An Introduction to Management Science

David R. Anderson is Professor Emeritus of Quantitative Analysis in the College of Business Administration at the University of Cincinnati. He earned his BS, MS and PhD 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 at the University of Cincinnati. 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 honoured with nominations and awards for excellence in teaching and excellence in service to student organizations. Professor Anderson has co-authored 10 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. Dennis J. Sweeney is Professor of Quantitative Analysis and founder of the Center for Productivity Improvement 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 ten textbooks in the areas of statistics, management science, linear programming and production and operations management. Thomas A. Williams is Professor Emeritus of Management Science in the College of Business at Rochester Institute of Technology. Born in Elmira, New York, he earned his BS degree at Clarkson University. He did his graduate work at Rensselaer Polytechnic Institute, where he received his MS and PhD 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 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. Mik Wisniewski is Senior Research Fellow in the Management Science department at Strathclyde Business School, Glasgow, Scotland. He is author of over a dozen texts on management science, quantitative business analysis and optimisation, including the established Quantitative Methods for Decision Makers (Pearson), and has many published articles in these areas. Mik also runs his own consulting company and clients including Shell, KPMG, PriceWaterhouseCoopers, numerous government agencies both in the UK and overseas, and a range of public sector organisations. He as worked extensively across Europe, the Middle East, Africa and Asia. Xavier Pierron is a Senior Lecturer in Business Operations and Coventry University, UK. He has wide experience in both industry and academia and has taken part in European projects, consultancy projects and training of professionals. His research interests include merging operations management techniques with business sustainability issues.

• 1.Introduction2.An Introduction to Linear Programming3.Linear Programming: Sensitivity Analysis and Interpretation of Solution4.Linear Programming Applications5.Linear Programming: The Simplex Method6.Simplex-Based Sensitivity Analysis and Duality7.Transportation, Assignment and Transshipment Problems8.Network Models9.Project Scheduling: PERT/CPM10.Inventory Models11.Queuing Models12.Simulation13.Decision Analysis14.Multicriteria DecisionsConclusion: Management Science in PracticeAppendix A Areas for the Standard Normal DistributionAppendix B Values of e_lAppendix C Bibliography and ReferencesAppendix D Self-Test SolutionsOnline chapters15.Integer Linear Programming16.Forecasting17.Dynamic Programming18.Markov Processes