Introduction to management science
著者
書誌事項
Introduction to management science
Pearson, c2016
12th ed.
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
For undergraduate courses in Management Science.
A logical, step-by-step approach to complex problem-solving
Using simple, straightforward examples to present complex mathematical concepts, Introduction to Management Science gives students a strong foundation in how to logically approach decision-making problems. Sample problems are used liberally throughout the text to facilitate the learning process and demonstrate different quantitative techniques. Management Science presents modeling techniques that are used extensively in the business world and provides a useful framework for problem-solving that students can apply in the workplace.
The Twelfth Edition focuses on the latest technological advances used by businesses and organizations for solving problems and leverages the latest versions of Excel 2013, Excel QM, TreePlan, Crystal Ball, Microsoft Project 2010, and QM for Windows.
目次
Preface
Management Science
Linear Programming: Model Formulation and Graphical Solution
Linear Programming: Computer Solution and Sensitivity Analysis
Linear Programming: Modeling Examples
Integer Programming
Transportation, Transshipment, and Assignment Problems
Network Flow Models
Project Management
Multicriteria Decision Making
Nonlinear Programming
Probability and Statistics
Decision Analysis
Queuing Analysis
Simulation
Forecasting
Inventory Management
Appendix A: Normal and Chi-Square Tables
Appendix B: Setting Up and Editing a Spreadsheet Site Modules
Appendix C: The Poisson and Exponential Distributions
Solutions to Selected Odd-Numbered Problems
Glossary
Index
The following items can be found on the Companion Website that accompanies this text:
Website Modules
Module A: The Simplex Solution Method
Module B: Transportation and Assignment Solution Methods
Module C: Integer Programming: The Branch and Bound Method
Module D: Nonlinear Programming Solution Techniques
Module E: Game Theory
Module F: Markov Analysis
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