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Gain a sound conceptual understanding of the role that management science plays in the decision-making process with the latest edition of the book that has defined today's management science course: Anderson/Sweeney/Williams/Camm/Martin's AN INTRODUCTION TO MANAGEMENT SCIENCE: QUANTITATIVE APPROACHES TO DECISION MAKING, REVISED 13th Edition. The trusted market leader for more than two decades, the new edition now prepares readers for success with the latest developments in Microsoft Office Excel 2010, including data sets, applications and screen visuals throughout that reflect Excel 2010. Readers learn from the book's proven applications-oriented approach, powerful examples, and problem-scenario approach that introduces each quantitative technique within an applications setting. Readers can get a copy of LINGO software and Excel add-ins with the book's online content. A copy of the popular Microsoft Project Professional 2010 accompanies the book on CD.
- Sales Rank: #15004 in Books
- Published on: 2011-03-04
- Original language: English
- Number of items: 1
- Dimensions: 1.60" h x 8.10" w x 10.10" l, 3.85 pounds
- Binding: Hardcover
- 896 pages
About the Author
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.
Dr. Dennis J. Sweeney is a textbook author, Professor Emeritus of Quantitative Analysis and founder of the Center for Productivity Improvement at the University of Cincinnati. He also served five years as head of the Department of Quantitative Analysis and four years as Associate Dean of the College of Business Administration. In addition, he has worked in the management science group at Procter & Gamble and has been a visiting professor at Duke University. Professor Sweeney has published more than 30 articles in the area of management science and statistics. The National Science Foundation, IBM, Procter & Gamble, Federated Department Stores, Kroger, and Cincinnati Gas & Electric have funded his research, which has been published in MANAGEMENT SCIENCE, OPERATIONS RESEARCH, MATHEMATICAL PROGRAMMING, DECISION SCIENCES, and other journals. Dr. Sweeney is the coauthor of ten textbooks in the areas of statistics, management science, linear programming, and production and operations management. Born in Des Moines, Iowa, he earned a BS degree from Drake University, graduating summa cum laude. He received his MBA and DBA degrees from Indiana University, where he was an NDEA Fellow.
Dr. Thomas A. Williams is Professor of Management Science in the College of Business at Rochester Institute of Technology where he was the first chairman of the Decision Sciences Department. He teaches courses in management science and statistics, as well as graduate courses in regression and decision analysis. Before joining the College of Business at RIT, Professor Williams served for seven years as a faculty member in the College of Business Administration at the University of Cincinnati, where he developed the undergraduate program in Information Systems and then served as its coordinator. The co-author of 11 leading textbooks in the areas of management science, statistics, production and operations management, and mathematics, Professor Williams has been a consultant for numerous Fortune 500 companies and has worked on projects ranging from the use of data analysis to the development of large-scale regression models. He earned his B.S. degree at Clarkson University and completed his graduate work at Rensselaer Polytechnic Institute, where he received his M.S. and Ph.D. degrees.
Jeffrey D. Camm is the Inmar Presidential Chair and Associate Dean of Analytics in the School of Business at Wake Forest University. Born in Cincinnati, Ohio, he holds a B.S. from Xavier University (Ohio) and a Ph.D. from Clemson University. Prior to joining the faculty at Wake Forest, he was on the faculty of the University of Cincinnati. He has also been a visiting scholar at Stanford University and a visiting professor of business administration at the Tuck School of Business at Dartmouth College. Dr. Camm has published over 30 papers in the general area of optimization applied to problems in operations management and marketing. He has published his research in Science, Management Science, Operations Research, Interfaces, and other professional journals. Dr. Camm was named the Dornoff Fellow of Teaching Excellence at the University of Cincinnati and he was the 2006 recipient of the INFORMS Prize for the Teaching of Operations Research Practice. A firm believer in practicing what he preaches, he has served as an operations research consultant to numerous companies and government agencies. From 2005 to 2010 he served as editor-in-chief of interfaces.
Dr. 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. Dr. 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.
Most helpful customer reviews
17 of 23 people found the following review helpful.
Deceptive Description
By NS2015
This textbook shipped promptly and is in perfect condition. However, the Micrsoft Project Professional 2010 that accomanies the book is only a 60 day trial. Nowhere is this stated in the item description. This was the main reason I purchased this product for a $50.00 premium over my school bookstore's copy. This needs to be corrected by Amazon promptly so no one else makes the same mistake.
3 of 3 people found the following review helpful.
Good quality but wrong book
By Ashley Thomas
I ordered a hardcover revised version, but a soft-covered book came and it was the international version. Thankfully it still worked for my class
2 of 2 people found the following review helpful.
Difficult Concepts, But Fun Math
By Dumbledore's Army
This course requires a lot of time and thinking, but the book is excellent in its approach to walking you through the problems and when to use certain models. Of all the courses I've taken online, this is the first time I can honestly say that the book alone is sufficient for learning. I haven't needed a tutor or the need to purchase online access codes (a pox on McGraw-Hill for their charades) because this book is great! Thank you, Mr. Anderson, for doing what is rarely done in the world of text books--you've made the material understandable.
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