tailieunhanh - Lecture Introduction to Management Science with Spreadsheets: Chapter 7 - Stevenson, Ozgur

Chapter 7 "Integer programming", after completing this chapter, you should be able to: Tell how integer programming problems differ from general linear programming problems; explain the difference among pure, mixed, and 0–1 integer programming problems; formulate and use Excel to solve integer programming problems;. | Chapter 7 Integer Programming Part 2 Deterministic Decision Models Learning Objectives Tell how integer programming problems differ from general linear programming problems. Explain the difference among pure, mixed, and 0–1 integer programming problems. Formulate and use Excel to solve integer programming problems. Formulate and use Excel to solve 0–1 integer programming problems. Formulate specialized integer programming problems including knapsack, set covering, fixed charge, and facility location problems. After completing this chapter, you should be able to: McGraw-Hill/Irwin 7– Types of Integer Programming Problems Pure-Integer Problems require that all decision variables have integer solutions. Mixed-Integer Problems Require some, but not all, of the decision variables to have integer values in the final solution, whereas others need not have integer values. 0–1 Integer Problems Require integer variables to have value of 0 or 1, such as situations in which decision variables are of the yes-no type. McGraw-Hill/Irwin 7– Figure 7–1 Graph of an Integer Programming Problem McGraw-Hill/Irwin 7– Example 7-1 McGraw-Hill/Irwin 7– Example 7-1 (cont’d) McGraw-Hill/Irwin 7– Exhibit 7-1 Input and Output Worksheet for the Boat-Manufacturing Example McGraw-Hill/Irwin 7– Exhibit 7-2 Solver Parameters Screen for the Boat-Manufacturing Problem McGraw-Hill/Irwin 7– Exhibit 7–3 Integer Requirement Specification Exhibit 7–4 Solver Results Screen McGraw-Hill/Irwin 7– Exhibit 7–4 Solver Results Screen McGraw-Hill/Irwin 7– Integer Programming Problems and Sensitivity Analysis Integer programming problems do not readily lend themselves to sensitivity analysis as only a relatively few of the infinite solution possibilities in a feasible solution space will meet integer requirements. Trial-and-error examination of a range of reasonable alternatives involving completely solving each revised problem is required. McGraw-Hill/Irwin 7– Formulating . | Chapter 7 Integer Programming Part 2 Deterministic Decision Models Learning Objectives Tell how integer programming problems differ from general linear programming problems. Explain the difference among pure, mixed, and 0–1 integer programming problems. Formulate and use Excel to solve integer programming problems. Formulate and use Excel to solve 0–1 integer programming problems. Formulate specialized integer programming problems including knapsack, set covering, fixed charge, and facility location problems. After completing this chapter, you should be able to: McGraw-Hill/Irwin 7– Types of Integer Programming Problems Pure-Integer Problems require that all decision variables have integer solutions. Mixed-Integer Problems Require some, but not all, of the decision variables to have integer values in the final solution, whereas others need not have integer values. 0–1 Integer Problems Require integer variables to have value of 0 or 1, such as situations in which decision variables

• Quản trị kinh doanh
• Management science
• Management science with spreadsheets
• Deterministic decision models
• Probabilistic decision models
• Integer programming
• Integer programming problems
• 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
• Linear programming
• Graphical solution
• Marketing applications
• Simplex method
• Sensitivity analysis
• Transportation method
• Solving transportation 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