Ch13 | Test Questions & Answers – Supplement Operational

CHAPTER 13 SUPPLEMENT

OPERATIONAL DECISION-MAKING TOOLS: SIMULATION

CHAPTER LEARNING OBJECTIVES

S1. Use the Monte Carlo technique for simulation and calculate expected value. The Monte Carlo technique is a method for selecting numbers randomly from a probability distribution for use in a simulation. The purpose of the Monte Carlo process is to generate the random variable “sampling” from the probability distribution.

S2. Describe numerous ways simulation can be applied in operations. Simulations can be applied to address operational issues in waiting lines/service, inventory management, production and manufacturing systems, capital investing and budgeting, logistics, service operations, and environmental and resource analysis.

TRUE-FALSE STATEMENTS

1. Simulation is a popular decision-making tool that provides a solution to any type of problem.

Difficulty: Easy

Learning Objective: Use the Monte Carlo technique for simulation and calculate expected value.

Section Reference: S13.1 Monte Carlo Simulation

2. Simulation is often viewed as the technique of last resort because it can be applied to situations when there is no applicable quantitative model.

Difficulty: Medium

Learning Objective: Use the Monte Carlo technique for simulation and calculate expected value.

Section Reference: S13.1 Monte Carlo Simulation

3. Because simulation is used to analyze probabilistic problems, it provides information that is used to make a decision versus an optimal solution.

Difficulty: Easy

Learning Objective: Use the Monte Carlo technique for simulation and calculate expected value.

Section Reference: S13.1 Monte Carlo Simulation

4. Simulation is the preferred technique for problems with random variables represented by probability distributions.

Difficulty: Easy

Learning Objective: Use the Monte Carlo technique for simulation and calculate expected value.

Section Reference: S13.1 Monte Carlo Simulation

5. The Monte Carlo technique selects numbers randomly from a probability distribution for use in a quantitative model.

Difficulty: Medium

Learning Objective: Use the Monte Carlo technique for simulation and calculate expected value.

Section Reference: S13.1 Monte Carlo Simulation

6. The Monte Carlo technique is a mathematical model used within a simulation.

Difficulty: Medium

Learning Objective: Use the Monte Carlo technique for simulation and calculate expected value.

Section Reference: S13.1 Monte Carlo Simulation

7. A random number’s likelihood of being selected is based on a normal distribution.

Difficulty: Easy

Learning Objective: Use the Monte Carlo technique for simulation and calculate expected value.

Section Reference: S13.1 Monte Carlo Simulation

8. A steady state results when a simulation is repeated enough times that the random variable being investigated reaches an average result that remains constant.

Difficulty: Hard

Learning Objective: Use the Monte Carlo technique for simulation and calculate expected value.

Section Reference: S13.1 Monte Carlo Simulation

9. As a simulation model becomes more complex, using a computer application is virtually impossible.

Difficulty: Easy

Learning Objective: Describe numerous ways simulation can be applied in operations.

Section Reference: S13.2 Areas of Simulation Application

10. An advantage of using a computer versus a manual approach when performing a simulation is that it often takes only seconds versus hours to reach a steady-state result.

Difficulty: Easy

Learning Objective: Describe numerous ways simulation can be applied in operations.

Section Reference: S13.2 Areas of Simulation Application

11. At a Walmart store, simulation can be used to analyze waiting lines at check-out stands to determine the required staffing levels.

Difficulty: Easy

Learning Objective: Describe numerous ways simulation can be applied in operations.

Section Reference: S13.2 Areas of Simulation Application

12. Simulation analysis is the preferred method used at hospitals to determine the type of treatment a patient requires.

Difficulty: Easy

Learning Objective: Describe numerous ways simulation can be applied in operations.

Section Reference: S13.2 Areas of Simulation Application

MULTIPLE CHOICE QUESTIONS

13. Simulation analysis is useful for operational problems that

a) are easy to solve analytically.

b) can’t be solved analytically.

c) require an optimal solution.

d) meet specific analytical criteria.

Difficulty: Easy

Learning Objective: Use the Monte Carlo technique for simulation and calculate expected value.

Section Reference: S13.1 Monte Carlo Simulation

14. The ___ technique selects numbers randomly from a probability distribution for use in a trial run of a simulation.

a) Computer World

b) Monaco

c) steady-state

d) none of the above

Difficulty: Easy

Learning Objective: Use the Monte Carlo technique for simulation and calculate expected value.

Section Reference: S13.1 Monte Carlo Simulation

15. After a sufficient number of simulation runs, a steady state results when the variable being investigated reaches an ___ value that remains constant.

a) optimal

b) average

c) expected

d) estimated

Difficulty: Medium

Learning Objective: Use the Monte Carlo technique for simulation and calculate expected value.

Section Reference: S13.1 Monte Carlo Simulation

16. The weekly demand for a company’s product follows the probability distribution below:

The expected value, or average, weekly demand is

a) 137.50.

b) 142.50.

c) 153.75.

d) 165.75.

Difficulty: Medium

Learning Objective: Use the Monte Carlo technique for simulation and calculate expected value.

Section Reference: S13.1 Monte Carlo Simulation

17. The weekly demand for a company’s product follows the probability distribution below:

Use the following random numbers to simulate the product’s demand for the next five weeks: 72, 27, 93, 17, 47.

If the first random number interval begins with 1, then the total demand for the simulated five week period is

a) 700.

b) 650.

c) 625.

d) 550.

Difficulty: Medium

Learning Objective: Use the Monte Carlo technique for simulation and calculate expected value.

Section Reference: S13.1 Monte Carlo Simulation

18. The weekly demand for a company’s product follows the probability distribution below:

Use the following random numbers to simulate the product’s demand for the next five weeks: 72, 27, 93, 17, 47.

If the first random number interval begins with 1, then the average weekly demand for the simulated five week period is

a) 137.50.

b) 140.00.

c) 142.50.

d) 152.50.

Difficulty: Medium

Learning Objective: Use the Monte Carlo technique for simulation and calculate expected value.

Section Reference: S13.1 Monte Carlo Simulation

19. The number of daily calls received by a help desk between the hours of 9:00 a.m. and 10:00 a.m. can be described by the following probability distribution:

Based on the distribution of calls above, the expected value, or average number of calls to the help desk between 9:00 a.m. and 10:00 a.m. is

a) 61.5.

b) 62.0.

c) 62.5.

d) 63.0.

Difficulty: Medium

Learning Objective: Use the Monte Carlo technique for simulation and calculate expected value.

Section Reference: S13.1 Monte Carlo Simulation

20. The number of daily calls received by a help desk between the hours of 9:00 a.m. and 10:00 a.m. can be described by the following probability distribution:

Use the following random numbers to simulate the number of calls to the help desk between 9:00 a.m. and 10:00 a.m. for the next five days: 39, 55, 18, 16, 70.

If the first random number interval begins with 1, then the number of calls that would be simulated for day 3 is

a) 50.

b) 55.

c) 60.

d) 65.

Difficulty: Medium

Learning Objective: Use the Monte Carlo technique for simulation and calculate expected value.

Section Reference: S13.1 Monte Carlo Simulation

21. The number of daily calls received by a help desk between the hours of 9:00 a.m. and 10:00 a.m. can be described by the following probability distribution:

Use the following random numbers to simulate the number of calls to the help desk between 9:00 a.m. and 10:00 a.m. for the next five days: 39, 55, 18, 16, 70.

If the first random number interval begins with 1, then the total number of calls received over the simulated five day period is

a) 375.

b) 350.

c) 325.

d) 300.

Difficulty: Medium

Learning Objective: Use the Monte Carlo technique for simulation and calculate expected value.

Section Reference: S13.1 Monte Carlo Simulation

22. The number of daily calls received by a help desk between the hours of 9:00 a.m. and 10:00 a.m. can be described by the following probability distribution:

Use the following random numbers to simulate the number of calls to the help desk between 9:00 a.m. and 10:00 a.m. for the next five days: 39, 55, 18, 16, 70.

If the first random number interval begins with 1, then the average number of calls received over the simulated five day period is

a) 63.

b) 62.

c) 61.

d) 60.

Difficulty: Medium

Learning Objective: Use the Monte Carlo technique for simulation and calculate expected value.

Section Reference: S13.1 Monte Carlo Simulation

23. The weekly capacity measured in machine hours for a small machine shop follows the probability distribution shown below:

Based on the probability distribution above, the expected value, or average hours of weekly capacity for the machine shop is

a) 500 hours.

b) 490 hours.

c) 480 hours.

d) 475 hours.

Difficulty: Medium

Learning Objective: Use the Monte Carlo technique for simulation and calculate expected value.

Section Reference: S13.1 Monte Carlo Simulation

24. The weekly capacity measured in machine hours for a small machine shop follows the probability distribution shown below:

Use the following random numbers to simulate weekly capacity for the machine shop for the next five weeks: 93, 31, 71, 8, 6.

If the first random number interval begins with 1, then the minimum capacity for the simulated five week period is

a) 560.

b) 520.

c) 440.

d) 400.

Difficulty: Medium

Learning Objective: Use the Monte Carlo technique for simulation and calculate expected value.

Section Reference: S13.1 Monte Carlo Simulation

SHORT-ANSWER ESSAY QUESTIONS

25. In what ways is simulation relevant to analyzing production problems?

Difficulty: Medium

Learning Objective: Describe numerous ways simulation can be applied in operations.

Section Reference: S13.2 Areas of Simulation Application

26. What is simulation and why is it a popular decision-making tool?

Difficulty: Medium

Learning Objective: Describe numerous ways simulation can be applied in operations.

Section Reference: S13.2 Areas of Simulation Application

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