An Introduction to Management Science

Dr. David R. Anderson is a leading 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. Dr. Anderson has received numerous honors for excellence in teaching and service to student organizations. He is the co-author 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 B.S., M.S. and Ph.D. degrees from Purdue University. Dr. Dennis J. Sweeney is a leading 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. Dr. 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 professional journals such as Management Science, Operations Research, Mathematical Programming and Decision Sciences. Dr. Sweeney is the co-author 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 B.S. degree from Drake University, graduating summa cum laude. He received his M.B.A. and D.B.A. degrees from Indiana University, where he was an NDEA Fellow. Dr. Thomas A. Williams is both a prominent author and Professor Emeritus 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 taught courses in management science and statistics, as well as graduate courses in regression and decision analysis. 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 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, Dr. 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. Dr. Jeffrey D. Camm is the Inmar Presidential Chair and Associate Dean of Business 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 40 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. In 2016, Professor Camm received the George E. Kimball Medal for service to the operations research profession and in 2017 he was named an INFORMS Fellow. Michael J. Fry is Professor and Academic Director of the Center for Business Analytics in the Carl H. Lindner College of Business at the University of Cincinnati. Born in Killeen, Texas, he earned a B.S. from Texas A&M University, and M.S.E. and Ph.D. degrees from the University of Michigan. He has been at the University of Cincinnati since 2002, where he has previously served as Head of the Department of Operations, Business Analytics, and Information Systems and has been named a Lindner Research Fellow. He has also been a visiting professor at the Samuel Curtis Johnson Graduate School of Management at Cornell University and the Sauder School of Business at the University of British Columbia. Professor Fry has published over twenty-five research papers in journals such as Operations Research, M&SOM, Transportation Science, Naval Research Logistics, IIE Transactions, and Interfaces. His research interests are in applying quantitative management methods to the areas of supply chain analytics, sports analytics, and public-policy operations. He has worked with many different organizations for his research, including Dell, Inc., Copeland Corporation, Starbucks Coffee Company, Great American Insurance Group, the Cincinnati Fire Department, the State of Ohio Election Commission, the Cincinnati Bengals, and the Cincinnati Zoo. In 2008, he was named a finalist for the Daniel H. Wagner Prize for Excellence in Operations Research Practice, and he has been recognized for both his research and teaching excellence at the University of Cincinnati. Jeffrey W. Ohlmann is Associate Professor of Management Sciences and Huneke Research Fellow in the Tippie College of Business at the University of Iowa. Born in Valentine, Nebraska, he earned a B.S. from the University of Nebraska, and M.S. and Ph.D. degrees from the University of Michigan. He has been at the University of Iowa since 2003. Professor Ohlmann’s research on the modeling and solution of decision-making problems has produced over twenty research papers in journals such as Operations Research, Mathematics of Operations Research, INFORMS Journal on Computing, Transportation Science, the European Journal of Operational Research, and Interfaces. He has collaborated with companies such as Transfreight, LeanCor, Cargill, the Hamilton County Board of Elections, and three National Football League franchises. Due to the relevance of his work to industry, he was bestowed the George B. Dantzig Dissertation Award and was recognized as a finalist for the Daniel H. Wagner Prize for Excellence in Operations Research Practice. James J. Cochran is Associate Dean for Research, Professor of Applied Statistics and the Rogers-Spivey Faculty Fellow at The University of Alabama. Born in Dayton, Ohio, he earned his B.S., M.S., and M.B.A. from Wright State University and Ph.D. from the University of Cincinnati. He has been at The University of Alabama since 2014 and has been a visiting scholar at Stanford University, Universidad de Talca, the University of South Africa and Pole Universitaire Leonard de Vinci. Dr. Cochran has published more than three dozen papers in the development and application of operations research and statistical methods. He has published in Management Science, The American Statistician, Communications in Statistics—Theory and Methods, Annals of Operations Research, European Journal of Operational Research, Journal of Combinatorial Optimization. Interfaces and Statistics and Probability Letters. He received the 2008 INFORMS Prize for the Teaching of Operations Research Practice, 2010 Mu Sigma Rho Statistical Education Award and 2016 Waller Distinguished Teaching Career Award from the American Statistical Association. Dr. Cochran was elected to the International Statistics Institute in 2005 and named a Fellow of the American Statistical Association in 2011. He also received the Founders Award in 2014 and the Karl E. Peace Award in 2015 from the American Statistical Association. A strong advocate for effective operations research and statistics education as a means of improving the quality of applications to real problems, Dr. Cochran has chaired teaching effectiveness workshops around the globe. He has served as operations research consultant to numerous companies and not-for-profit organizations. He served as editor-in-chief of INFORMS Transactions on Education and is on the editorial board of Interfaces, International Transactions in Operational Research and Significance.

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 Linear 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. Time Series Analysis and Forecasting.
16. Markov Processes.
17. Linear Programming: Simplex Method (on Website).
18. Simplex-Based Sensitivity Analysis and Duality (on Website).
19. Solutions Procedures for Transportation and Assignment Problems (on Website).
20. Minimal Spanning Tree (on Website).
21. Dynamic Programming (on Website).
Appendix A: Building Spreadsheet Models.
Appendix B: Areas for the Standard Normal Distribution.
Appendix C: Values of e–?.
Appendix D: References and Bibliography.
Appendix E: Self-Test Solutions and Answers to Even-Numbered Problems.