BUSOBA 2321 Final Exam Questions And Answers Verified 100% Correct

BUSOBA 2321 Final Exam Questions And Answers Verified 100% Correct Decision Making Under Risk - ANSWER -- Expected value - Decision trees Expected Value Method - ANSWER -- maximize EMV (probability of outcome * payoff of outcome) - OR minimize EOL (probability of outcome * regret of outcome) EMV - ANSWER -- use payoff table, maximize the value - make equations based on weak and strong demand - strong demand * p, weak demand * 1-p, add them - equations can be used to find indifference points EMV Graph - ANSWER -- plot lines - intersections represent indifference points - should NOT dictate choices - more risk as you approach certainties (P=0, P=1) Linear Model Assumptions - ANSWER -- certainty - proportionality - additivity - divisibility (solution doesn't need to be an integer) Linear Model - ANSWER -- x raised to the first power - aims to minimize or maximize something - has limitations (constraints) - there MUST be alternatives available Linear Programming - ANSWER -a mathematical optimization model build entirely from linear equations and/or inequalities Components of an LP Model - ANSWER -- decision variables - parameters - objective function - contraints Redundant Contraint - ANSWER -- does not determine the feasible region - won't usually prevent an optimal solution from being found - creates excess baggage - could be a modeling error *don't remove it, instead think about how to close the gap Feasible Region - ANSWER -the set of points that satisfies all constraints Corner Point Property - ANSWER -an optimal solution must lie at one or more corner points (optimal point) Binding (Active) Constraint - ANSWER -- restricts the solution; 0 slack or surplus - occurs if the RHS = LHS (always at least one present) - can change when an old constraint is no longer useful Non-Binding (Inactive) Constraint - ANSWER -- does not restrict the solution from becoming infinitely large or small - non-zero slack or surplus - always results from a modeling error Slack - ANSWER -extra RHS in a non-binding constraint Surplus - ANSWER -- over achievement of RHS in a non-binding constraint - can make the solution more optimal Multiple Optimal - ANSWER -- alternate optimal solutions are available - the slope of the objective function is equal to the slope of an active constraint - provides flexibility which is generally desirable - two adjacent points share an indifference point - software can only identify corner point solutions Infeasibility - ANSWER -- there's no overlapping area of constraints - software can detect but can't determine the cause Sensitivity Analysis - ANSWER -How sensitive is our optimal solution to changes and assumptions we made? Includes analyzing the effect of changes in - Objective function coefficients - RHS values - Constrain coefficients Shadow Price - ANSWER -- objective value at the optimal solution - if we increase one constraint by one unit - the marginal value of a resource - if positive, will increase the OFV - if negative, will decrease the OFV Adjacent Points - ANSWER -the corner points connected to the optimal solution by line segments Indifference Points - ANSWER -points that produce the same objective function value Allowable Change in OFC - ANSWER -- the amount by which a coefficient in the objective function can change before the optimal solution changes - thus, there are multiple optimal solutions within this range Characteristics of OFC Changes - ANSWER -- they do NOT change the feasible region - the slope changes (if it changes enough, a different corner point will become optimal) Changes in RHS - ANSWER -tells us how much of a particular resource we have Impact of RHS Change - ANSWER -- changing a non-redundant constraint will change the feasible region - relaxing and tightening - binding constrains have 0 slack or surplus Relaxing - ANSWER -makes the feasible region larger and can improve the objective function value Tightening - ANSWER -makes the feasible region smaller and can worsen the objective function Steps for Calculating Shadow Price - ANSWER -1. Add 1 unit to 1 constraint 2. Calculate new optimal solution 3. Calculate new OFV 4. Compare to new OFV to old OFV, the difference is the shadow price