Now you can gain with a sound conceptual understanding of the role that management science plays in the decision-making process while mastering the latest advantages of Microsoft® Office Excel® 2016. The trusted market leader for more than two decades, Anderson/Sweeney/Williams/Camm/Cochran/Fry/Ohlmann's AN INTRODUCTION TO MANAGEMENT SCIENCE: QUANTITATIVE APPROACHES TO DECISION MAKING, 15E uses a proven problem-scenario approach to introduce each quantitative technique within an applications setting. All data sets, applications, and screen visuals reflect the details of Excel 2016 to effectively prepare you to work with the latest spreadsheet tools. In addition, the book's online content offers LINGO software and Excel add-ins.
- Uses a problem-scenario approach to teach quantitative techniques through real business applications.
- Offers robust online content, including five extra chapters, Excel templates, and trial software like LINGO and Analytic Solver.
- Features real data examples to demonstrate practical business challenges and solutions.
- Authored by a trusted team of experts, ensuring accuracy and clarity in all content and assessments.
- Includes self-test exercises with solutions to reinforce understanding and support exam preparation.
- Integrates software tools like Excel and LINGO to build essential analytical skills.
- Fully updated for Microsoft Excel 2016, with step-by-step Solver and LINGO instructions.
- Chapter 12 on simulation is expanded, covering uncertainty modeling using native Excel and add-ins.
- Appendices reflect the latest Excel updates, including Solver changes and the Forecast Tool.
- A new appendix aligns spreadsheet modeling techniques with Excel 2016 enhancements.
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 Linear 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. Time Series Analysis and Forecasting.
16. Markov Processes.
17. Linear Programming: Simplex Method (online).
18. Simplex-Based Sensitivity Analysis and Duality (online).
19. Solutions Procedures for Transportation and Assignment Problems (online).
20. Minimal Spanning Tree (online).
21. Dynamic Programming (online).
Appendix A: Building Spreadsheet Models.
Appendix B: Areas for the Standard Normal Distribution.
Appendix C: Values of e–λ.
Appendix D: References and Bibliography.
Appendix E: Self-Test Solutions and Answers to Even-Numbered Problems (online only).
David R. Anderson
David R. Anderson is a leading author and professor emeritus of quantitative analysis in the College of Business Administration at the University of Cincinnati.
Dennis J. Sweeney
Dennis J. Sweeney is professor emeritus of quantitative analysis and founder of the Center for Productivity Improvement at the University of Cincinnati.
Jeffrey D. Camm
Jeffrey D. Camm is the Inmar Presidential Chair and senior associate dean of business analytics programs in the School of Business at Wake Forest University.
James J. Cochran
James J. Cochran is associate dean for research, a professor of applied statistics and the Rogers-Spivey Faculty Fellow at The University of Alabama.
Michael J. Fry
Michael J. Fry is a professor of operations, business analytics and information systems as well as academic director of the Center for Business Analytics in the Carl H. Lindner College of Business at the University of Cincinnati.
Jeffrey W. Ohlmann
Jeffrey W. Ohlmann is associate professor of business analytics and a Huneke Research Fellow in the Tippie College of Business at the University of Iowa. Born in Valentine, Nebraska, he earned a B.S. from the University of Nebraska and M.S. and Ph.D. degrees from the University of Michigan.
