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| 1 | |
The expected time in a system should equal the expected time in queue plus the expected time in service. |
| | A) | True |
| | B) | False |
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| 2 | |
For a queuing system to be feasible in the long run, the arrival rate must exceed the rate of service. |
| | A) | True |
| | B) | False |
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| 3 | |
Only two sources of empirical data are necessary for using queuing models, the mean arrival rate and the mean service rate. |
| | A) | True |
| | B) | False |
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| 4 | |
Although some service systems may be in a transient mode for a long time, steady state models can be useful for long-range capacity planning decisions. |
| | A) | True |
| | B) | False |
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| 5 | |
Little's law describes the relationship between the length of a queue and the probability that a customer will balk. |
| | A) | True |
| | B) | False |
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| 6 | |
Queuing models can be used for a variety of purposes, such as estimating the probability that a customer will wait more than a certain length of time. |
| | A) | True |
| | B) | False |
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| 7 | |
Which of the following is not a determinant of a service's capacity? |
| | A) | Physical facility |
| | B) | Free cash flow |
| | C) | Number of employees |
| | D) | Type and amount of equipment |
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| 8 | |
Which of the following is not a reason that strategic capacity planning is difficult for services? |
| | A) | Services must balance their desire for high utilization with the customer's desire for prompt service. |
| | B) | Since physical capacity is added in discrete units, demand must be forecasted far into the future and money must be invested long before it generates sufficient revenue. |
| | C) | It is often difficult for services to control the demands placed upon them. |
| | D) | There have been no formal models developed that deal with strategic capacity planning issues. |
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| 9 | |
Which of the following is a shortcoming of the naïve approach to capacity planning? |
| | A) | It can be too simplistic to capture the full scope of the process. |
| | B) | It does not take into account varying arrival rates. |
| | C) | It results in capacity that matches, but does not exceed, demand. |
| | D) | All of the above |
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| 10 | |
Queuing models use an A/B/C notation. What do these variables represent? |
| | A) | distribution of time between arrivals/distribution of service times/number of parallel servers |
| | B) | average time between arrivals/average service time/number of parallel servers |
| | C) | maximum time between arrivals/maximum allowable service time/maximum number of parallel servers |
| | D) | minimum time between arrivals/minimum allowable service time/minimum number of parallel servers |
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| 11 | |
Assume that the arrival of airplanes at a one-runway airport is a Poisson distribution with a mean rate of = 8 planes per hour. The landing time is an exponential distribution with a mean of 5 minutes per plane. What is the mean number of planes in the system? |
| | A) | 1 |
| | B) | 1.33 |
| | C) | 1.67 |
| | D) | 2 |
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| 12 | |
Theoretically, service capacity must exceed demand, lest queues become infinitely long. If capacity does not exceed demand, what is likely to happen? |
| | A) | Customers will renege or balk, thereby reducing demand. |
| | B) | Servers will decrease time spent per customer, thereby increasing capacity. |
| | C) | Servers will eliminate time-consuming portions of their jobs, thereby increasing capacity. |
| | D) | All of the above |
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