Introduction To Management Science 11th Edition

'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.'

ASW's Introduction to Management Science: A Quantitative Approach to Decision Making provides thorough, application-oriented coverage in a very readable writing style. This is the best traditional text on the market. Simply put, this is a classic! The problem-scenario approach introduces quantitative procedures through situations that include both problem formulation and technique application. The extensive linear programming coverage includes problem formulation, computer solution, and practical application. The text covers transportation, assignment, and the integer programming extension of linear programming, as well as advanced topics like waiting line models, simulation, and decision analysis. A large selection of problems includes self-test problems with complete solutions and case problems. Excel spreadsheet appendices are included in this edition as well.

Dr. 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. He was also 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. Dr. 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 of Quantitative Analysis and founder of the Center for Productivity Improvement at the University of Cincinnati. Born in Des Moines, Iowa, he earned BS and BA degrees from Drake University, graduating summa cum laude. He received his MBA and DBA degrees from Indiana University, where he was an NDEA Fellow. Dr. Sweeney has worked in the management science group at Procter and Gamble and has been a visiting professor at Duke University. Professor Sweeney served five years as Head of the Department of Quantitative Analysis and four years as Associate Dean of the College of Business Administration at the University of Cincinnati. He has published more than 30 articles in the area of management science and statistics. The National Science Foundation, IBM, Procter and Gamble, Federated Department Stores, Kroger, and Cincinnati Gas and Electric have funded his research, which has been published in Management Science, Operations Research, Mathematical Programming, Decision Sciences, and other journals. Dr. Sweeney has coauthored ten textbooks 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 (RIT). Born in Elmira, New York, he earned his BS degree at Clarkson University. He completed his graduate work at Rensselaer Polytechnic Institute, where he received his MS and PhD degrees. Before joining the College of Business at RIT, Dr. Williams served for seven years as a faculty member in the College of Business Administration at the University of Cincinnati, where he developed the first undergraduate program in Information Systems. At RIT he was the first chair of the Decision Sciences Department. Dr. Williams is the coauthor of 11 textbooks in the areas of management science, statistics, production and operations management, and mathematics. He has been a consultant for numerous Fortune 500 companies in areas ranging from the use of elementary 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.

Preface. About the Authors. 1. Introduction. 2. An Introduction To Linear Programming. 3. Linear Programming: Sensitivity Analysis and Interpretation of Solution. 4. Linear Programming Applications. 5. Linear Programming: The Simplex Method. 6. Simplex-Based Sensitivity Analysis and Duality. 7. Transportation, Assignment, and Transshipment Problems. 8. Integer Linear Programming. 9. Network Models. 10. Project Scheduling: PERT/CPM. 11. Inventory Models. 12. Waiting Line Models. 13. Simulation. 14. Decision Analysis. 15. Multicriteria Decisions. 16. Forecasting. 17. Markov Processes. 18. Dynamic Programming. Appendix A. Areas For The Standard Normal Distribution. Appendix B. Values Of E-l. Appendix C. References And Bibliography. Appendix D. Solutions To Self-Test Problems and Answers To Even-Numbered Problems.