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Spreadsheet Modeling & Decision Analysis. A Practical Introduction to Management Science 4th edition Cliff T. Ragsdale. Chapter 13. Queuing Theory. Introduction to Queuing Theory. It is estimated that Americans spend a total of 37 billion hours a year waiting in lines.

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Spreadsheet modeling decision analysis

Spreadsheet Modeling & Decision Analysis

A Practical Introduction to Management Science

4th edition

Cliff T. Ragsdale


Queuing theory

Chapter 13

Queuing Theory


Introduction to queuing theory
Introduction to Queuing Theory

  • It is estimated that Americans spend a total of 37 billion hours a year waiting in lines.

  • Places we wait in line...

    ▪ stores ▪ hotels ▪ post offices

    ▪ banks ▪ traffic lights ▪ restaurants

    ▪ airports ▪ theme parks ▪ on the phone

  • Waiting lines do not always contain people...

    ▪ returned videos

    ▪ subassemblies in a manufacturing plant

    ▪ electronic message on the Internet

  • Queuing theory deals with the analysis and management of waiting lines.


The purpose of queuing models
The Purpose of Queuing Models

  • Queuing models are used to:

    • describe the behavior of queuing systems

    • determine the level of service to provide

    • evaluate alternate configurations for providing service


Queuing costs
Queuing Costs

$

Total Cost

Cost of providing service

Cost of customer dissatisfaction

Service Level


Common queuing system configurations

CustomerArrives

CustomerLeaves

...

Server

Waiting Line

CustomerLeaves

Server 1

CustomerArrives

...

CustomerLeaves

Server 2

Waiting Line

CustomerLeaves

Server 3

CustomerLeaves

...

Server 1

Waiting Line

CustomerArrives

...

CustomerLeaves

Waiting Line

Server 2

...

CustomerLeaves

Waiting Line

Server 3

Common Queuing System Configurations


Characteristics of queuing systems the arrival process

Characteristics of Queuing Systems:The Arrival Process

  • Arrival rate - the manner in which customers arrive at the system for service.

  • where l is the arrival rate (e.g., calls arrive at a rate of l=5 per hour)

  • See file Fig13-3.xls


Characteristics of queuing systems the service process

Characteristics of Queuing Systems:The Service Process

  • Service time - the amount of time a customer spends receiving service (not including time in the queue).

  • where mis the service rate (e.g., calls can be serviced at a rate ofm=7 per hour)

  • The average service time is 1/m.

  • See file Fig13-4.xls


Comments
Comments variable:

  • If arrivals follow a Poisson distribution with mean l, interarrival times follow an Exponential distribution with mean 1/l.

    • Example

      • Assume calls arrive according to a Poisson distribution with mean l=5 per hour.

      • Interarrivals follow an exponential distribution with mean 1/5 = 0.2 per hour.

      • On average, calls arrive every 0.2 hours or every 12 minutes.

  • The exponential distribution exhibits the Markovian (memoryless) property.


Kendall notation
Kendall Notation variable:

  • Queuing systems are described by 3 parameters:

    1/2/3

    • Parameter 1

      M = Markovian interarrival times

      D = Deterministic interarrival times

    • Parameter 2

      M = Markovian service times

      G = General service times

      D = Deterministic service times

    • Parameter 3

      A number Indicating the number of servers.

  • Examples,

    M/M/3 D/G/4 M/G/2


Operating characteristics
Operating Characteristics variable:

Typical operating characteristics of interest include:

U - Utilization factor, % of time that all servers are busy.

P0 - Prob. that there are no zero units in the system.

Lq- Avg number of units in line waiting for service.

L - Avg number of units in the system (in line & being served).

Wq- Avg time a unit spends in line waiting for service.

W - Avg time a unit spends in the system (in line & being served).

Pw- Prob. that an arriving unit has to wait for service.

Pn- Prob. of n units in the system.


Key operating characteristics of the m m 1 model
Key variable: Operating Characteristics of the M/M/1 Model


The q xls queuing template
The Q.xls Queuing Template variable:

  • Formulas for the operating characteristics of a number of queuing models have been derived analytically.

  • An Excel template called Q.xls implements the formulas for several common types of models.

  • Q.xls was created by Professor David Ashley of the Univ. of Missouri at Kansas City.


The m m s model
The M/M/s Model variable:

  • Assumptions:

    • There are s servers.

    • Arrivals follow a Poisson distribution and occur at an average rate of l per time period.

    • Each server provides service at an average rate of m per time period, and actual service times follow an exponential distribution.

    • Arrivals wait in a single FIFO queue and are serviced by the first available server.

    • l< sm.


An m m s example bitway computers
An M/M/s Example: Bitway Computers variable:

  • The customer support hotline for Bitway Computers is currently staffed by a single technician.

  • Calls arrive randomly at a rate of 5 per hour and follow a Poisson distribution.

  • The technician services calls at an average rate of 7 per hour, but the actual time required to handle a call follows an exponential distribution.

  • Bitway’s president, Rod Taylor, has received numerous complaints from customers about the length of time they must wait “on hold” for service when calling the hotline.

    Continued…


Bitway computers continued
Bitway Computers ( variable: continued)

  • Rod wants to determine the average length of time customers currently wait before the technician answers their calls.

  • If the average waiting time is more than 5 minutes, he wants to determine how many technicians would be required to reduce the average waiting time to 2 minutes or less.


Implementing the model
Implementing the Model variable:

See file Q.xls


Summary of results bitway computers
Summary of Results: Bitway Computers variable:

Arrival rate 5 5

Service rate 7 7

Number of servers 1 2

Utilization 71.43% 35.71%

P(0), probability that the system is empty 0.2857 0.4737

Lq, expected queue length 1.7857 0.1044

L, expected number in system 2.5000 0.8187

Wq, expected time in queue 0.3571 0.0209

W, expected total time in system 0.5000 0.1637

Probability that a customer waits 0.7143 0.1880


The m m s model with finite queue length
The M/M/s Model With Finite Queue Length variable:

  • In some problems, the amount of waiting area is limited.

  • Example,

    • Suppose Bitway’s telephone system can keep a maximum of 5 calls on hold at any point in time.

    • If a new call is made to the hotline when five calls are already in the queue, the new call receives a busy signal.

    • One way to reduce the number of calls encountering busy signals is to increase the number of calls that can be put on hold.

    • If a call is answered only to be put on hold for a long time, the caller might find this more annoying than receiving a busy signal.

    • Rod wants to investigate what effect adding a second technician to answer hotline calls has on:

      • the number of calls receiving busy signals

      • the average time callers must wait before receiving service.


Implementing the model1
Implementing the Model variable:

See file Q.xls


Summary of results bitway computers with finite queue
Summary of Results: variable: Bitway Computers With Finite Queue

Arrival rate 5 5

Service rate 7 7

Number of servers 1 2

Maximum queue length 5 5

Utilization 68.43% 35.69%

P(0), probability that the system is empty 0.3157 0.4739

Lq, expected queue length 1.0820 0.1019

L, expected number in system 1.7664 0.8157

Wq, expected time in queue 0.2259 0.0204

W, expected total time in system 0.3687 0.1633

Probability that a customer waits 0.6843 0.1877

Probability that a customer balks 0.0419 0.0007


The m m s model with finite population
The M/M/s variable: Model With Finite Population

  • Assumptions:

    • There are s servers.

    • There are N potential customers in the arrival population.

    • The arrival pattern of each customer follows a Poisson distribution with a mean arrival rate of l per time period.

    • Each server provides service at an average rate of m per time period, and actual service times follow an exponential distribution.

    • Arrivals wait in a single FIFO queue and are serviced by the first available server.


M m s with finite population example the miller manufacturing company
M/M/s With Finite Population Example: variable: The Miller Manufacturing Company

  • Miller Manufacturing owns 10 identical machines that produce colored nylon thread for the textile industry.

  • Machine breakdowns follow a Poisson distribution with an average of 0.01 breakdowns per operating hour per machine.

  • The company loses $100 each hour a machine is down.

  • The company employs one technician to fix these machines.

  • Service times to repair the machines are exponentially distributed with an avg of 8 hours per repair. (So service is performed at a rate of 1/8 machines per hour.)

  • Management wants to analyze the impact of adding another service technician on the average time to fix a machine.

  • Service technicians are paid $20 per hour.


Implementing the model2
Implementing the Model variable:

See file Q.xls


Summary of results miller manufacturing
Summary of Results: Miller Manufacturing variable:

Arrival rate 0.01 0.01 0.01

Service rate 0.125 0.125 0.125

Number of servers 1 2 3

Population size 10 10 10

Utilization 67.80% 36.76% 24.67%

P(0), probability that the system is empty 0.3220 0.4517 0.4623

Lq, expected queue length 0.8463 0.0761 0.0074

L, expected number in system 1.5244 0.8112 0.7476

Wq, expected time in queue 9.9856 0.8282 0.0799

W, expected total time in system 17.986 8.8282 8.0799

Probability that a customer waits 0.6780 0.1869 0.0347

Hourly cost of service technicians $20.00 $40.00 $60.00

Hourly cost of inoperable machines $152.44 $81.12 $74.76

Total hourly costs $172.44 $121.12 $134.76


The m g 1 model
The M/G/1 Model variable:

  • Not all service times can be modeled accurately using the Exponential distribution.

    • Examples:

      • Changing oil in a car

      • Getting an eye exam

      • Getting a hair cut

  • M/G/1 Model Assumptions:

    • Arrivals follow a Poisson distribution with mean l.

    • Service times follow any distribution with mean m and standard deviation s.

    • There is a single server.


An m g 1 example zippy lube
An M/G/1 Example: Zippy Lube variable:

  • Zippy-Lube is a drive-through automotive oil change business that operates 10 hours a day, 6 days a week.

  • The profit margin on an oil change at Zippy-Lube is $15.

  • Cars arrive at the Zippy-Lube oil change center following a Poisson distribution at an average rate of 3.5 cars per hour.

  • The average service time per car is 15 minutes (or 0.25 hours) with a standard deviation of 2 minutes (or 0.0333 hours).

    Continued…


Zippy lube continued
Zippy Lube ( variable: continued)

  • A new automated oil dispensing device costs $5,000.

  • The manufacturer's representative claims this device will reduce the average service time by 3 minutes per car. (Currently, employees manually open and pour individual cans of oil.)

  • The owner wants to analyze the impact the new automated device would have on his business and determine the pay back period for this device.


Implementing the model3
Implementing the Model variable:

See file Q.xls


Summary of results zippy lube
Summary of Results: Zippy Lube variable:

Arrival rate 3.5 3.5 4.371

Average service TIME 0.25 0.2 0.2

Standard dev. of service time 0.0333 0.0333 0.333

Utilization 87.5% 70.0% 87.41%

P(0), probability that the system is empty 0.1250 0.3000 0.1259

Lq, expected queue length 3.1168 0.8393 3.1198

L, expected number in system 3.9918 1.5393 3.9939

Wq, expected time in queue 0.8905 0.2398 0.7138

W, expected total time in system 1.1405 0.4398 0.9138


Payback period calculation
Payback Period Calculation variable:

Increase in:

Arrivals per hour 0.871

Profit per hour $13.06

Profit per day $130.61

Profit per week $783.63

Cost of Machine $5,000

Payback Period 6.381 weeks


The m d 1 model
The M/D/1 Model variable:

  • Service times may not be random in some queuing systems.

    • Examples

      • In manufacturing, the time to machine an item might be exactly 10 seconds per piece.

      • An automatic car wash might spend exactly the same amount of time on each car it services.

  • The M/D/1 model can be used in these types of situations where the service times are deterministic (not random).

  • The results for an M/D/1 model can be obtained using the M/G/1 model by setting the standard deviation of the service time to 0 ( s= 0).


Simulating queues
Simulating Queues variable:

  • The queuing formulas used in Q.xls describe the steady-state operations of the various queuing systems.

  • Simulation is often used to analyze more complex queuing systems.

  • See file Fig13-21.xls



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