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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Dr. David R. Anderson is Professor of Quantitative Analysis in the College of Business Administration at the University of Cincinnati. He earned his B.S., M.S., and Ph.D. 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. At the University of Cincinnati, Professor Anderson has taught introductory statistics for business students as well as graduate-level courses in regression analysis, multivariate analysis, and management science. He has also taught statistical courses at the Department of Labor in Washington, D.C. He has been honored with numerous nominations and awards for excellence in teaching and excellence in service to student organizations. Professor Anderson has co-authored 10 leading textbooks in the areas of statistics, management science, linear programming, and production and operations management. He is an active consultant in the field of sampling and statistical methods.
Dr. Dennis J. Sweeney is Professor 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. Professor Sweeney has published more than 30 articles and monographs 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. Professor Sweeney has co-authored 10 leading texts in the areas of statistics, management science, linear programming, and production and operations management.
Dr. Thomas A. Williams is Professor 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. 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. At RIT 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. Professor Williams is the co-author of 11 leading textbooks in the areas of management science, statistics, production and operations management, and mathematics. He 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.
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.
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Book Description South-Western College Pub, 2007. Hardcover. Book Condition: New. 12. Bookseller Inventory # DADAX0324399804
Book Description Cengage Learning, 2007. Hardcover. Book Condition: New. book. Bookseller Inventory # 0324399804