Simulation is a technique usually reserved for studying only the simplest and most straightforward of problems.

1. True

2. False

A simulation model is designed to arrive at a single specific numerical answer to a given problem.

1. True

2. False

Simulation typically requires a familiarity with statistics to evaluate the results.

1. True

2. False

The verification process involves making sure that

1. the model adequately represents the real-world system.

2. the model is internally consistent and logical.

3. the correct random numbers are used.

4. enough trial runs are simulated.

The validation process involves making sure that

1. the model adequately represents the real-world system.

2. the model is internally consistent and logical.

3. the correct random numbers are used.

4. enough trial runs are simulated.

Which of the following is an advantage of simulation?

1. It allows time compression.

2. It is always relatively simple and inexpensive.

3. The results are usually transferable to other problems.

4. It will always find the optimal solution to a problem.

Which of the following is a disadvantage of simulation?

1. It is inexpensive even for the most complex problem.

2. It always generates the optimal solution to a problem.

3. The results are usually transferable to other problems.

4. Managers must generate all of the conditions and constraints for solutions that they wish to examine.

A meteorologist was simulating the number of days that rain would occur in a month. The random number interval from 01 to 30 was used to indicate that rain occurred on a particular day, and the interval 31–00 indicated that rain did not occur. What is the probability that rain did occur?

1. 0.30

2. 0.31

3. 1.00

4. 0.70

Simulation is best thought of as a technique to

1. give concrete numerical answers.

2. increase understanding of a problem.

3. provide rapid solutions to relatively simple problems.

4. provide optimal solutions to complex problems.

When simulating the Monte Carlo experiment, the average simulated demand over the long run should approximate the

1. real demand.

2. expected demand.

3. sample demand.

4. daily demand.

The idea behind simulation is

1. to imitate a real-world situation.

2. to study the properties and operating characteristics of a real-world situation.

3. to draw conclusions and make action decisions based on simulation results.

4. all of the above.

Using simulation for a queuing problem would be appropriate if

1. the arrival rate follows a Poisson distribution.

2. the service rate is constant.

3. the FIFO queue discipline is assumed.

4. there is a 10% chance an arrival would leave before receiving service.

A probability distribution has been developed, and the probability of 2 arrivals in the next hour is 0.20. A random number interval is to be assigned to this. Which of the following would not be an appropriate interval?

1. 01–20

2. 21–40

3. 00–20

4. 00–19

5. All of the above would be appropriate.

In a Monte Carlo simulation, a variable that we might want to simulate is

1. lead time for inventory orders to arrive.

2. time between machine breakdowns.

3. time between arrivals at a service facility.

4. number of employees absent from work each day.

5. all of the above.

Use the following random numbers to simulate yes and no answers to 10 questions by starting in the first row and letting

1. the double-digit numbers 00–49 represent yes and 50–99 represent no.

2. the double-digit even numbers represent yes and the odd numbers represent no.

   Random numbers: 52 06 50 88 53 30 10 47 99 37 66 91 35 32 00 84 57 00