Common objective function for project selection problems

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Reference no: EM132256256

1. If you see 'Model Type - NLP Convex' in the Model Diagnosis area of the Task Pane Model tab of the Analytic Solver Platform (ASP) after you conducted a convexity test, you may conclude that:

a. The problem does not have a feasible solution.

b. The algorithm cannot find a local optimal solution.

c. A local optimal solution found is also a global optimal solution.

d. You minimized the number of decision variables.

2. When solving an NLP problem, Solver displayed the following completion message: “Solver found a solution. All constraints and optimality conditions are satisfied.” The message means that:

a. The objective function value changed very slowly for the last few iterations.

b. Solver found a global optimal solution.

c. Your model is degenerate and the Solver is cycling.

d. Solver found a local optimal solution, but does not guarantee that the solution is the global optimal solution.

3. One example of real-world NLP problems is:

a. The Economic Order Quantity (EOQ) model.

b. The assignment problem.

c. The transportation problem.

d. The generalized network flow problem.

4. A decision problem, in which the balance-of-flow restrictions must be maintained while optimizing a nonlinear objective function is called:

a. A balanced network flow problem.

b. A GRG problem.

c. A network flow problem.

d. A nonlinear network flow problem.

5. A common objective function for project selection problems is to:

a. Maximize the total portfolio return.

b. Minimize the total portfolio cost.

c. Maximize the net present value of the project portfolio.

d. Maximize customer value.

6. In a project selection problem formulation:

a. The objective function is nonlinear.

b. There cannot be any integer variables.

c. There cannot be any binary variables.

d. All variables are continuous

Reference no: EM132256256

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