ISYE 6644 OAN|001 EXAM 1
SIMULATION | V1 AND V2 — ORIGINAL
PRACTICE QUESTIONS 1–100
INTRODUCTION
ISYE 6644 Simulation develops the analytical skills required to model, analyze, and
interpret stochastic systems when closed-form mathematical solutions are difficult or
impossible. The course is particularly relevant to graduate students and analytics
professionals who need to evaluate operational systems involving uncertainty, queues,
inventories, reliability, manufacturing, transportation, and service processes. Exam 1
typically emphasizes foundational simulation concepts, probability models, random-
number generation, discrete-event modeling, statistical estimation, and the proper
interpretation of simulation output.
This practice bank is designed to strengthen application-level reasoning rather than
simple memorization. The questions use realistic scenarios requiring you to select
appropriate modeling assumptions, identify statistical issues, interpret distributions,
diagnose simulation problems, and distinguish valid conclusions from misleading
ones. Working through these questions can help identify conceptual weaknesses
before an assessment and build the type of reasoning needed for difficult simulation
problems. Use the rationales to understand not only which answer is best, but also
why competing approaches fail under the stated assumptions.
CORE DOMAINS TESTED
1.
Simulation Fundamentals — Purpose, scope, advantages, limitations, and
appropriate applications of simulation.
2.
3.
Discrete-Event Simulation — Events, state variables, system clocks, entities,
resources, queues, and event scheduling.
4.
5.
Probability and Random Variables — Discrete and continuous distributions,
expectations, variance, and conditional probability.
6.
1
, 7.
Random-Number Generation — Pseudorandom numbers, seeds,
reproducibility, uniformity, and independence.
8.
9.
Input Modeling — Selecting and validating probability distributions from
empirical observations.
10.
11.
Monte Carlo Simulation — Sampling uncertain quantities and estimating
probabilities, expectations, and other performance measures.
12.
13.
Queueing and Stochastic Systems — Arrivals, service processes, utilization,
waiting, and congestion.
14.
15.
Statistical Analysis of Simulation Output — Replications, confidence
intervals, standard errors, and sampling variability.
16.
17.
Initialization and Steady State — Transient behavior, warm-up periods, and
initialization bias.
18.
19.
Model Validation and Verification — Checking whether a simulation is
implemented correctly and represents the intended real system.
20.
21.
Variance Reduction and Experimental Design — Techniques for improving
simulation efficiency and making meaningful comparisons.
22.
23.
2
, Interpretation of Simulation Results — Correctly communicating
uncertainty and avoiding unsupported conclusions.
24.
QUESTIONS 1-100
Q1: A logistics company is considering a new warehouse layout.
Customer arrivals, processing times, and equipment failures
are all uncertain, and management wants to compare
alternative layouts before physically implementing one. Which
approach is most appropriate?
A) Solve a deterministic linear program and ignore uncertainty
B) Develop a stochastic simulation model and compare the performance of the
alternative layouts
C) Replace every random input with its sample mean and calculate one deterministic
result
D) Select the layout with the highest theoretical capacity without modeling
interactions
Rationale: The correct answer is B because simulation is particularly useful when
system behavior depends on interacting stochastic components and alternative
designs can be evaluated experimentally. Option A is inadequate when uncertainty
and dynamic interactions materially affect performance. Option C can distort
nonlinear systems because performance under average inputs need not equal average
performance. Option D considers only capacity and ignores stochastic congestion,
failures, and interactions.
Q2: In a discrete-event simulation of a bank, customers
arrive, join a queue, begin service, and depart. Which
variable most appropriately represents the system state?
A) The bank's annual revenue
B) The average customer age
C) The number of customers currently in the system
D) The probability that a customer arrives next year
Rationale: The correct answer is C because the number of customers in the system
changes as arrival and departure events occur and therefore is a state variable.
Option A may be an output measure rather than an instantaneous state. Option B is
not necessarily relevant to the modeled system state. Option D describes a probability
rather than the current system condition.
3
, Q3: A simulation clock currently reads 14.2 minutes. The next
scheduled event occurs at time 15.0. What should a standard
next-event discrete-event simulation do?
A) Advance the clock one minute at a time
B) Advance the simulation clock directly to 15.0 and process the event
C) Keep the clock at 14.2 until another event is generated
D) Reset the clock to zero before processing the event
Rationale: The correct answer is B because next-event simulation advances the clock
directly from one event time to the next rather than incrementing through periods
during which nothing changes. Option A is inefficient and describes a time-stepping
approach. Option C would prevent the scheduled event from being processed
correctly. Option D destroys the temporal structure of the simulation.
Q4: A simulation has three future events scheduled at times
4.1, 4.8, and 5.3. The current simulation time is 3.7. Which
event should be processed first?
A) The event at 5.3
B) The event at 4.8
C) The event at 4.1
D) All three events simultaneously
Rationale: The correct answer is C because discrete-event simulation processes the
chronologically earliest future event. Option A and B violate chronological event
processing. Option D is incorrect because the events occur at distinct times.
Q5: A call-center simulation begins with 50 customers already
waiting because the analyst wants to approximate a system
that has been operating continuously. What modeling issue is
being addressed?
A) Random-number periodicity
B) Initialization conditions
C) Distribution truncation
D) Sampling without replacement
Rationale: The correct answer is B because the initial state of a simulation can
strongly affect early observations, especially when the real system normally operates
in a nonempty condition. Option A concerns random-number generation. Option C
concerns the support of a probability distribution. Option D concerns sampling
procedures rather than system initialization.
Q6: A manufacturing simulation begins empty, but the real
factory normally operates near capacity. The analyst
4
SIMULATION | V1 AND V2 — ORIGINAL
PRACTICE QUESTIONS 1–100
INTRODUCTION
ISYE 6644 Simulation develops the analytical skills required to model, analyze, and
interpret stochastic systems when closed-form mathematical solutions are difficult or
impossible. The course is particularly relevant to graduate students and analytics
professionals who need to evaluate operational systems involving uncertainty, queues,
inventories, reliability, manufacturing, transportation, and service processes. Exam 1
typically emphasizes foundational simulation concepts, probability models, random-
number generation, discrete-event modeling, statistical estimation, and the proper
interpretation of simulation output.
This practice bank is designed to strengthen application-level reasoning rather than
simple memorization. The questions use realistic scenarios requiring you to select
appropriate modeling assumptions, identify statistical issues, interpret distributions,
diagnose simulation problems, and distinguish valid conclusions from misleading
ones. Working through these questions can help identify conceptual weaknesses
before an assessment and build the type of reasoning needed for difficult simulation
problems. Use the rationales to understand not only which answer is best, but also
why competing approaches fail under the stated assumptions.
CORE DOMAINS TESTED
1.
Simulation Fundamentals — Purpose, scope, advantages, limitations, and
appropriate applications of simulation.
2.
3.
Discrete-Event Simulation — Events, state variables, system clocks, entities,
resources, queues, and event scheduling.
4.
5.
Probability and Random Variables — Discrete and continuous distributions,
expectations, variance, and conditional probability.
6.
1
, 7.
Random-Number Generation — Pseudorandom numbers, seeds,
reproducibility, uniformity, and independence.
8.
9.
Input Modeling — Selecting and validating probability distributions from
empirical observations.
10.
11.
Monte Carlo Simulation — Sampling uncertain quantities and estimating
probabilities, expectations, and other performance measures.
12.
13.
Queueing and Stochastic Systems — Arrivals, service processes, utilization,
waiting, and congestion.
14.
15.
Statistical Analysis of Simulation Output — Replications, confidence
intervals, standard errors, and sampling variability.
16.
17.
Initialization and Steady State — Transient behavior, warm-up periods, and
initialization bias.
18.
19.
Model Validation and Verification — Checking whether a simulation is
implemented correctly and represents the intended real system.
20.
21.
Variance Reduction and Experimental Design — Techniques for improving
simulation efficiency and making meaningful comparisons.
22.
23.
2
, Interpretation of Simulation Results — Correctly communicating
uncertainty and avoiding unsupported conclusions.
24.
QUESTIONS 1-100
Q1: A logistics company is considering a new warehouse layout.
Customer arrivals, processing times, and equipment failures
are all uncertain, and management wants to compare
alternative layouts before physically implementing one. Which
approach is most appropriate?
A) Solve a deterministic linear program and ignore uncertainty
B) Develop a stochastic simulation model and compare the performance of the
alternative layouts
C) Replace every random input with its sample mean and calculate one deterministic
result
D) Select the layout with the highest theoretical capacity without modeling
interactions
Rationale: The correct answer is B because simulation is particularly useful when
system behavior depends on interacting stochastic components and alternative
designs can be evaluated experimentally. Option A is inadequate when uncertainty
and dynamic interactions materially affect performance. Option C can distort
nonlinear systems because performance under average inputs need not equal average
performance. Option D considers only capacity and ignores stochastic congestion,
failures, and interactions.
Q2: In a discrete-event simulation of a bank, customers
arrive, join a queue, begin service, and depart. Which
variable most appropriately represents the system state?
A) The bank's annual revenue
B) The average customer age
C) The number of customers currently in the system
D) The probability that a customer arrives next year
Rationale: The correct answer is C because the number of customers in the system
changes as arrival and departure events occur and therefore is a state variable.
Option A may be an output measure rather than an instantaneous state. Option B is
not necessarily relevant to the modeled system state. Option D describes a probability
rather than the current system condition.
3
, Q3: A simulation clock currently reads 14.2 minutes. The next
scheduled event occurs at time 15.0. What should a standard
next-event discrete-event simulation do?
A) Advance the clock one minute at a time
B) Advance the simulation clock directly to 15.0 and process the event
C) Keep the clock at 14.2 until another event is generated
D) Reset the clock to zero before processing the event
Rationale: The correct answer is B because next-event simulation advances the clock
directly from one event time to the next rather than incrementing through periods
during which nothing changes. Option A is inefficient and describes a time-stepping
approach. Option C would prevent the scheduled event from being processed
correctly. Option D destroys the temporal structure of the simulation.
Q4: A simulation has three future events scheduled at times
4.1, 4.8, and 5.3. The current simulation time is 3.7. Which
event should be processed first?
A) The event at 5.3
B) The event at 4.8
C) The event at 4.1
D) All three events simultaneously
Rationale: The correct answer is C because discrete-event simulation processes the
chronologically earliest future event. Option A and B violate chronological event
processing. Option D is incorrect because the events occur at distinct times.
Q5: A call-center simulation begins with 50 customers already
waiting because the analyst wants to approximate a system
that has been operating continuously. What modeling issue is
being addressed?
A) Random-number periodicity
B) Initialization conditions
C) Distribution truncation
D) Sampling without replacement
Rationale: The correct answer is B because the initial state of a simulation can
strongly affect early observations, especially when the real system normally operates
in a nonempty condition. Option A concerns random-number generation. Option C
concerns the support of a probability distribution. Option D concerns sampling
procedures rather than system initialization.
Q6: A manufacturing simulation begins empty, but the real
factory normally operates near capacity. The analyst
4
