Question 1

Simulation models are inexpensive to design and use.

Accepted Answer

A) True 
 B)False

Question 2

The Las Vegas method is a simulation technique that uses random elements when chance exists in their behavior.

Accepted Answer

A) True 
 B)False

Question 3

Would you simulate a problem for which there is an exact mathematical model already?

Accepted Answer

In terms of getting the problem solved,

Question 4

The effects of OM policies over many months or years can be obtained by computer simulation in a short time. This phenomenon is referred to as

Accepted Answer

A)  time suppression 
B)  time suspension 
C)  time compression 
D)  time inversion 
E)  time conversion 
A)  time suppression 
B)  time suspension 
C)  time compression 
D)  time inversion 
E)  time conversion

Question 5

A waiting-line problem that cannot be modeled by standard distributions has been simulated. The table below shows the result of a Monte Carlo simulation. (Assume that the simulation began at 8:00 a.m. and there is only one server.) Why do you think this problem does not fit the standard distribution for waiting lines?
 Explain briefly how a Monte Carlo simulation might work where analytical models cannot.
 $$\begin{array} { | c | c | c | c | } 
\hline \text { Customer Number } & \text { Arrival Time } & \text { Service Time } & \text { Service Ends } \
\hline 1 & 8 : 05 & 2 & 8 : 07 \
\hline 2 & 8 : 06 & 10 & 8 : 17 \
\hline 3 & 8 : 10 & 15 & 8 : 32 \
\hline 4 & 8 : 20 & 12 & 8 : 44 \
\hline 5 & 8 : 30 & 4 & 8 : 48 \
\hline
\end{array}$$

Accepted Answer

Service times do not appear to be expone

Question 6

A small store is trying to determine if its current checkout system is adequate. Currently, there is only one cashier, so it is a single-channel, single-phase system. The store has collected information on the interarrival time, and service time distributions. They are represented in the tables below. Use the following two-digit random numbers given below to simulate 10 customers through the checkout system. What is the average time in line, and average time in system?
 (Set first arrival time to the interarrival time generated by first random number.
 $$\begin{array} { | c | c | c | c | c | } 
\hline \begin{array} { c } 
\text { Interarrival } \
\text { time (minutes) }
\end{array} & \text { Probability } & & \begin{array} { c } 
\text { Service time } \
\text { (minutes) }
\end{array} & \text { Probability } \
\hline 3 & .25 & & 1 & .30 \
\hline 4 & .25 & & 2 & .40 \
\hline 5 & .30 & & 3 & .20 \
\hline 6 & .20 & & 4 & .10 \
\hline
\end{array}$$ 
Random numbers for interarrival times: 07, 60, 77, 49, 76, 95, 51, 16, 14, 85
Random numbers of service times: 57, 17, 36, 72, 85, 31, 44, 30, 26, 09

Accepted Answer

@#IMG-DLM& 
 @#IMG-DLM& 
Average time in

Question 7

From a portion of a probability distribution, you read that P(demand = 1) is 0.05, P(demand = 2) is 0.15, and P(demand = 3) is .20. The cumulative probability for demand 3 would be

Accepted Answer

A)  0.133 
B)  0.200 
C)  0.400 
D)  0.600 
E)  cannot be determined from the information given 
A)  0.133 
B)  0.200 
C)  0.400 
D)  0.600 
E)  cannot be determined from the information given

Question 8

Explain how Monte Carlo simulation uses random numbers.

Accepted Answer

First, a cumulative probability distribu

Question 9

Like mathematical and analytical models, simulation is restricted to using the standard probability distributions.

Accepted Answer

A) True 
 B)False

Question 10

One reason for using simulation rather than an analytical model in an inventory problem is that the simulation is able to handle probabilistic demand and lead times.

Accepted Answer

A) True 
 B)False

Question 11

What is the cumulative probability distribution of the following variable?
$\begin{array}{|l|l|}
\hline\text { Tires Sold } & \text { Probability } \
\hline 0 & 1 \
\hline 1 & 2 \
\hline 2 & 15 \
\hline 3 & 3 \
\hline 4 & 25 \
\hline
\end{array}$

Accepted Answer

A)  1 
B)  .5 
C)  10 
D)  .2 
E)  none of the above 
A)  1 
B)  .5 
C)  10 
D)  .2 
E)  none of the above

Question 12

Which of the following statements regarding simulation is true?

Accepted Answer

A)  Simulation can be physical or mathematical. 
B)  Simulation has numerous areas of application in operations. 
C)  Simulation attempts to duplicate a real system. 
D)  Monte Carlo simulation is a chance-based mathematical model of a real system. 
E)  All of these are true. 
A)  Simulation can be physical or mathematical. 
B)  Simulation has numerous areas of application in operations. 
C)  Simulation attempts to duplicate a real system. 
D)  Monte Carlo simulation is a chance-based mathematical model of a real system. 
E)  All of these are true.

Question 13

A distribution of service times at a waiting line shows that service takes 6 minutes 40 percent of the time, 7 minutes 30 percent of the time, 8 minutes 20 percent of the time, and 9 minutes 10 percent of the time. Prepare the probability distribution, the cumulative probability distribution, and the random number intervals for this problem.

Accepted Answer

The answer of A distribution of service times at a...

Question 14

Provide a small example illustrating how random numbers are used in Monte Carlo simulation.

Accepted Answer

For example, demand can be 0, 40% of the

Question 15

From a portion of a probability distribution, you read that P(demand = 0) is 0.05 and P(demand = 1) is 0.10. The cumulative probability for demand 1 would be

Accepted Answer

A)  0.05 
B)  0.075 
C)  0.10 
D)  0.15 
E)  cannot be determined 
A)  0.05 
B)  0.075 
C)  0.10 
D)  0.15 
E)  cannot be determined

Question 16

One of the advantages of simulation is that

Accepted Answer

A)  real-world complications can be included that most OM models cannot permit 
B)  it always generates a more accurate solution than a mathematical solution 
C)  it is a trial-and-error approach that may produce different solutions in repeated runs 
D)  model development is less time consuming than for mathematical models 
E)  model solutions are transferable to a wide variety of problems 
A)  real-world complications can be included that most OM models cannot permit 
B)  it always generates a more accurate solution than a mathematical solution 
C)  it is a trial-and-error approach that may produce different solutions in repeated runs 
D)  model development is less time consuming than for mathematical models 
E)  model solutions are transferable to a wide variety of problems

Question 17

The number of tires sold at a car garage varies randomly between 0 and 4 each hour. What set of random numbers (on the 1-100 scale would tire sales of 2 be assigned?

Accepted Answer

A)  01 through 20 
B)  21 through 40 
C)  41 through 60 
D)  61 through 80 
E)  81 through 100 
A)  01 through 20 
B)  21 through 40 
C)  41 through 60 
D)  61 through 80 
E)  81 through 100

Question 18

A distribution of service times at a waiting line shows that service takes 6 minutes 30 percent of the time, 7 minutes 40 percent of the time, 8 minutes 20 percent of the time, and 9 minutes 10 percent of the time. This distribution has been prepared for Monte Carlo analysis. The first two random numbers drawn are 53 and 74. The simulated service times are __________ minutes, then __________ minutes.

Accepted Answer

A)  6; 7 
B)  7; 7 
C)  7; 8 
D)  8; 9 
E)  Cannot determine, because no service time probability is that large. 
A)  6; 7 
B)  7; 7 
C)  7; 8 
D)  8; 9 
E)  Cannot determine, because no service time probability is that large.

Question 19

The idea behind simulation is threefold: (1) to imitate a real-world situation mathematically, (2) then to study its properties and operating characteristics, and (3) finally to draw conclusions and make action decisions based on the results of the simulation.

Accepted Answer

A) True 
 B)False

Question 20

Which of the following restrictions applies to queuing models but not Monte Carlo simulations?

Accepted Answer

A)  Poisson distribution of arrivals 
B)  constant or exponential service times 
C)  average length of line 
D)  A and B 
E)  A, B, and C 
A)  Poisson distribution of arrivals 
B)  constant or exponential service times 
C)  average length of line 
D)  A and B 
E)  A, B, and C

Exam 26: Simulation

Simulation models are inexpensive to design and use.

The Las Vegas method is a simulation technique that uses random elements when chance exists in their behavior.

Would you simulate a problem for which there is an exact mathematical model already?

The effects of OM policies over many months or years can be obtained by computer simulation in a short time. This phenomenon is referred to as

From a portion of a probability distribution, you read that P(demand = 1) is 0.05, P(demand = 2) is 0.15, and P(demand = 3) is .20. The cumulative probability for demand 3 would be

Explain how Monte Carlo simulation uses random numbers.

Like mathematical and analytical models, simulation is restricted to using the standard probability distributions.

One reason for using simulation rather than an analytical model in an inventory problem is that the simulation is able to handle probabilistic demand and lead times.

What is the cumulative probability distribution of the following variable? Tires Sold Probability 0 1 1 2 2 15 3 3 4 25

Which of the following statements regarding simulation is true?

Provide a small example illustrating how random numbers are used in Monte Carlo simulation.

From a portion of a probability distribution, you read that P(demand = 0) is 0.05 and P(demand = 1) is 0.10. The cumulative probability for demand 1 would be

One of the advantages of simulation is that

The number of tires sold at a car garage varies randomly between 0 and 4 each hour. What set of random numbers (on the 1-100 scale would tire sales of 2 be assigned?

The idea behind simulation is threefold: (1) to imitate a real-world situation mathematically, (2) then to study its properties and operating characteristics, and (3) finally to draw conclusions and make action decisions based on the results of the simulation.

Which of the following restrictions applies to queuing models but not Monte Carlo simulations?

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Operations Strategy in a Global Environment

Project Management

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Managing Quality

Statistical Process Control

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Outsourcing as a Supply-Chain Strategy

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