ISYE MIDTERM 2 | FALL | ACTUAL EXAM
PAPER 2026 QUESTIONS WITH ANSWERS
GRADED A+
◍ Markov chain.
Answer: Process where a system changes its state in a way that depends only
on its current state.
◍ Variable (optimization sense).
Answer: A decision that an optimization model suggests a value for.
◍ Parametric test.
Answer: Statistical test that assumes the data being tested is sampled from a
distribution governed by certain parameter(s). Parametric tests often focus
on the mean.
◍ Bernoulli distribution.
Answer: Discrete probability distribution where the outcome is binary,
either 0 or 1. Often, 1 represents success and 0 represents failure. The
probability of the outcome being 1 is and the probability of outcome
being 0 is = 1 − , where is between 0 and 1.
◍ Tail(s).
Answer: Highest and lowest-value parts of a distribution.
◍ Perturbation.
Answer: A change (usually small) from the actual or expected value of
something.
◍ Algorithm.
Answer: Step-by-step procedure designed to carry out a task.
◍ Learning.
, Answer: Finding/discovering patterns (or rules) in data, often that can be
applied to new data.
◍ Binary integer program.
Answer: Integer program where all variables are binary variables.
◍ Module.
Answer: In ARENA, a building-block of a simulation, or the process,
resource, etc. it represents.
◍ Convex set.
Answer: A set of points for which a straight line drawn between any two
points in the set, stays inside the set. A circle is a convex set. A set shaped
like the letter "U" is not convex; the line between the two points on top goes
outside of the set.
◍ Lower tail.
Answer: Lowest-value part of a distribution
◍ Wilcoxon signed rank test (one sample).
Answer: Nonparametric test for a single response, to determining whether
the median is different from a specific value.
◍ Objective function.
Answer: Part of an optimization model that measures the quality of a
solution (the values of the variables).
◍ Network.
Answer: Model where locations (nodes or vertices) are connected by arcs or
edges, with flow on the arcs from node to node.
◍ Non-negativity constraints.
Answer: Constraints that require variables to be greater than or equal to
zero.
◍ Assignment problem.
Answer: Network optimization model with two sets of nodes, that finds the
best way to assign each node in one set to each node in the other set.
, ◍ Graph.
Answer: Among other definitions, another name for a network.
◍ Binary variable.
Answer: Variable that can take just two values: 0 and 1.
◍ Exploitation.
Answer: Using known information to get good outcomes.
◍ Queuing.
Answer: The mathematical study of queues.
◍ Markov decision process.
Answer: Markov chain model where decisions are made at some states, and
state transitions have associated rewards.
◍ A/B testing.
Answer: Test of two alternatives to see if either one leads to better
outcomes.
◍ Response surface.
Answer: Sequential experimentation strategy to understand the relationship
between response and input factors, and/or optimize the response.
◍ Transition probability.
Answer: Probability of moving from current state to next state , often
denoted .
◍ Stochastic optimization.
Answer: An optimization model that accounts for randomness or
uncertainty.
◍ Integer program.
Answer: Optimization model where the objective function is a linear
function of the variables, the constraints are linear equations and/or linear
inequalities in terms of the variables, and some or all variables are restricted
to have integer values.
PAPER 2026 QUESTIONS WITH ANSWERS
GRADED A+
◍ Markov chain.
Answer: Process where a system changes its state in a way that depends only
on its current state.
◍ Variable (optimization sense).
Answer: A decision that an optimization model suggests a value for.
◍ Parametric test.
Answer: Statistical test that assumes the data being tested is sampled from a
distribution governed by certain parameter(s). Parametric tests often focus
on the mean.
◍ Bernoulli distribution.
Answer: Discrete probability distribution where the outcome is binary,
either 0 or 1. Often, 1 represents success and 0 represents failure. The
probability of the outcome being 1 is and the probability of outcome
being 0 is = 1 − , where is between 0 and 1.
◍ Tail(s).
Answer: Highest and lowest-value parts of a distribution.
◍ Perturbation.
Answer: A change (usually small) from the actual or expected value of
something.
◍ Algorithm.
Answer: Step-by-step procedure designed to carry out a task.
◍ Learning.
, Answer: Finding/discovering patterns (or rules) in data, often that can be
applied to new data.
◍ Binary integer program.
Answer: Integer program where all variables are binary variables.
◍ Module.
Answer: In ARENA, a building-block of a simulation, or the process,
resource, etc. it represents.
◍ Convex set.
Answer: A set of points for which a straight line drawn between any two
points in the set, stays inside the set. A circle is a convex set. A set shaped
like the letter "U" is not convex; the line between the two points on top goes
outside of the set.
◍ Lower tail.
Answer: Lowest-value part of a distribution
◍ Wilcoxon signed rank test (one sample).
Answer: Nonparametric test for a single response, to determining whether
the median is different from a specific value.
◍ Objective function.
Answer: Part of an optimization model that measures the quality of a
solution (the values of the variables).
◍ Network.
Answer: Model where locations (nodes or vertices) are connected by arcs or
edges, with flow on the arcs from node to node.
◍ Non-negativity constraints.
Answer: Constraints that require variables to be greater than or equal to
zero.
◍ Assignment problem.
Answer: Network optimization model with two sets of nodes, that finds the
best way to assign each node in one set to each node in the other set.
, ◍ Graph.
Answer: Among other definitions, another name for a network.
◍ Binary variable.
Answer: Variable that can take just two values: 0 and 1.
◍ Exploitation.
Answer: Using known information to get good outcomes.
◍ Queuing.
Answer: The mathematical study of queues.
◍ Markov decision process.
Answer: Markov chain model where decisions are made at some states, and
state transitions have associated rewards.
◍ A/B testing.
Answer: Test of two alternatives to see if either one leads to better
outcomes.
◍ Response surface.
Answer: Sequential experimentation strategy to understand the relationship
between response and input factors, and/or optimize the response.
◍ Transition probability.
Answer: Probability of moving from current state to next state , often
denoted .
◍ Stochastic optimization.
Answer: An optimization model that accounts for randomness or
uncertainty.
◍ Integer program.
Answer: Optimization model where the objective function is a linear
function of the variables, the constraints are linear equations and/or linear
inequalities in terms of the variables, and some or all variables are restricted
to have integer values.
