• Notes and Comments Provide Additional Insights and Warnings About Methodology. At the end of many sections, "Notes & Comments" offer added information about the methodology being discussed and its application. Notes & Comments may include warnings or highlight limitations of the methodology, offer recommendations for applications, or provide brief technical considerations.
• Self-Test Exercises Let Students Instantly Check Comprehension Before Progressing. Helpful Self-Test Exercises enable students to immediately evaluate their understanding of chapter concepts before advancing to the next topic. Completely worked-out Self-Test solutions appear in an appendix in addition to the solutions for even-numbered problems, as requested by past users.
• Engaging Q.M. in Action Articles Summarize Applications from Real-World Practice. Interesting Q.M. in Action articles throughout the text offer practical summaries of how quantitative methods apply in business today. The articles feature adaptations from INTERFACES and OR/MS TODAY as well as contributions from leading practitioners.
• Helpful Margin Annotations Clarify Key Points For Students. Brief, informative annotations in the margins of the book highlight key information and offer additional insights for readers who wish to know more. Providing appropriate emphasis, these clear annotations enhance students' understanding of key terms and concepts.
• ompletely Revised and Updated Simulation Chapter. While the authors maintain Chapter 16's intuitive introduction by continuing the use of best-, worst-, and base-case scenarios, they also added a more elaborate treatment of uncertainty by using Microsoft Excel to develop spreadsheet simulation models. Chapter 16 thoroughly explains how to construct a spreadsheet simulation model using only native Excel functionality, while the chapter appendix covers how the use of an Excel add-in−Analytic Solver Platform−facilitates more sophisticated simulation analyses. This new appendix replaces the previous edition's coverage of Crystal Ball, which is no longer paired with the textbook.
• New Cases: End-of-chapter student cases offer more in-depth and open-ended exercises than homework problems, giving students plenty of experience applying what they learn to real-world practice. This edition includes new cases on linear programming applications in Chapter 9, distribution and network models in Chapter 10, and integer programming in Chapter 11. Solutions to all cases are provided to instructors.
• Data Tables and Goal Seek in Appendix A. These two Excel features were added to Appendix A, Building Spreadsheet Models, as they are particularly useful in the construction of spreadsheet simulation models in the completely revised Chapter 16.
• Adjustment of Forecasting Notation in Chapter 6. The notation in Chapter 6, Time Series Analysis and Forecasting, was adjusted to be more in line with "regression-style" standard notation for forecasting.
• New And Updated Homework Problems: The 13th Edition added more than 35 new homework problems as well as updated numerous others to ensure the timeliest references available.
• New Section on Variability in Project Management. The 13th Edition's new section on variability provides a more holistic description of how variable activity times affect the probability of a project meeting a deadline, while maintaining simplicity by showing when using the critical path for these calculations is reasonable. In contract, traditional coverage has focused solely on the critical path to estimate the probability of a project meeting a deadline (on average, the longest sequence of activities). However, this calculation is based on the implicit assumption that no other "non-critical" activity will become a bottleneck. In the presence of highly variable activities, the assumption may not be accurate, yet traditional coverage provides no insight on this.
• Updated Q.M. in Action. The 13th Edition includes 15 all-new Q.M. in Action vignettes to provide the most recent examples available.

Preface.

1. Introduction.

2. Introduction to Probability.

3. Probability Distributions.

4. Decision Analysis.

5. Utility and Game Theory.

6. Time Series Analysis and 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.

Appendix A: Building Spreadsheet Models.

Appendix B: Binomial Probabilities.

Appendix C: Poisson Probabilities.

Appendix D: Areas for the Standard Normal Distribution.

Appendix E: Values for e-λ.

Appendix F: References and Bibliography.

Appendix G: Self-Test Solutions and Answers to Even-Numbered Problems.

David R. Anderson

David R. Anderson is a leading author and professor emeritus of quantitative analysis in the College of Business Administration at the University of Cincinnati. Dr. 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 introductory statistics for business students, Dr. Anderson taught graduate-level courses in regression analysis, multivariate analysis and management science. He also 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 well-respected textbooks related to decision sciences, and he actively consults with businesses in the areas of sampling and statistical methods. Born in Grand Forks, North Dakota, Dr. Anderson earned his B.S., M.S. and Ph.D. degrees from Purdue University.

Dennis J. Sweeney

Dennis J. Sweeney is professor emeritus of quantitative analysis and founder of the Center for Productivity Improvement at the University of Cincinnati. Born in Des Moines, Iowa, 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. Dr. Sweeney has worked in the management science group at Procter & Gamble and has been a visiting professor at Duke University. He also served as head of the Department of Quantitative Analysis and served four years as associate dean of the College of Business Administration at the University of Cincinnati. Dr. 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 journals such as Management Science, Operations Research, Mathematical Programming and Decision Sciences. Dr. Sweeney has co-authored ten textbooks in the areas of statistics, management science, linear programming and production and operations management.

Jeffrey D. Camm

Jeffrey D. Camm is the Inmar Presidential Chair and senior associate dean of business analytics programs 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 served 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 more than 45 papers in the general area of optimization applied to problems in operations management and marketing. He has published his research in many professional journals, including Science, Management Science, Operations Research and the INFORMS Journal on Applied Analytics. 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, Dr. Camm has served as an operations research consultant to numerous companies and government agencies. From 2005 to 2010 he served as editor-in-chief of the INFORMS Journal on Applied Analytics (formerly Interfaces). In 2016 Dr. Camm received the George E. Kimball Medal for service to the operations research profession, and in 2017 he was named an INFORMS fellow.

James J. Cochran

James J. Cochran is associate dean for research, a 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 his Ph.D. from the University of Cincinnati. He has been at The University of Alabama since 2014 and has served as 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 50 papers in the development and application of operations research and statistical methods. He has published in numerous journals, including Management Science, The American Statistician, Communications in Statistics-Theory and Methods, Annals of Operations Research, European Journal of Operational Research, Journal of Combinatorial Optimization, INFORMS Journal on Applied Analytics, BMJ Global Health and Statistics and Probability Letters. Dr. Cochran received the 2008 INFORMS prize for the Teaching of Operations Research Practice, the 2010 Mu Sigma Rho Statistical Education Award and the 2016 Waller Distinguished Teaching Career Award from the American Statistical Association. Dr. Cochran was elected to the International Statistics Institute in 2005 and was named a fellow of the American Statistical Association in 2011 and a fellow of INFORMS in 2017. He also received the Founders Award in 2014 and the Karl E. Peace Award in 2015 from the American Statistical Association. In addition, he received the INFORMS President's Award in 2019. 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 also served as an operations research or statistics consultant to numerous companies and not-for-profit organizations.

Michael J. Fry

Michael J. Fry is a professor of operations, business analytics and information systems as well as 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, Dr. Fry earned his 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 was previously department head. He has also been named a Lindner Research Fellow. Dr. Fry 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.He has published more than 25 research papers in journals such as Operations Research, M&SOM, Transportation Science, Naval Research Logistics, IIE Transactions, Critical Care Medicine 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., Starbucks Coffee Company, Great American Insurance Group, the Cincinnati Fire Department, the State of Ohio Election Commission, the Cincinnati Bengals and the Cincinnati Zoo & Botanical Garden. 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

Jeffrey W. Ohlmann is associate professor of business analytics and a 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. Dr. Ohlmann has been at the University of Iowa since 2003. His research on the modeling and solution of decision-making problems has produced more than two dozen research papers published in journals such as Operations Research, Mathematics of Operations Research, INFORMS Journal on Computing, Transportation Science and the European Journal of Operational Research. He has collaborated with organizations such as Transfreight, LeanCor, Cargill, the Hamilton County Board of Elections and three National Football League franchises. Because of the relevance of his work to industry, Dr. Ohlmann received the George B. Dantzig Dissertation Award, and he was recognized as a finalist for the Daniel H. Wagner Prize for Excellence in Operations Research Practice.