An Introduction To Management Science With Cd

Book Description

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 leading text on the market. Simply put, it's 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 as well.

About the Author

David R. Anderson is Professor of Quantitative Analysis in the College of Business Administration at the University of Cincinnati. Born in Grand Forks, N.D., 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 the Associate Dean of the College of Business Administration. In addition, he was the 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 has also taught statistical courses at the Department of Labor in Washington, D.C. Professor Anderson has been honored with nominations and awards for excellence in teaching and excellence in service to student organizations. He has co-authored 10 textbooks related to decision sciences and actively consults with businesses in the areas of sampling and statistical methods. 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 B.S. and B.A. degrees 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. Sweeney has worked in the management science group at Procter & 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 & 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 11 textbooks in the areas of statistics, management science, linear programming, and production and operations management. Thomas A. Williams is Professor of Management Science in the College of Business at Rochester Institute of Technology. Born in Elmira, New York, he earned his B.S. degree at Clarkson University. He did 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 first undergraduate program in Information Systems. At RIT he was the first chairman of the Decision Sciences Department. Professor Williams is the co-author of 12 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.