Best-selling text in the market. Introduction to Management Science, 9e blends problem formulation, managerial interpretation, and math techniques with an emphasis on problem solving. The problem-scenario approach introduces quantitative procedures through situations that include both problem formulation and technique application
Extensive linear programming coverage includes problem formulation, computer solution, and practical application. Text covers transportation, assignment, and the integer programming extension of linear programming, as well as advanced topics like waiting line models, simulation, and decision analysis. Large selection of problems includes self-test problems with complete solutions and 20 case problems. Excel spreadsheet appendices are included in this edition.
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David R. Anderson is Professor of Quantitative Analysis in the College of Business Administration at the University of Cincinnati. Born in Grand Forks, North Dakota, he earned his BS, MS, and PhD degrees from Purdue University. Professor Anderson has served as Head of the Department of Quantitative Analysis and Operations Management and as Associate Dean of the College of Business Administration. In addition, he was the coordinator of the College's first Executive Program. In addition to teaching introductory statistics for business students, Dr. Anderson has taught graduate-level courses in regression analysis, multivariate analysis, and management science. He also has taught statistical courses at the Department of Labor in Washington, D.C. Professor Anderson has been honored with nominations and awards for excellence in teaching and excellence in service to student organizations. He has coauthored ten textbooks related to decision sciences and actively consults with businesses in the areas of sampling and statistical methods.
1. Introduction. 2. An Introduction to Linear Programming. 3. Linear Programming: Sensitivity Analysis and Interpretation of Solution. 4. Linear Programming Applications in Marketing, Finance, and Operations Management. 5. Advanced Linear Programming Applications. 6. Distribution and Network Models. 7. Integer Programming. 8. Nonlinear Optimization Models. 9. Project Scheduling: PERT/CPM. 10. Inventory Models. 11. Waiting Line Models. 12. Simulation. 13. Decision Analysis. 14. Multicriteria Decisions. 15. Forecasting. 16. Markov Processes. On the Student CD: 17. Linear Programming: Simplex Method. 18. Simplex-Based Sensitivity Analysis and Duality. 19. Solution Procedures for Transportation and Assignment Problems. 20. Minimal Spanning Tree Algorithm. 21. Dynamic Programming.
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