Review of Probability Distributions

1 Slide

Review of Probability Distributions

Probability distribution is a theoretical frequency distribution.

Example 1.If you throw a fair die (numbered 1 through 6). What is the probability that you get a 1? or a 5?

Example 2.If you throw a fair coin twice. What is the probability that you get two tails?

2 Slide

A variable can be discrete or continuous

A variable is discrete if it takes on a limited number of values, which can be listed.Example: Poisson distributionOther examples:

A variable is continuous if it can take any value within a given range.Example: Exponential distribution.Other examples:

Discrete vs. Continuous distributions

3 Slide

Poisson Distribution A Poisson distribution is a discrete distribution

that can take an integer value > 0 (i.e., 0, 1, 2, 3, ….)

Formula• P(x) = (lx e –l)/x! (where e = natural

logarithm or 2.718, and x! = x factorial)

Example• l = 3• What is P(x = 0)?

• What is P(x = 2)?

4 Slide

Exponential Distribution

An exponential distribution is a continuous random variable that can take on any positive value.

Formula: f(x) = l e (-lx) ; F(x) = P(X < x) = 1- e (-lx)

for l > 0, and 0 < x < infinity. Example: l = 3

f(x=5) =

F(x=5)

5 Slide

Relationship between Poisson distribution and Exponential distribution

Poisson distribution and exponential distribution are used to describe the same random process.

Poisson distribution describes the probability that there is/are x occurrence/s per given time period.

Exponential distribution describes the probability that the time between two consecutive occurrence is within a certain number x.

ExampleIf the arrival rate of customers are Poisson distributed and, say, 6 per hour, then the time between arrivals of customers are exponentially distributed with a mean of (1/6) hour or 10 minutes.

6 Slide

Class ExerciseSuppose the arrival rate of customers is 10 per hour, Poisson distributed

What is the probability that 2 customers are arrival in one hour?

What is the average inter-arrival time of customers?

What is the probability that the inter-arrival time of customers is exactly 3 minutes?

What is the probability that the inter-arrival time of customers is less than or equal to 3 minutes?

7 Slide

Class ExerciseSuppose the arrival rate of customers is 10 per hour, Poisson distributed

What is the probability that 2 customers are arrival in one hour?

What is the average inter-arrival time of customers?

What is the probability that the inter-arrival time of customers is exactly 3 minutes?

What is the probability that the inter-arrival time of customers is less than or equal to 3 minutes?

8 Slide

Chapter 11: Waiting Line Models

Structure of a Waiting Line System Queuing Systems Queuing System Input Characteristics Queuing System Operating Characteristics Analytical Formulas Single-Channel Waiting Line Model with

Poisson Arrivals and Exponential Service Times

Single-Channel Waiting Line Model with Poisson Arrivals and Constant Service Times

Multiple-Channel Waiting Line Model with Poisson Arrivals and Exponential Service Times

Economic Analysis of Waiting Lines

9 Slide

Queuing theory is the study of waiting lines. Four characteristics of a queuing system are:

• the manner in which customers arrive• the time required for service• the priority determining the order of

service• the number and configuration of servers

in the system.

Structure of a Waiting Line System

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Structure of a Waiting Line System Distribution of Arrivals

• Generally, the arrival of customers into the system is a random event.

• Frequently the arrival pattern is modeled as a Poisson process

Distribution of Service Times• Service time is also usually a random variable.

• A distribution commonly used to describe

service time is the exponential distribution. Queue Discipline

• Most common queue discipline is first come, first served (FCFS).

• What is the queue discipline in elevators?

11 Slide

Structure of a Waiting Line System

Single Service Channel

Multiple Service Channels

S1

S1

S2

S3

Customerleaves

Customerleaves

Customerarrives

Customerarrives

Waiting line

Waiting line

System

System


Multiple Service Channels

12 Slide

Steady-State Operation When a business like a restaurant opens in

the morning, no customers are in the restaurant.

Gradually, activity builds up to a normal or steady state.

The beginning or start-up period is referred to as the transient period.

The transient period ends when the system reaches the normal or steady-state operation.

Waiting line/Queueing models describe the steady-state operating characteristics of a waiting line.

13 Slide

Queuing Systems

A three part code of the form A/B/k is used to describe various queuing systems.

A identifies the arrival distribution, B the service (departure) distribution, and k the number of identical servers for the system.

Symbols used for the arrival and service processes are: M - Markov distributions (Poisson/exponential), D - Deterministic (constant) and G - General distribution (with a known mean and variance). For example, M/M/k refers to a system in which arrivals occur according to a Poisson distribution, service times follow an exponential distribution and there are k servers working at identical service rates.

14 Slide

Analytical Formulas

When the queue discipline is FCFS, analytical formulas have been derived for several different queuing models including the following: • M/M/1• M/D/1• M/M/k

Analytical formulas are not available for all possible queuing systems. In this event, insights may be gained through a simulation of the system.

15 Slide

Queuing Systems Assumptions The arrival rate is l and arrival process is

Poisson There is one line/channel The service rate, m, is per server (even for

M/M/K). The queue discipline is FCFS Unlimited maximum queue length Infinite calling population Once the customers arrive they do not leave

the system until they are served

16 Slide

Queuing System Input Characteristics

l = the arrival rate 1/l = the average time between arrivals µ = the service rate for each server 1/µ = the average service time = the standard deviation of the service time

Suppose the arrival rate, l, is 6 per hour.What is the average time between arrivals?

17 Slide

Relationship between L and Lq and W and Wq.


S1Customer

leavesCustomer

arrives

System

How many customers are waiting in the queue?

How many customers are in the system?

Suppose a customer waits for 10 minutes before she is served and the service time takes another 5 minutes.What is the waiting time in the queue?What is the waiting time in the system?

18 Slide

Queuing System Operating Characteristics

P0 = probability the service facility is idle Pn = probability of n units in the system Pw = probability an arriving unit must wait for service Lq = average number of units in the queue

awaiting service L = average number of units in the system Wq = average time a unit spends in the queue awaiting service W = average time a unit spends in the system

19 Slide

M/M/1 Operating Characteristics

P0 = 1 – l/m Pn = (l/m)n P0 = (l/m)n (1 – l/m) Pw = l/m Lq = l2 /{m(m – l)} L = Lq + l/m = l /(m – l) Wq = Lq/l = l /{m(m – l)} W = Wq + 1/m = 1 /(m – l)

20 Slide

Some General Relationships for Waiting Line Models (M/M/1, M/D/1, and M/M/K)

Little's flow equations are:

L = lW and Lq = lWq

Little’s flow equations show how operating characteristics L, Lq, W, and Wq are related in

any waiting line system. Arrivals and service times

do not have to follow specific probability

distributions for the flow equations to be applicable.

21 Slide

Single-Channel Waiting Line Model

M/M/1 queuing system Number of channels = Arrival process = Service-time distribution = Queue length = Calling population = Customer leave the system without service?

Examples:• Single-window theatre ticket sales booth• Single-scanner airport security station

22 Slide

Example: SJJT, Inc. (A)

M/M/1 Queuing SystemJoe Ferris is a stock trader on the floor of

the NewYork Stock Exchange for the firm of Smith, Jones,Johnson, and Thomas, Inc. Daily stock transactions arrive at Joe’s desk at a rate of 20 per hour, Poisson distributed. Each order received by Joe requires an average of two minutes to process, exponentially distributed. Joe processes these transactions in FCFS order.

23 Slide


What is the probability that an arriving order does not have to wait to be processed?

What percentage of the time is Joe processing orders?

24 Slide


What is the probability that Joe has exactly 3 orders waiting to be processed?

What is the probability that Joe has at least 2 orders in the system?

25 Slide

Example: SJJT, Inc. (A) What is the average time an order must wait

from the time Joe receives the order until it is finished being processed (i.e. its turnaround time)?

What is the average time an order must wait from before Joe starts processing it?

26 Slide

Example: SJJT, Inc. (A) What is the average number of orders Joe has

waiting to be processed?

What is the average number of orders in the system?

27 Slide

M/D/1 queuing system Single channel Poisson arrival-rate distribution Constant service time Unlimited maximum queue length Infinite calling population Examples:

• Single-booth automatic car wash• Coffee vending machine

Single-Channel Waiting Line Model with Poisson Arrivals and Constant Service

Times

28 Slide

M/D/1 Operating Characteristics

P0 = 1 – l/m Pw = l/m Lq = l2 /{2m(m – l)} L = Lq + l/m Wq = Lq/l = l /{2m(m – l)} W = Wq + 1/m

29 Slide

Example: SJJT, Inc. (B)

M/D/1 Queuing SystemThe New York Stock Exchange the firm of

Smith, Jones, Johnson, and Thomas, Inc. now has an opportunity to purchase a new machine that can process the transactions in exactly 2 minutes. Instead of using Joe, the company would like to evaluate the impact of using the new machine. Daily stock transactions still arrive at a rate of 20 per hour, Poisson distributed.

30 Slide

Example: SJJT, Inc. (B) What is the average time an order must wait

from the time the order arrives until it is finished being processed (i.e. its turnaround time)?

What is the average time an order must wait from before machine starts processing it?

31 Slide

Example: SJJT, Inc. (B) What is the average number of orders waiting

to be processed?

What is the average number of orders in the system?

32 Slide

Improving the Waiting Line Operation

Waiting line models often indicate when improvements in operating characteristics are desirable.

To make improvements in the waiting line operation, analysts often focus on ways to improve the service rate by:

- Increasing the service rate by making a creative

design change or by using new technology.

- Adding one or more service channels so that more

customers can be served simultaneously.

33 Slide

M/M/k queuing system Multiple channels (with one central waiting

line) Poisson arrival-rate distribution Exponential service-time distribution Unlimited maximum queue length Infinite calling population Examples:

• Four-teller transaction counter in bank• Two-clerk returns counter in retail store

Multiple-Channel Waiting Line Model withPoisson Arrivals and Exponential Service

Times

34 Slide

M/M/k Example: SJJT, Inc. (C)

M/M/2 Queuing SystemSmith, Jones, Johnson, and Thomas, Inc.

has begun a major advertising campaign which it believes will increase its business 50%. To handle the increased volume, the company has hired an additional floor trader, Fred Hanson, who works at the same speed as Joe Ferris.

Note that the new arrival rate of orders, l , is 50% higher than that of problem (A). Thus, l = 1.5(20) = 30 per hour.

35 Slide


Sufficient Service Rate: l > kmQuestion

Will Joe Ferris alone not be able to handle the increase in orders?Answer

Since Joe Ferris processes orders at a mean rate of µ = 30 per hour, then l = µ = 30 and the utilization factor is 1.

This implies the queue of orders will grow infinitely large. Hence, Joe alone cannot handle this increase in demand.

36 Slide

M/M/k Example: SJJT, Inc. (C) Probability of No Units in System (continued)

Given that l = 30, µ = 30, k = 2 and (l /µ) = 1, theprobability that neither Joe nor Fred will be working

is: =

P

n kk

k

n k

n

k0

0

11

=

=

( / )!

( / )! ( )l m l m m

m l

What is the probability that neither Joe nor Fred will be working on an order at any point in time?

37 Slide


Probability of n Units in System

knforPn

Pn

n = __!)/(

0ml

knforPkk

P kn

n

n >= __!

)/(0)(

ml

38 Slide

Example: SJJT, Inc. (C)

Average Length of the Queue

The average number of orders waiting to be filled with both Joe and Fred working is 1/3.

2

02 2( ) (30)(30)(30 30) 1( ) (1/ 3)( 1)!( ) (1!)(2(30) 30) 3

k

qL Pk klm l m

m l= = =

Average Length of the systemL = Lq + (l /µ) =

39 Slide


Average Time in QueueWq = Lq /l =

Average Time in System

W = L/l =

QuestionWhat is the average turnaround time for

an order with both Joe and Fred working?

40 Slide


Economic Analysis of Queuing SystemsThe advertising campaign of Smith,

Jones, Johnson and Thomas, Inc. (see problems (A) and (B)) was so successful that business actually doubled. The mean rate of stock orders arriving at the exchange is now 40 per hour and the company must decide how many floor traders to employ. Each floor trader hired can process an order in an average time of 2 minutes.

41 Slide


Economic Analysis of Queuing Systems Based on a number of factors the

brokerage firm has determined the average waiting cost per minute for an order to be $.50. Floor traders hired will earn $20 per hour in wages and benefits. Using this information compare the total hourly cost of hiring 2 traders with that of hiring 3 traders.

42 Slide

Economic Analysis of Waiting Lines

The total cost model includes the cost of waiting and

the cost of service.

TC = cwL csk where: cw = the waiting cost per time period for each unit L = the average number of units in the system cs = the service cost per time period for each channel k = the number of channels TC = the total cost per time period

43 Slide


Economic Analysis of Waiting LinesTotal Hourly Cost = (Total hourly cost for orders in the system)

+ (Total salary cost per hour) = ($30 waiting cost per hour)

x (Average number of orders in the system)

+ ($20 per trader per hour) x (Number of traders) = 30L + 20k

Thus, L must be determined for k = 2 traders and for k = 3 traders with l = 40/hr. and m = 30/hr. (since the average service time is 2 minutes (1/30 hr.).

44 Slide


Cost of Two Servers

P0 = 1 / [1+(1/1!)(40/30)]+[(1/2!)(40/30)2(60/(60-40))]

= 1 / [1 + (4/3) + (8/3)] = 1/5

P

n kk

k

n k

n

k0

0

11

=

=

( / )!

( / )! ( )l m l m m

m l

45 Slide


Cost of Two Servers (continued)Thus,

L = Lq + (l /µ) = 16/15 + 4/3 = 2.40 Total Cost = 30(2.40) + (20)(2) = $112.00 per hour

2

02 2( ) (40)(30)(40 30) 16( ) (1/ 5)( 1)!( ) (1!)(2(30) 40) 15

k

qL Pk klm l m

m l= = =

46 Slide


Cost of Three Servers

P0 =

P

n kk

k

n k

n

k0

0

11

=

=

( / )!

( / )! ( )l m l m m

m l

47 Slide


Cost of Three Servers (continued)

Thus, L = .1446 + 40/30 = 1.4780 Total Cost = 30(1.4780) + (20)(3) = $104.35

per hour

3

02 2( ) (30)(40)(40 30)( ) (15/ 59) .1446( 1)!( ) (2!)(3(30) 40)

k

qL Pk k

lm l mm l

= = =

48 Slide


System Cost Comparison Waiting Wage

TotalCost/Hr Cost/Hr

Cost/Hr2 Traders $82.00 $40.00

$112.003 Traders 44.35 60.00

104.35 Thus, the cost of having 3 traders is less than that of 2 traders.

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Review of Probability Distributions