Learn today's management science concepts and techniques from a leader in the field. The key purpose of this book is to provide a sound conceptual understanding of the role that management science plays in the decision-making process. AN INTRODUCTION TO MANAGEMENT SCIENCE is applications-oriented and continues to use the problem-scenario approach in which a problem is described in conjunction with the management science model that's introduced. The model is then solved to generate a solution and recommendation to management.
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Dr. David R. Anderson is a textbook author and Professor Emeritus of Quantitative Analysis in the College of Business Administration at the University of Cincinnati. He has served as head of the Department of Quantitative Analysis and Operations Management and as Associate Dean of the College of Business Administration. He was also coordinator of the College's first Executive Program. In addition to 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 received numerous honors for excellence in teaching and service to student organizations. He is the coauthor of ten textbooks related to decision sciences and actively consults with businesses in the areas of sampling and statistical methods. Born in Grand Forks, North Dakota, he earned his BS, MS, and PhD degrees from Purdue University.Review:
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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