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BUSOBA 2321 Final Exam | Verified with 100% Correct Answers2024
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BUSOBA 2321 Final Exam | Verified with 100% Correct Answers2024
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BUSOBA 2321 Final Exam | Verified with100% Correct Answers2024
Decision Analysis - <<ANSWER>>a systematic and analytic way to tackle problems
5 Steps in Decision Analysis - <<ANSWER>>1. Define the problem
2. List all possible alternatives
3. Identify the possible outcomes for each decision alternative
4. Identify the payoff for each combination
5. Select technique(s)
Decision Making Under Uncertainty - <<ANSWER>>- Maximax
- Maximin
- Minimax Regret
Maximax - <<ANSWER>>- What is the best outcome for each option? Choose max of
the max
- Optimistic
Maximin - <<ANSWER>>- What is the minimum outcome for each option? Choose max
of min
- Pessimistic
Minimax Regret - <<ANSWER>>1. Find the best outcome for each state of nature
2. Determine the regret for each combo (best option - worst option)
3. Find max regret for each option
LaPlace Method (EMV-equally likely) - <<ANSWER>>- assume all states of nature to
be equally likely
- calculate average "payoff" for each alternative
- select alternative with highest average payoff
*EMV for each option is equal to the avgs. from that option
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
Changes in RHS (Non-Binding Constraint) - <<ANSWER>>- the shadow price is always
0
- there is always some amount (equal to the slack or surplus) that the RHS of a non-
binding constraint can change without affecting the optimal solution
Constraint Coefficient Change - <<ANSWER>>- less likely to occur than OFC and RHS
changes
- if the constraint includes more than one decision variable, the slop of the constraint
changes
- changes the feasible region
- classical LP provides no automatic sensitivity analysis results -- change the coefficient
and rerun the model
Bounded - <<ANSWER>>enclosed by points
Unbounded - <<ANSWER>>- not fully enclosed by points; goes to infinity
- nothing prevents something become infinitely large with a MAX objective or infinitely
small with a MIN objective
Multiple Optimal (Linear Systems) - <<ANSWER>>2+ corner points have the same
value when plugged in to the obj. function
Unique Optimal - <<ANSWER>>each corner point has a unique value when plugged in
to the obj. function
Optimal Solution - <<ANSWER>>- the best solution to the problem
- an optimal point is one that produces the highest OFV when maximizing or the lowest
OFV value when minimizing
EOL - <<ANSWER>>use regret table, minimize opportunity loss
Linear Programming Solver - <<ANSWER>>- use SUMPRODUCT for objective function
cell
- optimal solution cells change from Solver
- Simplex LP
- Non-Neg Constraint box
Constraints - <<ANSWER>>- move all variables to the LHS (might mean there's a 0 on
the RHS)
- variables on the LHS can never be in the denominator (convert to multiplication by a
fraction)
Percent of Target Constrain - <<ANSWER>>distribute percentage to variables, move all
variables to LHS
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