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Case Analysis for EE590 Wang Yu Iowa State University.

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Presentation on theme: "Case Analysis for EE590 Wang Yu Iowa State University."— Presentation transcript:

1 Case Analysis for EE590 Wang Yu Iowa State University

2 Case 2: Andrew Olgilvie Introduction Decision Maker: Andrew Olgilvie Objective: Maximum profit Opportunity: Whether to accept the client’s offer Alternative: Accept or not. If accept, the sequence of deal.

3 Case 2: Andrew Olgilvie Assumption The most loss for Mr. Andrew Olgilvie is $1600.00 and we can assume that it’s within the risk tolerance of Mr. Andrew Olgilvie, owner of an art gallery. The measure of merit will be EMV. No discount rate will be considered for the time span is less than three months.

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5 Case 2: Andrew Olgilvie Analysis and Conclusion Decision tree provide insight into this case Andrew Olgilvie should accept the client’s offer for it will bring him $1648.00. After selling the portrait, he should continue with still life for it will bring more than landscape. At last, he should try to sell landscape.

6 Case 7: Slippery Jim Introduction Decision maker: Mr. Joe Swift Objective: Minimum cost Opportunity: Where he buys his camera and where he gets more information on the acceptability of the lens. Alternative: Buy from local retail store or from Slippery Jim. If Slippery Jim, whether to test the lens and how?

7 Case 7: Slippery Jim Assumption The most loss for Joe Swift is less than $100.00 and we assume that it’s within the risk tolerance of Joe Swift. The measure of merit will be EMV. No discount rate will be considered. If we want to take Joe Swift’s risk attitude into consideration, we must assume Joe Swift’s utility function.

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9 Case 7: Slippery Jim Analysis One thing need to be mentioned is how to get the value of perfect value: The EVPI = [0.7*(-270)+0.3*(-420)]-[0.7*(- 270)+0.3*(-510)]=-315-(-342)=27.00 Since the perfect information will cost $20.00, the test is worthy to do and Joe Swift should make decision on which information will minimize his cost, the perfect or the imperfect one.

10 Case 7: Slippery Jim Analysis-Con’d Another problem is that there is a $10.00 deposit for testing, which will be forfeited without purchasing. My treatment is that give $10.00 value to the decision on purchasing from Slippery Jim.

11 Case 7: Slippery Jim Conclusion Joe Swift should test the lens and get perfect information on the lens’ acceptability. Notice: Here assume that the utility for Joe Swift is the same once he get the workable camera and lens. ( The time and money needed to test the lens or fix the lens is not taken into consideration.)

12 Case 4: Sussman’s Department Introduction Decision maker: Mr. Sussman, the director of the Social Worker Department at L.P.H. The problem is allocation of staff to the various units in the department. The decision maker articulates his preference clearly. The fully employment of stuff is the most important.

13 Case 4: Sussman’s Department Analysis Information needed to solve this problem: 1. There should be some information about the alternatives. For example, Mr. Sussman should give alternatives on how to short- staff a unit by providing social worker service on a consultative basis.

14 Case 4: Sussman’s Department Analysis-Con’d Another issue should be clarified is whether the social worker can be assigned to different department at the same time. Can we change the assignment when it is needed?

15 Case 4: Sussman’s Department Analysis-Con’d The uncertainty here is the workload of different department at different time. According to the statistical data, we can get probabilities of them. But the risk for short- staffed clinical department should be given.

16 My solution This problem is typical assignment problem in linear programming. Since the problem is not articulated clearly, what we need to do is gathering more information. Then we can model it.


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