This text's emphasis is on presenting management science in a manner that is managerially focused and easily understood by students. This is done in part by using easy-to-understand examples demonstrating each technique in understandable contexts. The text is application oriented dealing with realistic problems emphasizing model formulation, computer-based solutions, and implementation of model results. The text uses models related to managerial application, which are used to demonstrate management science techniques. Techniques are illustrated by examples placed in a decision-making context. Model use is demonstrated by the computer without being tied to specific computer systems. The text presents a comprehensive yet easily readable coverage of all important management science techniques.
|Publisher:||Thomson Learning Custom Publishing|
|Edition description:||Older Edition|
About the Author
Sang M. Lee is currently the University Eminent Scholar, Regents Distinguished Professor, Chair of the Management Department, Executive Director of the Nebraska Productivity and Entrepreneurship Center, and Director of the Center for Albania Studies. He received his Ph.D. degree in Management from the University of Georgia in 1969. He served as Professor at Virginia Polytechnic Institute and State University prior to coming to the University of Nebraska in 1976.
Dr. Lee is currently President of the Pan-Pacific Business Association, an international scholarly society of over 4,000 members in 35 countries. He has organized 26 international conferences as the Program Chair. He is on the editorial board of 23 journals.
Dr. Lee is an internationally known expert in the fields of decision sciences, productivity management and global business. His pioneering work in Goal Programming and Multiple Objective Decision Making has been widely recognized throughout the world. He is a Senior Scientist of the Gallup Organization where he advises on global projects.
David L. Olson is the James & H.K. Stuart Professor in MIS and Othmer Professor at the University of Nebraska. He received his Ph.D. in Business from the University of Nebraska in 1981. He has published research in over 80 refereed journal articles, primarily on the topic of multiple objective decision-making. He teaches in the management information systems, management science, and operations management areas. He has authored the books Decision Aids for Selection Problems, Introduction to Information Systems Project Management, and Managerial Issues of Enterprise Resource Planning Systems and co-authored the books Decision Support Models and Expert Systems; Introduction to Management Science; Introduction to Simulation and Risk Analysis; Business Statistics: Quality Information for Decision Analysis; Statistics, Decision Analysis, and Decision Modeling; Multiple Criteria Analysis in Strategic Siting Problems, and Introduction to Business Data Mining.
He has made over 100 presentations at international and national conferences on research topics. He is a member of the Association for Information Systems, the Decision Sciences Institute, the Institute for Operations Research and Management Sciences, and the Multiple Criteria Decision Making Society. He has coordinated the Decision Sciences Institute Dissertation Competition, Innovative Education Competition, chaired the Doctoral Affairs Committee, served as nationally elected vice president three times, and as National Program Chair. He was with Texas A&M University from 1981 through 2001, the last two years as Lowry Mays Professor of Business in the Department of Information and Operations Management.
He received a Research Fellow Award from the College of Business and Graduate School of Business at Texas A&M University, and held the Business Analysis Faculty Excellence Fellowship for two years. He is a Fellow of the Decision Sciences Institute.
Table of Contents
PART I: INTRODUCTION: 1. The Role of Management Science 2. Modeling in Management Science PART II: OPTIMIZATION MODELS: 3. Introduction to Linear Programming 4. Simplex Method of Linear Programming 5. Duality, Sensitivity Analysis, and Computer Solutions of Linear Programming 6. Integer and Zero-one Programming 7. Goal Programming 8. The Transportation Problem 9. The Assignment Problem 10. Network Models 11. Nonlinear Programming PART III: OTHER QUANTITATIVE TECHNIQUES: 12. Project Planning with PERT and CPM 13. Decision Theory 14. Analytic Hierarchy Process 15. Inventory Models 16. Waiting Line (Queuing) Models Appendix: Supplementary Section on Waiting Line Models 17. Dynamic Programming 18. Simulation 19. Forecasting 20. Markov Analysis PART IV: RELATIONSHIP TO INFORMATION SYSTEMS: 21. Management Science Implementation and Decision Support Systems Appendix 1: Poisson Probability Values Appendix 2: Values of ex and eBx Appendix 3: Values of Po for Various Combinations of