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GB307 exam #2
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optimization
finds feasible solutions to objective functions that are best in some measurable sense
Simplex Method for Linear Programming (LP)
finds optimal solutions to systems of linear equations
optimal solution
1) occurs at a corner point of the feasible region where at least one constraint is binding
2) comprises the final values of decision variables, and the final best outcome
sensitivity analysis
helps understand if a solution is robust to changes in inputs and coefficients
shadow price
tells you how much you would be willing to pay to relax a constraint because it indicates how much the optimal outcome will improve for another unit of a resource
reduced cost
indicates how much an objective function coefficient could change without changing the associated final value
also has an opportunity cost
when the final value of the decision variable is zero & reduced cost is not zero...
the opportunity cost of making the final value = 1
when final value of the decision variable is not zero & reduced cost is zero...
no opportunity cost nor improvement needed in the objective coefficient for the final value to be greater than zero (it is)
when final value is not zero & reduced cost is not zero...
reduced cost is the shadow price for the simple upper or lower bound constraint on the final value of the decision variable
auxiliary variables
a new set of cells for any decision variables that have simple upper or lower bound constraints created by referencing (not copying) the original changing cells
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