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by: YanRou Ng

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# Exam1CheetSheet.pdf

Marketplace > Exam1CheetSheet pdf
YanRou Ng
Purdue
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This 2 page Reader was uploaded by YanRou Ng on Monday October 12, 2015. The Reader belongs to a course at a university taught by a professor in Fall. Since its upload, it has received 15 views.

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Date Created: 10/12/15
MGMT 306 EXAM 1 Cheat Sheet Components of mathematical model for decisionmaking decision variables objective functions and constraints input parameters If a LP has an optimal solution then the feasible region cannot be empty In order to obtain a feasible solution to an otherwise infeasible problem some of the constraints must be changed or removed Changing objective function will never make an infeasible problem feasible Why conduct sensitivity analysis Some input parameters may not have been known with certainty estimationapproximation Model represents a dynamic environment in which some of the parameters are subject to change by time Given an optimal solution to a LP There is always some amount by which the RHS value of a non binding constraint can be changed without affecting the optimal solution If a shadow price of a constraint is negative a unit decreases in the RHS value of the constraint within feasible range results in an increase in the optimal objective function value Objective Function An equation to be maximized or minimized representing the goal of a mathematical model Optimal Solution The set of values for the decision variables that yield the best value for the objective function 4 types of LP Unique optimal solution alternate optimal solution when obj function parallel to a boundary constraint in the direction of optimization all points on the line are optimal solutions in nite infeasible no point satis es all the constraints 0 optimal solution unbounded optimal solution feasible region unbounded value can always be improved Sensitivity Report Determine how the optimal solution and the optimal objective value are affected by changes in model input data Investigate objective function coefficient and RHS constraints Shadow Price Change of the optimal objective value due to a unit increase to the RHS RatioUnit Price Change of the optimal objective value shadow price X change of RHS Binding Solution makes inequality function equal MGMT 306 EXAM 1 Cheat Sheet Slack Difference between LHS and RHS call slack Don t forget NONNEGATIVITY constraints

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