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Queuing Theory – Study of Congestion Operations Research
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Page 1: Queuing Theory

Queuing Theory – Study of Congestion

Operations Research

Page 2: Queuing Theory

What is a queue?

• People waiting for service – Customers at a supermarket (IVR, railway counter)– Letters in a post office (Emails, SMS)– Cars at a traffic signal

• In ordered fashion (who defines order?)– Bank provides token numbers– Customers themselves ensure FIFO at Railway

ticket counters

Page 3: Queuing Theory

Where do we find queues?

Page 4: Queuing Theory

A thought experiment• When does a queue form?

• When will it not form?

• When will you not join a queue?

• When will you leave a queue?

• What is the worst case scenario?

Page 5: Queuing Theory

What do you observe near a queue?

• Conflict• Congestion• Idle counters• Overworked counters• Smart people trying to circumvent the queue

Page 6: Queuing Theory

What do we want to know?

• How much time will it take?• How many counters should be there?• How to manage peak hour traffic?

Page 7: Queuing Theory

Origins

• Queuing Theory had its beginning in the research work of a Danish engineer named A. K. Erlang.

• In 1909 Erlang experimented with fluctuating demand in telephone traffic.

• Eight years later he published a report addressing the delays in automatic dialing equipment.

• At the end of World War II, Erlang’s early work was extended to more general problems and to business applications of waiting lines.

Page 8: Queuing Theory

Queuing System

Page 9: Queuing Theory

Kendall Notation (a/b/c : d/e/f)

(a/b/c : d/e/f)

Arrival Distribution

Service Time Distribution

Number of concurrent

servers

Service Discipline

Maximum number of

customers in system

Size of source

M/D

M/D

nFIFO/LIFO/

Priority/ Random

n

Infinite/ finite

Page 10: Queuing Theory

Identify the queuing system

Railway ticket counter (M/D/3:FIFO/200/∞)

Bank Service Counter

ATM

Airport – Check In

Airport - Security

Traffic Signal

Bus Stop

Train Platform (Boarding)

Paper Correction

Page 11: Queuing Theory
Page 12: Queuing Theory

Arrival modeled using Poisson Distribution

Page 13: Queuing Theory

Some parameters

Arrival Rate λ

Service Rate μ

Number of customers in system Ls

Number of customers in queue Lq

Waiting time in system Ws

Waiting time in queue Wq

Utilization ρ

Page 14: Queuing Theory

Types of queuing systems

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M/M/1

Page 20: Queuing Theory

T1=3T2=7T3=10T4=6T5=6T6=6J=38

Arrival Rate = N/Tt=6/19=.31Mean Time in system = J/N = 38/6=6.3Mean number in system = J/Tt=38/19=2 = (J/N)*(N/Tt)=6.3*.31=2 =Tq