An Introduction to Management Science: Quantitative Approaches to Decision Making

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Cengage Learning, Sep 17, 2007 - Business & Economics - 848 pages
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Learn today's management science concepts and techniques--and how they will benefit you in the classroom and business world beyond--with the definitive leader in management science, INTRODUCTION TO MANAGEMENT SCIENCE: A QUANTITATIVE APPROACH TO DECISION MAKING, 12E. The latest edition of this leading text blends a readable style with a wealth of examples that demonstrate how businesses throughout the world use management science techniques to further their success. Proven, realistic problems help strengthen critical problem-solving skills, while numerous self-test exercises with complete solutions allow you to immediately check your personal understanding of the material. Every new edition now includes the highly respected LINGO 10 software that is integrated with text problems to help you develop the skills to use this, Excel, and many other valuable software packages to resolve management science problems. This edition now places greater emphasis on the applications of management science and use of computer software with less focus on algorithms. Much of the algorithm coverage as well as Excel templates and add-in software, and the user-friendly Management Scientist software are available on the text's accompanying Student CD. Trust INTRODUCTION TO MANAGEMENT SCIENCE, 12E to introduce the management science skills you need now and into the future with clarity you can understand and practicality you can immediately apply.
  

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Contents

An Introduction to Linear Programming
30
Linear Programming Sensitivity Analysis and Interpretation of Solution
94
Linear Programming Applications in Marketing Finance and Operations Management
159
Advanced Linear Programming Applications
218
Distribution and Network Models
260
Integer Linear Programming
318
Nonlinear Optimization Models
366
Project Scheduling PERTCPM
410
Waiting Line Models
496
Simulation
536
Decision Analysis
595
Multicriteria Decisions
650
Forecasting
695
Markov Processes
748
Appendixes
773
Index
807

Inventory Models
447

Common terms and phrases

About the author (2007)

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.

Dr. Dennis J. Sweeney is Professor Emeritus of Quantitative Analysis and Founder of the Center for Productivity Improvement at the University of Cincinnati. He earned a B.S.B.A. degree from Drake University and his M.B.A. and D.B.A. degrees from Indiana University, where he was an NDEA Fellow. Professor Sweeney has worked in the management science group at Procter & Gamble and has served as visiting professor at Duke University. Professor Sweeney has also served as Head of the Department of Quantitative Analysis and as Associate Dean of the College of Business Administration at the University of Cincinnati.

Dr. Thomas A. Williams is Professor Emeritus of Management Science in the College of Business at Rochester Institute of Technology. He earned his B.S. degree at Clarkson University. He completed his graduate work at Rensselaer Polytechnic Institute, where he received his M.S. and Ph.D. degrees.

Kipp Martin is Professor of Operations Research and Computing Technology at the Graduate School of Business, University of Chicago. Born in St.Bernard, Ohio, he earned a B.A. in Mathematics, an MBA, and a Ph.D. in Management Science from the University of Cincinnati. While at the University of Chicago, Professor Martin has taught courses in Management Science, Operations Management, Business Mathematics, and Information Systems. Research interests include incorporating Web technologies such as XML, XSLT, XQuery, and Web Services into the mathematical modeling process; the theory of how to construct good mixed integer linear programming models; symbolic optimization; polyhedral combinatorics; methods for large scale optimization; bundle pricing models; computing technology and database theory. Professor Martin has published in INFORMS Journal of Computing, Management Science, Mathematical Programming, Operations Research, The Journal of Accounting Research, and other professional journals. He is also the author of The Essential Guide to Internet Business Technology (with Gail Honda) and Large Scale Linear and Integer Optimization.

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