Quantitative Analysis for Management：管理定量分析.ppt

1 - # Chapter Outline 7.1 Introduction 7.2 Requirements of a Linear Programming Problem 7.3 Formulating LP Problems 7.4 Graphical Solution to an LP Problem 7.5 Solving Flair Furniture’s LP Problem using QM for Windows and Excel Chapter Outline - continued 7.6 Solving Minimization Problems 7.7 Summary of the Graphical Solution Methods 7.8 Four Special Cases 7.9 Sensitivity Analysis in LP Learning Objectives Students will be able to: Understand the basic assumptions and properties of linear programming (LP). Formulate small to moderate-sized LP problems. Graphically solve any LP problem with two variables by both the corner point and isoline methods. Learning Objectives - continued Understand special issues in LP - infeasibility, unboundedness, redundancy, and alternative optima. Understand the role of sensitivity analysis. Use Excel spreadsheets to solve LP problems, Examples of Successful LP Applications 1. development of a production schedule that will satisfy future demands for a firm’s production and at the same time minimize total production and inventory costs 2. selection of the product mix in a factory to make best use of machine-hours and labor-hours available while maximizing the firm’s products 3. determination of grades of petroleum products to yield the maximum profit 4. selection of different blends of raw materials to feed mills to produce finished feed combinations at minimum cost 5. determination of a distribution system that will minimize total shipping cost from several warehouses to various market locations Requirements of a Linear Programming Problem 1. All problems seek to maximize or minimize some quantity (the objective function). 2. The presence of restrictions or constraints, limits the degree to which we can pursue our objective. 3. There must be alternative courses of action to choose from. 4. The objective and constraints in linear programming problems must be expressed in terms of linear equations or inequalities. Basic Assumptions o