Supply Chain Modeling and Decision Making Final Exam

You want to minimize the maximum deviation from any goal.

A MINIMAX objective is sometimes helpful in goal programming (GP) when:

Is a graphical representation of a set of rules for classifying observations into two or more groups. Use a hierarchical sorting process consisting of splitting nodes and terminal nodes to group records from a data set into increasingly homogeneous groups. Are popular because the resulting classification rules are very apparent and easy to interpret. (All of the above are characteristics of classification trees)

A classification tree:

An alternative

A course of action intended to solve a problem is called:

Variable whose value cannot be predicted or set with certainty.

A random variable is a:

Is a sensitivity analysis technique. Allows a decision maker to analyze optimal decision strategy changes. Allows two simultaneous changes in probability estimates. All of the above answers are correct.

A strategy table:

A payoff matrix

A table that summarizes the final outcome (or payoff) for each decision alternative under each possible state of nature is referred to as:

A data mining technique aimed at discovering what goes with what.

Affinity analysis is:

Create the RNGs required for simulating a model.

Analytic Solver Platform (ASP) provides several "Psi" functions that can be used to

Discrete

Classification techniques differ from most other predictive statistical methods, such as regression analysis, because the dependent variable is:

A data mining technique used to identify meaningful groupings of records within a data set.

Cluster analysis is:

d1-= 2 and d1+= 0

Consider the constraint:X1+d1--d1+= 5. Suppose that X1 = 3 in the optimal solution. The values of deviational variables d1- and d1+ are:

Classification, Prediction, Association/ Segmentation (All of the above)

Data mining tasks fall into the following potential categories:

Represent the amount by which each goal deviates from its target value.

Deviational variables:

States of Nature

Future events that are not under the decision maker's control are known as:

Involves solving problems containing a collection of goals that we would like to achieve.

Goal Programming:

Goals with target values and deviational variables, which measure the amount by which a given solution deviates from a particular goal.

Goal programming (GP) provides a way of analyzing potential solutions to a decision problem that involves soft constraints. Soft constraints can be stated as:

The base-case scenario.

If we don't know what value a particular cell in a spreadsheet will assume and enter a number that we think is the most likely value for the uncertain cell, we can calculate the most likely value of the bottom-line performance measure. This is called:

How accurate the classifier is when it predicts a "success".

In Discriminant Analysis (DA), precision is a measure of:

Combines the precision and recall measures to provide an overall measure of a classifier's accuracy.

In Discriminant Analysis, the F1 score:

Cannot be violated.

In goal programming, hard constraints:

"Add Output" button on the Analytic Solver Platform (ASP) menu.

In order to indicate the output cell (or cells) that we want Analytic Solver Platform (ASP) to track during the simulation we can use the:

A manager changes the values of the uncertain input variables to see what happens to the bottom-line performance measure.

In what-if analysis:

A classification technique that estimates the probability of an observation belonging to a particular group.

Logistic regression is:

Simulate human learning, Are computer programs modeled after computing architecture of the human brain, Are a pattern recognition technique that attempts to learn what relationship exists between a set of input and output variables (All of the above characterize neural networks)

Neural networks:

Classifies new observations less accurately than trees that do not overfit the training data.

Overfitting refers to a situation when the tree algorithm:

Replication

Recalculating the spreadsheet several hundred or several thousand times and recording the resulting values generated for the output cell(s), or bottom-line performance measure(s) is called:

You may be interested in examining how sensitive the simulation output results are to various uncertain input cells in the model.

Sensitivity analysis is useful when:

A technique that measures various characteristics of the model bottom-line performance measure. A technique that describes various characteristics of the bottom-line performance measure. A technique that is useful when one or more values for the independent variables are uncertain. All of the above

Simulation is:

All goals must be met exactly.

Suppose that all goal constraints in a goal programming problem are hard and the objective is: MIN E(di-+di+) Then:

Preemptive GP

Suppose that in goal programming (GP) we assign arbitrarily large weights to deviations from these goals to ensure that undesirable deviations from them never occur. This approach is called:

The target value for goal i.

Suppose that the objective function for a GP problem is: MIN E1/ti (wi-di-+wi+di+) . The term ti represents:

Numeric constants that can be assigned values to weight the various deviational variables in the problem.

Suppose that the objective function for a GP problem is: MIN: E(wi-di-+wi+di+). The terms wi- and wi+ represent:

No, because EVPI is $25, which is less than the consultant's fee of $30.

Suppose that you have an option to hire a consultant who has the ability to predict the future with 100 percent accuracy. Using the consultant's reliable recommendations, you found that the expected value with perfect information is equal to $200. Without the consultant's insights you determined the EMV to be equal to $175. Would you pay the consultant $30 for her service? Why or why not?

23

Suppose that, for a given decision alternative, the payoffs are (10, 20, 30) with respective probabilities of (0.2, 0.3 and 0.5). The expected payoff for this alternative is:

20

Suppose the highest payoff decision for a given state of nature is 100. You made a decision with a payoff of 80. The regret (or opportunity loss) for your decision is:

Criteria

The ______ in a decision problem represent various factors that are important to the decision maker.

The results of simulation do give us greater insight into the problem. It gives a decision maker some idea of the best- and worst-case total outcomes for the problem. It provides an idea of the distribution and variability of the possible outcomes. All of the Above

The benefit(s) of simulation include(s):

The right data in the right amount for the problem at hand.

The challenge with data availability today is getting:

Maximin

The decision rule that pessimistically assumes that nature will always be "against us" regardless of the decision we make is:

Maximax

The decision rule that selects the alternative associated with the largest payoff is:

Nonprobabilistic rules

The decision rules that assume that probabilities of occurrence are not known or cannot be assigned to the states of nature in a decision problem are referred to as:

A graphical presentation of the information available in the payoff table. Intuitive to use. Easy to use for multi-stage decisions. ALL OF THE ABOVE

The decision tree is:

Always result in the selection of the same decision alternative.

The expected monetary value (EMV) and expected opportunity loss (EOL) decision rules:

Placing a random number generator (RNG) formula in each cell that represents a random, or uncertain, independent variable.

The first step in performing a simulation in a spreadsheet is:

Converting the payoff matrix into a regret matrix.

The first step in using the minimax regret decision rule is:

Click the Partition icon in the Data Mining section.

The first step to create the training and validation data set using XLMiner Platform is:

Help individuals make good decisions.

The goal of decision analysis is to:

Identifies the k observations in the training data that are most similar (or nearest) to a new observation we want to classify.

The k-nearest neighbor (k-NN) technique:

Reconciling trade-offs between conflicting goals.

The key concept in goal programming (GP) is:

Provide the most likely value for the bottom-line performance measure (Y)

The manager hopes that using the expected, or most likely, values for all the uncertain variables will:

Provide theoretically optimal or good classification results.

The purpose of discriminant analysis (DA) is to:

Encompasses a variety of analytic techniques that can be used to help managers analyze, understand, and extract value from large sets of data.

The term "data mining":

The decision maker investigates a variety of solutions to find one that is most satisfactory.

The term 'iterative solution procedure' means that:

The inputs that have the greatest impact on the EMV

Tornado charts in Analytic Solver Platform (ASP) help identify:

The states of nature in a decision problem can be assigned probabilities of occurrence.

Under probabilistic decision rules:

Rolling back

We can apply a process known as ______ to a decision tree to determine the decision with the largest EMV.

Classification

When a credit manager of a mortgage company identifies the loans as those resulting in default and those that are current, he/she uses:

The composition of the training sample.

When faced with a classification problem, careful consideration should be given to:

React with Paralysis Do exhaustive research Avoid making a decision All of the above

When faced with uncertainty, people do the following:

Risk

When such a decision is made, some chance exists that the decision will not produce the intended results. This chance, or uncertainty, represents:

State of nature.

Your company decided to build a manufacturing plant in Georgia in anticipation of increased demand for the product it produces. The level of demand in this problem is a(n):