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Spreadsheet Modeling & Decision Analysis

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Spreadsheet Modeling & Decision Analysis

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Spreadsheet Modeling & Decision Analysis

A Practical Introduction to Management Science

4th edition

Cliff T. Ragsdale

Chapter 13

- 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.

- Queuing models are used to:
- describe the behavior of queuing systems
- determine the level of service to provide
- evaluate alternate configurations for providing service

$

Total Cost

Cost of providing service

Cost of customer dissatisfaction

Service Level

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

- Arrivals are often described by a Poisson random variable:

- 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

- Service times are often described by an Exponential random variable:

- 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

- 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.

- Example
- The exponential distribution exhibits the Markovian (memoryless) property.

- 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.

- Parameter 1
- Examples,
M/M/3D/G/4M/G/2

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.

- 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.

- 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.

- 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…

- 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.

See file Q.xls

Arrival rate55

Service rate 77

Number of servers 12

Utilization71.43%35.71%

P(0), probability that the system is empty0.2857 0.4737

Lq, expected queue length1.7857 0.1044

L, expected number in system2.5000 0.8187

Wq, expected time in queue0.3571 0.0209

W, expected total time in system0.5000 0.1637

Probability that a customer waits0.7143 0.1880

- 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.

See file Q.xls

Arrival rate 55

Service rate 77

Number of servers12

Maximum queue length55

Utilization68.43%35.69%

P(0), probability that the system is empty0.31570.4739

Lq, expected queue length1.08200.1019

L, expected number in system1.76640.8157

Wq, expected time in queue0.22590.0204

W, expected total time in system0.36870.1633

Probability that a customer waits0.68430.1877

Probability that a customer balks0.04190.0007

- 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.

- 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.

See file Q.xls

Arrival rate 0.010.010.01

Service rate0.1250.1250.125

Number of servers 123

Population size101010

Utilization67.80%36.76%24.67%

P(0), probability that the system is empty0.32200.4517 0.4623

Lq, expected queue length0.84630.0761 0.0074

L, expected number in system1.52440.8112 0.7476

Wq, expected time in queue9.98560.8282 0.0799

W, expected total time in system17.9868.8282 8.0799

Probability that a customer waits0.67800.1869 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

- 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

- Examples:
- 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.

- 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…

- 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.

See file Q.xls

Arrival rate 3.53.54.371

Average service TIME0.250.20.2

Standard dev. of service time0.03330.03330.333

Utilization87.5%70.0%87.41%

P(0), probability that the system is empty0.12500.30000.1259

Lq, expected queue length3.11680.83933.1198

L, expected number in system3.99181.53933.9939

Wq, expected time in queue0.89050.23980.7138

W, expected total time in system1.14050.43980.9138

Increase in:

Arrivals per hour0.871

Profit per hour$13.06

Profit per day$130.61

Profit per week$783.63

Cost of Machine$5,000

Payback Period6.381 weeks

- 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.

- Examples
- 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).

- 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