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goal-seeking analysis.
what-if analysis.
sensitivity analysis.
utility modeling.
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multiple goals that need to be simultaneously or jointly optimized
single goals that cannot be optimized using linear and nonlinear programming.
single goals that can be optimized using linear and nonlinear programming
a small number of goals that can be independently optimized using linear and nonlinear programming
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Problems with a single goal
problems with a large number of alternatives
problems where probabilities are unknown
problems with a tabular representation
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It can handle multiple criteria and goals.
It is really not a decision model at all
It is based entirely on quantitative data.
It is an opaque "black box" in the same way as neural networks.
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classification and clustering methods.
the customer.
business partners.
customer service reps
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Goal-seeking analysis.
What-if analysis.
sensitivity analysis.
utility modeling.
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goal-seeking analysis.
what-if analysis.
Sensitivity analysis.
utility modeling.
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internal metrics
decision variables
constraints
objective function
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the best possible outcome of most alternatives will occur.
the best possible outcome of each alternative will occur.
the best possible outcome of one alternative will occur
the best possible outcome of some alternatives will occur.
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how to package products in the right format
how to sell products at the right price.
how to get products to the right customer
All of the above
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decision tree models
mathematical programming models
qualitative models.
heuristic programming models
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organizational culture is important and affects the model use.
environments have greater impact on a model than the organization does
environmental factors may have created the current problem.
it is critical to identify key corporate decision makers
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enable organizations to see likely results of their decisions.
Replace strategy formulation at top levels of the organization.
enhance the decision making process
reduce the costs of providing services to customers.
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They can be used to build static and dynamic models
they easily import and manipulate massive databases.
they are able to import and export to many different file formats.
it is easy to manipulate data and see results instantly.
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It assumes the worst possible outcome of one alternative will occur and then avoids it.
It assumes the worst possible outcome of some alternatives will occur and then selects the best of them
It assumes the worst possible outcome of each alternative will occur and then selects the best of them
It assumes the worst possible outcome of each alternative will occur and then selects the worst of them.
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In calculating utility, it assumes the decision will be made thousands of times, making the probabilities more likely on average
Used in decision making, it can bring huge risk to a small startup with limited resources.
It does not affect decisions made with expected values.
Used in decision making, it is an objective value, not subjective
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It permits the manager to input data to increase his/her confidence in the model.
it allows flexibility and adaptation to changing conditions
it provides a better understanding of the model and the decision-making situation.
it improves the mathematical optimality of the generated solutions.
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product mix variables
financial and accounting variables
constraint and limit variables
slack and surplus variables
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A map of the environment around decision makers
a map of the environment around a model
a diagram showing the influence of decision makers
A graphical representation of a model
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