Introductory Management Science
4th Edition
Published on 1. December 1992
Book
Hardback
810 pages
978-0-13-486440-2 (ISBN)
Description
This highly-esteemed text introduces readers to the key ideas of management science that will be important to them throughout their careers. Addressing the needs of readers interested in both management science and business administration careers, the book emphasizes the role of management science techniques in the larger context of business decision-making. There is a strong focus on models-how they are created, how they are used, what kinds of insights they provide-and on the critical importance of managerial judgement in utilizing those insights. At the same time, for readers interested in more technical aspects of the subject, there is an unparalleled treatment of linear programming techniques. Computer applications, including the use of spreadsheets and of many popular special-purpose software packages, are integrated throughout the text in great abundance.
More details
Edition
4th edition
Language
English
Place of publication
United States
Publishing group
Pearson Education (US)
Target group
Professional and scholarly
Dimensions
Height: 283 mm
Width: 217 mm
Thickness: 46 mm
Weight
2136 gr
ISBN-13
978-0-13-486440-2 (9780134864402)
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Schweitzer Classification
Other editions
New editions
Jeffrey H. Moore | Larry R. Weatherford
Decision Modeling with Microsoft (R) Excel
United States Edition
Book
01/2001
6th Edition
Pearson
€122.86
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Content
(NOTE: At the End of Each Chapter is a Video Case.) 1. Introduction: Models and Modeling. I: DETERMINISTIC MODELS. 2. Linear Programming: Formal and Spreadsheet Models. 3. Linear Programming: Geometric Representations and Graphical Solutions. 4. Analysis of LP Models: The Graphical Approach. 5. Linear Programs: Computer Analysis, Interpreting Sensitivity Output, and the Dual Problem. 6. Linear Programming: The Simplex Method. 7. Linear Programming: Special Applications. 8. Integer and Quadratic Programming. 9. Network Models. 10. Inventory Control with Known Demand. 11. Heuristics, Multiple Objectives, and Goal Programming. 12. Calculus-Based Optimization and an Introduction to Nonlinear Programming. II: PROBABILISTIC MODELS. 13. Simulation. 14. Decision Theory and Decision Trees. 15. Project Management: PERT and CPM. 16. Inventory Models with Probabilistic Demand. 17. Queuing Models. 18. Forecasting. Answers to Odd-Numbered Problems. Appendix A: Basic Concepts in Probability. Using LINDO. Index.