Bernard Taylor

Introduction to Management Science, Global Edition

• Broschiertes Buch

Produktbeschreibung

For undergraduate courses in management science. A logical, step-by-step approach to complex problem-solving Introduction to Management Science gives students a strong foundation in how to make decisions and solve complex problems using both quantitative methods and software tools. In addition to extensive examples, problem sets, and cases, the 13th Edition incorporates Excel 2016 and other software resources, developing students' ability to leverage the technology they will use throughout their careers. By practicing these modeling techniques, students gain a useful framework for problem-solving that they can then apply in the workplace.

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Produktdetails

• Verlag: Pearson Education Limited
• 13 ed
• Seitenzahl: 864
• Erscheinungstermin: 5. März 2019
• Englisch
• Abmessung: 251mm x 201mm x 26mm
• Gewicht: 1436g
• ISBN-13: 9781292263045
• ISBN-10: 1292263040
• Artikelnr.: 56846512

Inhaltsangabe

 
1. Management Science 
2. Linear Programming: Model Formulation and Graphical Solution 
3. Linear Programming: Computer Solution and Sensitivity Analysis 
4. Linear Programming: Modeling Examples 
5. Integer Programming 
6. Transportation, Transshipment, and Assignment Problems 
7. Network Flow Models 
8. Project Management 
9. Multicriteria Decision Making 
10. Nonlinear Programming 
11. Probability and Statistics 
12. Decision Analysis 
13. Queuing Analysis 
14. Simulation 
15. Forecasting 
16. Inventory Management
 
Appendix A: Normal and Chi-Square Tables 
Appendix B: Setting Up and Editing a Spreadsheet 
Appendix C: The Poisson and Exponential Distributions 
 
Solutions to Selected Odd-Numbered Problems 
 
The following items can be found on the companion website that accompanies
this text:
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