Applications and Algorithms (with CD-ROM and InfoTrac)
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Wayne L. Winston is Professor Emeritus of Decision Sciences at the Kelley School of Business at Indiana University and is now Professor of Decision and Information Sciences at the Bauer College at the University of Houston. Dr. Winston has received more than 45 teaching awards and is a six-time recipient of the school-wide M.B.A. award. His current interest focuses on showing how to use spreadsheet models to solve business problems in all disciplines, particularly in finance, sports and marketing. In addition to publishing more than 20 articles in leading journals, Dr. Winston has written successful textbooks on topics including operations research, mathematical programming, simulation modeling, spreadsheet modeling, data analysis for managers, marketing analytics and more. Dr. Winston received his B.S. degree in mathematics from MIT and his Ph.D. in operations research from Yale.
1. INTRODUCTION TO MODEL BUILDING.
An Introduction to Modeling. The Seven-Step Model-Building Process. Examples.
2. BASIC LINEAR ALGEBRA.
Matrices and Vectors. Matrices and Systems of Linear Equations. The Gauss-Jordan Method for Solving Systems of Linear Equations. Linear Independence and Linear Dependence. The Inverse of a Matrix. Determinants.
3. INTRODUCTION TO LINEAR PROGRAMMING.
What is a Linear Programming Problem? The Graphical Solution of Two-Variable Linear Programming Problems. Special Cases. A Diet Problem. A Work-Scheduling Problem. A Capital Budgeting Problem. Short-term Financial Planning. Blending Problems. Production Process Models. Using Linear Programming to Solve Multiperiod Decision Problems: An Inventory Model. Multiperiod Financial Models. Multiperiod Work Scheduling.
4. THE SIMPLEX ALGORITHM AND GOAL PROGRAMMING.
How to Convert an LP to Standard Form. Preview of the Simplex Algorithm. The Simplex Algorithm. Using the Simplex Algorithm to Solve Minimization Problems. Alternative Optimal Solutions. Unbounded LPs. The LINDO Computer Package. Matrix Generators, LINGO, and Scaling of LPs. Degeneracy and the Convergence of the Simplex Algorithm. The Big M Method. The Two-Phase Simplex Method. Unrestricted-in-Sign Variables. Karmarkar''s Method for Solving LPs. Multiattribute Decision-Making in the Absence of Uncertainty: Goal Programming. Solving LPs with Spreadsheets.
5. SENSITIVITY ANALYSIS: AN APPLIED APPROACH.
A Graphical Introduction to Sensitivity Analysis. The Computer and Sensitivity Analysis. Managerial Use of Shadow Prices. What Happens to the Optimal z-value if the Current Basis is No Longer Optimal?
6. SENSITIVITY ANALYSIS AND DUALITY.
A Graphical Introduction to Sensitivity Analysis. Some Important Formulas. Sensitivity Analysis. Sensitivity Analysis When More Than One Parameter is Changed: The 100% Rule. Finding the Dual of an LP. Economic Interpretation of the Dual Problem. The Dual Theorem and Its Consequences. Shadow Prices. Duality and Sensitivity Analysis.
7. TRANSPORTATION, ASSIGNMENT, AND TRANSSHIPMENT PROBLEMS.
Formulating Transportation Problems. Finding Basic Feasible Solutions for Transportation Problems. The Transportation Simplex Method. Sensitivity Analysis for Transportation Problems. Assignment Problems. Transshipment Problems.
8. NETWORK MODELS.
Basic Definitions. Shortest Path Problems. Maximum Flow Problems. CPM and PERT. Minimum Cost Network Flow Problems. Minimum Spanning Tree Problems. The Network Simplex Method.
9. INTEGER PROGRAMMING.
Introduction to Integer Programming. Formulation Integer Programming Problems. The Branch-and-Bound Method for Solving Pure Integer Programming Problems. The Branch-and-Bound Method for Solving Mixed Integer Programming Problems. Solving Knapsack Problems by the Branch-and-Bound Method. Solving Combinatorial Optimization Problems by the Branch-and-Bound Method. Implicit Enumeration. The Cutting Plane Algorithm.
10. ADVANCED TOPICS IN LINEAR PROGRAMMING.
The Rev...