tailieunhanh - Lecture Introduction to Management Science with Spreadsheets: Chapter 4S - Stevenson, Ozgur

Chapter 4 - Supplement "Linear programming: The simplex method", after completing this chapter, you should be able to: Explain the ways in which the simplex method is superior to the graphical method for solving linear programming problems, solve small maximization problems manually using the simplex method, interpret simplex solutions,. | Chapter 4 Supplement Linear Programming: The Simplex Method Part 2 Deterministic Decision Models Learning Objectives Explain the ways in which the simplex method is superior to the graphical method for solving linear programming problems. Solve small maximization problems manually using the simplex method. Interpret simplex solutions. Convert = and constraints into standard form. Solve maximization problems that have mixed constraints and interpret those solutions. After completing this chapter, you should be able to: > McGraw-Hill/Irwin 4S– Learning Objectives (cont’d) Solve minimization problems and interpret those solutions. Discuss unbound solutions, degeneracy, and multiple optimal solutions in terms of the simplex method and recognize infeasibility in a simplex solution. After completing this chapter, you should be able to: McGraw-Hill/Irwin 4S– Overview of the Simplex Method Advantages and Characteristics More realistic approach as it is not limited to problems with two decision variables Systematically examines basic feasible solutions for an optimal solution. Based on the solutions of linear equations (equalities) using slack variables to achieve equality. Rule Linear programming models have fewer equations than variables; unless the number of equations equals the number of variables, a unique solution cannot be found. McGraw-Hill/Irwin 4S– Developing the Initial Simplex Tableau Notation used in the simplex tableau: McGraw-Hill/Irwin 4S– Figure 4S–1 Comparison of Server Model and General Simplex Notation McGraw-Hill/Irwin 4S– Table 4S–1 Comparison of Server Model and General Simplex Notation (cont’d) McGraw-Hill/Irwin 4S– Table 4S–2 Completed Initial Tableau for the Server Problem Each tableau represents a basic feasible solution to the problem. Unit Vector A simplex solution in a maximization problem is optimal if the C–Z row consists entirely of zeros and negative numbers (., there are no positive values in the bottom row). . | Chapter 4 Supplement Linear Programming: The Simplex Method Part 2 Deterministic Decision Models Learning Objectives Explain the ways in which the simplex method is superior to the graphical method for solving linear programming problems. Solve small maximization problems manually using the simplex method. Interpret simplex solutions. Convert = and constraints into standard form. Solve maximization problems that have mixed constraints and interpret those solutions. After completing this chapter, you should be able to: > McGraw-Hill/Irwin 4S– Learning Objectives (cont’d) Solve minimization problems and interpret those solutions. Discuss unbound solutions, degeneracy, and multiple optimal solutions in terms of the simplex method and recognize infeasibility in a simplex solution. After completing this chapter, you should be able to: McGraw-Hill/Irwin 4S– Overview of the Simplex Method Advantages and Characteristics More realistic approach as it is not limited to problems with two

• Quản trị kinh doanh
• Management science
• Management science with spreadsheets
• Deterministic decision models
• Probabilistic decision models
• Linear programming
• Simplex method
• Applications of management science
• Excel spreadsheets
• Management thought
• Evolution of management thought
• Ideas in management
• Management theory
• Systems in management
• Production management
• Forecasting process
• Qualitative forecasts
• Graphical solution
• Marketing applications
• Sensitivity analysis
• Transportation method
• Solving transportation problems
• Integer programming
• Integer programming problems
• Solving integer programming
• Nonlinear programming
• Network optimization models
• Differential arithmetic
• Multicriteria decision
• Making models
• Decision theory
• Decision making
• Waiting line models
• Poisson distribution
• Fixed Interval simulations
• Discrete simulations
• An introduction to management science
• Advanced linear programming applications
• Integer linear programming
• Markov processes
• Nonlinear optimization models
• Inventory models
• Decision analysis