Quantitative Methods for Business

Dr. Jeffrey D. Camm is Professor of Quantitative Analysis, Head of the Department of Operations and Business Analytics, and College of Business Research Fellow in the College of Business at the University of Cincinnati. Dr. Camm holds a B.S. from Xavier University and a Ph.D. from Clemson University. He has served at the University of Cincinnati since 1984 and has 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 more than 30 papers in the general area of optimization applied to problems in operations management. He has published his research in Science, Management Science, Operations Research, Interfaces and other professional journals. At the University of Cincinnati, he was named the Dornoff Fellow of Teaching Excellence 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 2005-2010 he served as editor-in-chief of Interfaces, and is currently on the editorial board of INFORMS Transactions on Education. 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. 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. 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. 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.

1. Introduction. 2. Introduction to Probability. 3. Probability Distributions. 4. Decision Analysis. 5. Utility and Game Theory. 6. Forecasting. 7. Introduction to Linear Programming. 8. Linear Programming: Sensitivity Analysis and Interpretation of Solution. 9. Linear Programming Applications in Marketing, Finance, and Operations Management. 10. Distribution and Network Models. 11. Integer Linear Programming. 12. Advanced Optimization Applications 13. Project Scheduling: PERT/CPM. 14. Inventory Models. 15. Waiting Line Models. 16. Simulation. 17. Markov Processes. Appendixes A-G. Index.

1. Introduction. 2. Introduction to Probability. 3. Probability Distributions. 4. Decision Analysis. 5. Utility and Game Theory. 6. Forecasting. 7. Introduction to Linear Programming. 8. Linear Programming: Sensitivity Analysis and Interpretation of Solution. 9. Linear Programming Applications in Marketing, Finance, and Operations Management. 10. Distribution and Network Models. 11. Integer Linear Programming. 12. Advanced Optimization Applications 13. Project Scheduling: PERT/CPM. 14. Inventory Models. 15. Waiting Line Models. 16. Simulation. 17. Markov Processes. Appendixes A-G. Index.