tailieunhanh - Lecture Introduction to Management Science with Spreadsheets: Chapter 9 - Stevenson, Ozgur

Chapter 8 "Network optimization models", after completing this chapter, you should be able to: State why network models are important tools for problem solving, describe the kinds of problems that can be solved using the shortest-route algorithm and use the algorithm to solve typical shortest-route problems, formulate the shortest-route problem as a linear programming problem,. | Chapter 9 Nonlinear Programming Part 2 Deterministic Decision Models Learning Objectives Explain the difference between optimization problems that can be solved using linear programming methods and those that require nonlinear programming or calculus-based methods. Find the optimal values of the decision variable and the objective function in problems that involve one decision variable (unconstrained and constrained). Solve one-decision variable problems with a nonlinear objective function using Excel (unconstrained or constrained). Find the optimal values of the decision variables and the objective function in unconstrained problems that involve two decision variables. After completing this chapter, you should be able to: McGraw-Hill/Irwin 9– Learning Objectives (cont’d) Solve one-decision-variable unconstrained problems with a nonlinear objective function using Excel. Use the Lagrange multiplier to find the optimal values of two decision variables and the objective function in problems that involve equality constraints. Solve two-decision-variable problems with a nonlinear objective function and an equality constraint using Excel. Find the optimal values of the decision variables and the objective function in problems that involve two decision variables and one inequality constraint. Solve two-decision-variable problems with a nonlinear objective function and multiple constraints using Excel. After completing this chapter, you should be able to: McGraw-Hill/Irwin 9– Nonlinear Programming Characteristics of Nonlinear Models Have one or more nonlinear components which cannot be handled by linear programming techniques. Require nonlinear programming procedures which involves obtaining the first derivative of the objective function, finding all solutions for which the first derivative is equal to zero, and then checking second derivative conditions to ascertain the nature of the zero points (., a local maximum or a local minimum). McGraw-Hill/Irwin 9– | Chapter 9 Nonlinear Programming Part 2 Deterministic Decision Models Learning Objectives Explain the difference between optimization problems that can be solved using linear programming methods and those that require nonlinear programming or calculus-based methods. Find the optimal values of the decision variable and the objective function in problems that involve one decision variable (unconstrained and constrained). Solve one-decision variable problems with a nonlinear objective function using Excel (unconstrained or constrained). Find the optimal values of the decision variables and the objective function in unconstrained problems that involve two decision variables. After completing this chapter, you should be able to: McGraw-Hill/Irwin 9– Learning Objectives (cont’d) Solve one-decision-variable unconstrained problems with a nonlinear objective function using Excel. Use the Lagrange multiplier to find the optimal values of two decision variables and the objective function in .