MSIS 3223 Final Exam review (Jason Morgan okstate)CORRECT 100%
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Course
MSIS 3223
Institution
MSIS 3223
Constraints in an Optimization Model - ANSWER•Constraints are limitations, requirements, or other restrictions that are imposed on any solution, either from practical or technological considerations or by management policy.
variable cost - ANSWERunit variable cost*quntity produced
total cos...
MSIS 3223 Final Exam review (Jason
Morgan okstate)CORRECT 100%
Constraints in an Optimization Model - ANSWER•Constraints are limitations, requirements, or other
restrictions that are imposed on any solution, either from practical or technological considerations or
by management policy.
variable cost - ANSWERunit variable cost*quntity produced
total cost - ANSWERfixed cost+variable cost*quantity produced
demand - ANSWERdemand has an influence on profit by predicting how many units of a product will
be sold. quantity produced is a decision option typically based on demand
data tables - ANSWER•Multiple outputs may be evaluated using one way data tables. For example,
profit and revenue.
•Two-way data tables can evaluate only one output variable. To evaluate multiple output variables,
you must construct multiple two-way tables.
Net Income - ANSWEREBT-taxes
Predictive Decision Models - ANSWER•Predictive models focus on understanding the future.
•Practical business models focus on predicting financial performance, customer retention, and
product sales.
•Sales levels are often used to plan inventory levels.
•Predictive models usually involve multiple time periods.
•Predictive models incorporate the uncertainty element
spreadsheet model for profit - ANSWERrevenue-variable cost-fixed cost
What-if analysis - ANSWERallows you to use
data tables
scenario manager
, goal seek
.
Optimization - ANSWERthe process of selecting values of decision variables that minimize or
maximize some quantity of interest
Solver - ANSWERAn Excel add-in that was developed by frontline systems and can be used to solve
many different types of optimization
developing linear optimization models - ANSWER1.Identify the decision variables - the unknown
values that the model seeks to determine.
2.Identify the objective function - the quantity we seek to minimize or maximize.
3.Identify all appropriate constraints - limitations, requirements, or other restrictions that are
imposed on any solution, either from practical or technological considerations or by management
policy.
4.Write the objective function and constraints as mathematical expressions.
5.Implement the mathematical model on a spreadsheet.
Alternative optimal solutions - ANSWERobjective is maximized or minimized by more than one
combination of decision variables
if a solution is unbounded - ANSWERif the value of the objective can be increased or decreased
without bond
An infeasible problem - ANSWERone for which no feasible solution exists - that is, when there is no
solution that satisfies all constraints simultaneously
Solver and error messages - ANSWER•Many types of financial investment problems are modeled and
solved using linear optimization.
•A poorly scaled model is one that computes values of the objective, constraints, or intermediate
results that differ by several orders of magnitude. As a result, Solver may detect or suffer from
"numerical instability."
•Poor scaling can cause Solver engines to return different error messages.
•In the Solver options, you can check the box Use Automatic Scaling and Solver will rescale the
values of the objective and constraint functions internally to minimize the effects of poor scaling.
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