An Introduction to Management Science: Quantitative Approach to Decision Making (With CD) 13thEdition

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Introduce your students to management science techniques with the thorough, applications-oriented coverage you can trust from the definitive leader in traditional management science texts. The best-selling Anderson/Sweeney/Williams/Martin's INTRODUCTION TO MANAGEMENT SCIENCE: A QUANTITATIVE APPROACH TO DECISION MAKING, 13E, has helped define the topical coverage presented within today's management science course curriculum. This book provides a thorough grounding in management science techniques with a readable presentation style and a wealth of examples drawn from a variety of businesses throughout the world. Students learn the techniques and refine their problem solving skills with realistic problems that continue to set this established leader apart. 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, Microsoft Excel, and many other valuable software packages to resolve management science problems. In response to feedback from instructors like you, this edition now places greater emphasis on the applications of management science and use of computer software with much of the focus on algorithms moved to optional chapters on the accompanying Student CD for your flexibility. As always, the well-respected authors have continued their reputation for excellent and accuracy with error-free presentations throughout the text, test bank, and supplements Salient Features Trusted team of expert authors who are respected leaders and active consultants in the fields of business and statistics, provide unerring accuracy throughout the text presentation, problems, and test bank. Proven problem-scenario approacha hallmark feature of this textintroduces each quantitative technique within an application setting. Students must apply the technique to a problem to generate a business solution or recommendation. Powerful actual examples offers even more real data that emphasizes application as well as solid management science and quantitative methodology. Integrated coverage of software applications most useful in today''s business world equips students to use not only the new LINGO 10, but also Excel with quantitative add-ins. Coverage of Management Scientist, LINGO, and Excel with add-ins Crystal Ball, TreePlan, and Premium Solver for Education appear in appendices and on the Student CD for maximum teaching flexibility. Dynamic, updated Student CD that accompanies the text now includes five chapters focusing on solution algorithms that were previously in the text; Excel worksheets and templates that correspond with text examples and models; the new Excel add-ins Crystal Ball Professional v7.2.1 (time-limited), Premium Solver for Education v6, and TreePlan v1.64 Excel add-in for Decision Analysis; the new LINGO 10 trial edition software, and The Management Scientist 6.0 About the Author 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 Colleges 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. 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 complete 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.. Table of Contents 1. Introduction. 2. An Introduction to Linear Programming. 3. Linear Programming: Sensitivity Analysis and Interpretation of Solution. 4. Linear Programming Applications in Marketing, Finance, and Operations Management. 5. Advanced Linear Programming Applications. 6. Distribution and Network Models. 7. Integer Programming. 8. Nonlinear Optimization Models. 9. Project Scheduling: PERT/CPM. 10. Inventory Models. 11. Waiting Line Models. 12. Simulation. 13. Decision Analysis. 14. Multicriteria Decisions. 15. Forecasting. 16. Markov Processes. On the Student CD: 17. Linear Programming: Simplex Method. 18. Simplex-Based Sensitivity Analysis and Duality. 19. Solution Procedures for Transportation and Assignment Problems. 20. Minimal Spanning Tree Algorithm. 21. Dynamic Programming