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An Introduction to Management Science: Quantitative Approaches to Decision Making (13/e)
일시품절 (02-6416-8007 문의)
An Introduction to Management Science: Quantitative Approaches to Decision Making (13/e)
저자
Anderson, Sweene, Williams, Martin
역자
-
분야
해외원서 ▷ Sciences
출판사
박영사
발행일
2011.01.01
개정 출간예정일
페이지
846P
판형
203*255
ISBN
9780538475655
부가기호
강의자료다운
-
정가
39,000원
Introduce your students to management science techniques with the thorough, applications-oriented coverage you can trust from the definitive leader in traditional management science texts. The best-selling Anderson/Sweeney/Williams/Martin's INTRODUCTION TO MANAGEMENT SCIENCE: A QUANTITATIVE APPROACH TO DECISION MAKING, 13E, International Edition has helped define the topical coverage presented within today's management science course curriculum. This book provides a thorough grounding in management science techniques with a readable presentation style and a wealth of examples drawn from a variety of businesses throughout the world.
Students learn the techniques and refine their problem solving skills with realistic problems that continue to set this established leader apart. Every new edition now includes the highly respected LINGO 10 software that is integrated with text problems to help you develop the skills to use this, Microsoft® Excel, and many other valuable software packages to resolve management science problems. In response to feedback from instructors like you, this edition now places greater emphasis on the applications of management science and use of computer software with much of the focus on algorithms moved to optional chapters on the accompanying Student CD for your flexibility.
As always, the well-respected authors have continued their reputation for excellent and accuracy with error-free presentations throughout the text, test bank, and supplements.
Trust INTRODUCTION TO MANAGEMENT SCIENCE, 12E, International Edition to deliver the sound, practical and student-oriented approach that enables students to achieve success in your course and the world of business beyond.

David R. Anderson
Dr. David R. Anderson is Professor of Quantitative Analysis in the College of Business Administration at the University of Cincinnati. Born in Grand Forks, North Dakota, 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. He was also 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 also has taught statistical courses at the Department of Labor in Washington, D.C. Dr. Anderson has been honored with nominations and awards for excellence in teaching and excellence in service to student organizations. He has coauthored ten textbooks related to decision sciences and actively consults with businesses in the areas of sampling and statistical methods.

Dennis J. Sweeney
Dr. 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 BS and BA degrees from Drake University, graduating summa cum laude. He received his MBA and DBA 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. Dr. Sweeney has coauthored ten textbooks in the areas of statistics, management science, linear programming, and production and operations management.

Thomas A. Williams
Dr. Thomas A. Williams is Professor of Management Science in the College of Business at Rochester Institute of Technology (RIT). Born in Elmira, New York, he earned his BS degree at Clarkson University. He completed his graduate work at Rensselaer Polytechnic Institute, where he received his MS and PhD degrees. 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 first undergraduate program in Information Systems. At RIT he was the first chair of the Decision Sciences Department. Dr. Williams is the coauthor 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 in areas ranging from the use of elementary data analysis to the development of large-scale regression models.

R. Kipp Martin
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. 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 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. Forecasting.
16. Markov Processes.
On the Student CD:
17. Linear Programming: Simplex Method.
18. Simplex-Based Sensitivity Analysis and Duality.
19. Solution Procedures for Transportation and Assignment Problems.
20. Minimal Spanning Tree Algorithm.
21. Dynamic Programming.