorov, Khintchine, Palm, Crommelin followed the track
  • 5. Queues/ waiting line ▪Common phenomenon of everyday life ▪Line maybe People / Items ▪Examples – Grocery shop, Bank, Petrol refilling units, Automobile Service station, Airplane, Train etc.
  • 6. Queue The line that forms in front of service facilities is called a queue or a waiting line It involves arriving customers (or items) who wait to utilize the services at the facility which provide the service they require Departure of served customers Departure of impatient customers Customer arrivals
  • 7. Queueing theory A branch of the operations research providing probabilistic modelling tools for Queueing Systems  Defined as the construction of mathematical models of various types of queuing system so that predictions may be made about how the systems will cope with demands made upon it
  • 8. Objective of queueing theory ▪Minimize total waiting time and service costs
  • 9. WAITING COST AND SERVICE LEVEL TRADE-OFF
  • 10. Queueing system  A very simple mechanism  Customers arrive at service counter and are attended by one or more of the service counters  As soon as a customer is served, he departs from the system  Arrivals  Queue  Service  Outlet
  • 11. Components/Characteristics of queueing system 1.The input process or arrival process 2.The Queue discipline or Waiting line 3.The Service mechanism 4.The Outlet of the queue
  • 12. The input process or arrival process Arrival from the input population may be classified based on a.Size of the calling population b.Pattern of arrivals at the system c.Behavior of the arrivals
  • 13. a. Size of the calling population ▪Can be finite / infinite ▪Infinite –Cars arriving at toll booth –Shoppers arriving at a supermarket ▪Finite –Fleet of 4 cars visiting a repair shop –A milling unit with three mills which require service from time to time
  • 14. b. Pattern of arrivals at the system ▪Organized pattern/ random order ▪No. of arrivals per unit time estimated by Poisson distribution P(x) = e-λ λx x! ◦ λ = the average arrival rate per time unit ◦ P(x) = the probability of exactly x arrivals occurring during one time period
  • 15. Arrival rate ▪The average number of customers arriving per time period ▪λ
  • 16. c. Behavior of the arrivals ▪Customer attitude about getting into line differs ▪Balking customer refuse to join the line because it is too long to suit their needs or interest ▪Reneging customer enters the queue but they become impatient and leave without completing their transaction ▪Jockeying customers move from one queue to another hoping to receive service more quickly
  • 17. 2. The Queue discipline or Waiting line ▪Length of line unlimited/ limited ▪A queue is limited when it cannot by law or physical restrictions increase to an infinite length. Ex. Small restaurant ▪A queue is unlimited when its size is unrestricted, as in case of the toll booth serving arriving automobiles
  • 18. Order of service ▪FCFS ▪LCFS ▪SIRO ▪Priority service ▪Pre-emptive priority ▪Non pre-emptive priority
  • 19. 3. The Service mechanism ◦ Two aspects a.Structure of the service system b.Speed of service
  • 20. Structure of the service system How the service facilities are arranged i.A single facility ii.1 queue – several service facility iii.Multichannel facility iv.Multi-stage channel facility
  • 21. b. Speed of service Speed of service can be expressed in 2 ways: i.Service rate –No. of customers during a particular period of time ii.Service time –The amount of time required to service a customer –Generally assumed to be exponentially distributed about average service time
  • 22. Service rate ▪The average number of customers that can be served per time period ▪μ
  • 23. 4. The Outlet of the queue ▪No problem in single channel facility whereas, ▪Problem in multistage channel facility ▪Service station breakdown can have effect on the queues ▪Line before the breakdown will lengthen and the line following the breakdown will diminsh
  • 24. Queuing Models ▪Grouped as ▪Deterministic (Fixed arrival and fixed service time) ▪Probabilistic (Random arrivals) – Use assumptions
  • 25. Kendall’s Notation A/B/m/K/N/D A Arrival Process M: Markovian D: Deterministic Er: Erlang G: General B Service Process M: Markovian D: Deterministic Er: Erlang G: General m Number of servers m=1,2,… K Storage Capacity K= 1,2,… (if œ †hen i† is omified) N Number of customers N= 1,2,… (for closed networks otherwise it is omitted) D Queueing Discipline FCFS,LCFS,SIROetc. Default is FCFS
  • 26. Commonly used Queueing models Model Queue characteristic Example M/M/1/∞ Single server, discouraged Arrivals Single help Desk or counter in store M/M/m/∞ m servers, infinite number of waiting positions Airline ticket counter M/M/m/m m server loss system, no waiting Automated car wash facility M/M/1/K Single server queue with K waiting positions Mechanic shop M/M/1/-/N Single server, infinite number of waiting positions, finite customer population N Receiving degree in Convocation