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Queueing Theory

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Submitted By kayemosqueda
Words 786
Pages 4
Multiple-Server
Waiting Line
Model
with Poisson
Arrivals and
Exponential
Service Times
Rechell Kaye B. Mosqueda, CPA

Multiple-Server Waiting Line Model
A multiple-server waiting line consists of two or more servers that are assumed to be identical in terms of service capability.
For multipleserver systems, there are two typical queuing possibilities: (1) arriving customers wait in a single waiting line
(called a “pooled” or “shared” queue) and then move to the first available server for processing,
(2) each server has a “dedicated” queue and an arriving customer selects one of these lines to join
(and typically is not allowed to switch lines).

Multiple-Server Waiting Line Model
The
formulas for M/M/S are applicable if the following conditions exist: The arrivals follow a Poisson probability distribution. The service time for each server follows an exponential probability distribution.
The service rate µ is the same for each server. The arrivals wait in a single waiting line and then move to the first open server for service. Multiple-Server Waiting Line Model
Important
Consideration

The average service rate must always exceed the average arrival rate.

µ>
Otherwise, the queue will grow to infinity.

Operating Characteristics

k

number of servers



average arrival rate

µ

average service rate for each server

Operating Characteristics
The probability that no units are in the system:
The average number of units in the waiting line:
The average number of units in the system:

=


=

λ⁄
!

+

1 λ⁄ !

λ⁄
− 1 !(

=



− )

+

Operating Characteristics
The average time a unit spends in the waiting line:

=

The average time a unit spends in the system: The probability that an arriving unit has to wait for service:

=
=

!

( ) (

+

1

)

Operating Characteristics
The probability of n units in the system: =
=

( ⁄ )
!
( ⁄ )
)
! (

for n ≤ k for n > k

Operating Characteristics
Values of P0 for MultipleServer Waiting
Lines with
Poisson
Arrivals and
Exponential
Service Times

Example: Burger Dome
Burger Dome Restaurant sells hamburgers, cheeseburgers, French fries, soft drinks, and milk shakes, as well as a limited number of specialty items and dessert selections. Although Burger Dome would like to serve each customer immediately, at times more customers arrive than can be handled by the
Burger Dome food service staff. Thus, customers wait in line to place and receive their orders. Burger Dome is concerned that the methods currently used to serve customers are resulting in excessive waiting times and a possible loss of sales. Management wants to conduct a waiting line study to help determine the best approach to reduce waiting times and improve service. Suppose that Burger Dome analyzed data on customer arrivals and concluded that the arrival rate is 45 customers per hour. For a one-minute period, the arrival rate would be 45 customers/60 minutes = 0.75 customers per minute. Suppose that Burger Dome studied the order-filling process and found that a single employee can process an average of 60 customer orders per hour. On a one-minute basis, the service rate would be 60 customers/60 minutes = 1 customer per minute.

Example: Burger Dome

For an arrival rate of 0.75 customers per minute and a service rate of 1 customer per minute for each of the two servers, (λ = 0.75, µ = 1, k = 2) we obtain the operating characteristics: Example: Burger Dome
The probability that no units are in the system:
The average number of units in the waiting line:
The average number of units in the system:

= 0.4545
(based on the table)
.75⁄1 75 1
=
2 − 1 ! [(2)(1 − .75)
= .1227 customer

.75
L = .1227 +
1
= .8727 customer

.4545

Example: Burger Dome
Values of P0 for MultipleServer Waiting
Lines with
Poisson
Arrivals and
Exponential
Service Times

Example: Burger Dome
The average time a unit spends in the waiting line:

0.1227
=
= 0.1636
0.75

The average time a unit spends in the system: 1
= .1636 +
1
= 1.1636

The probability that an arriving unit has to wait for service:

1 .75
2 1
=
( )
. 4545
2! 1
2 1 − .75
= .2045 or 20.45%

Example: Burger Dome
The probability of n units in the system: Single-server versus Multiple server
We can now compare the steady-state operating characteristics of the two-server system to the operating characteristics of the original single-server system
Operating Characteristics

Single-server

Multiple Server

The average time a customer spends in the system (waiting time plus service time)

W = 4 minutes

W = 1.1636 minutes

The average number of customers
Lq = 2.25 customers in the waiting line
The average time a customer
Wq = 3 minutes spends in the waiting line
The probability that a customer has
Pw = 0.75 to wait for service

Lq = 0.1227 customers
Wq = 0.1636 minutes
Pw = 0.2045

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