Spreadsheet Modeling and Applications: Essentials of Practical Management Science (with CD-ROM and InfoTrac) / Edition 1by S. Christian Albright, Wayne Winston, Wayne L. Winston
Pub. Date: 04/21/2004
Publisher: Cengage Learning
Chris Albright and Wayne Winston have brought their hallmark teach-by-example approach to the undergraduate spreadsheet modeling course. Renowned for their other successful texts in operations research/management science, Winston and Albright successfully show how spreadsheets are used in real life to model and analyze real business problems. By modeling problems
Chris Albright and Wayne Winston have brought their hallmark teach-by-example approach to the undergraduate spreadsheet modeling course. Renowned for their other successful texts in operations research/management science, Winston and Albright successfully show how spreadsheets are used in real life to model and analyze real business problems. By modeling problems using spreadsheets from the outset, SPREADSHEET MODELING AND APPLICATIONS prepares future managers for the types of problems they will encounter on the job. Real cases throughout the text further cement this book's status as the most relevant of its kind on the market. This text is also accompanied by Palisade Corporation's professional spreadsheet add-ins, DecisionTools Suite.
- Cengage Learning
- Publication date:
- Edition description:
- Book & CD-ROM, with Software Package
- Product dimensions:
- 7.92(w) x 10.18(h) x 1.88(d)
Table of Contents
Part I: INTRODUCTION TO SPREADSHEET MODELING. 1. Introduction to Modeling. Introduction. A Waiting Line Example. Modeling Versus Models. The Seven-Step Modeling Process. A Successful Management Science Application. Why Study Management Science? Software Included in This Book. Conclusion. 2. Introductory Spreadsheet Modeling. Introduction. Basic Spreadsheet Modeling: Concepts and Best Practices. Cost Projections. Breakeven Analysis. Ordering with Quantity Discounts and Demand Uncertainty. Decisions Involving the Time Value of Money. Conclusion. Appendix: Tips for Editing and Documenting Spreadsheets. Part II: DECISION MAKING UNDER CERTAINTY. 3. Introduction to Optimization Modeling. Introduction. Introduction to Optimization. A Two-Variable Model. Sensitivity Analysis. Properties of Linear Models. Infeasibility and Unboundedness. A Product Mix Model. A Multiperiod Production Model. A Comparison of Algebraic and Spreadsheet Models. A Decision Support System. Conclusion. Appendix: Information on Solvers. 4. Linear Programming Models. Introduction. Advertising Models. Static Workforce Scheduling Models. Aggregate Planning Models. Blending Models. Production Process Models. Financial Models. Conclusion. 5. Network Models. Introduction. Transportation Models. Assignment Models. Minimum Cost Network Flow Models. Shortest Path Models. Project Scheduling Models. Conclusion. 6. Linear Optimization Models with Integer Variables. Introduction. Overview of Optimization with Integer Variables. Capital Budgeting Models. Fixed-Cost Models. Set Covering Models and Location/Assignment Models. Conclusion. 7. Nonlinear Optimization Models. Introduction. Basic Ideas of Nonlinear Optimization. Pricing Models. Advertising Response and Selection Models. Facility Location Models. Models for Rating Sports Teams. Portfolio Optimization Models. Conclusion. Part III: DECISION MAKING UNDER UNCERTAINTY. 8. Decision Making Under Uncertainty. Introduction. Elements of a Decision Analysis. The PrecisionTree Add-In. Bayes Rule. Multistage Decision Problems. Incorporating Attitudes Toward Risk. Conclusion. 9. Introduction to Simulation Modeling. Introduction. Real Applications of Simulation. Probability Distributions for Input Variables. Simulation with Built-In Excel Tools. Introduction to @RISK. The Effect of Input Distributions on Results. Conclusion. 10. Simulation Models. Introduction. Operations Models. Financial Models. Marketing Models. Simulating Games of Chance. Conclusion. 11. Queuing Models. Introduction. Elements of Queuing Models. The Exponential Distribution. Important Queueing. Relationships. Analytical Queuing Models. Queuing Simulation Models. Conclusion. 12. Regression and Forecasting Models. Introduction. Overview of Regression Models. Simple Regression Models. Multiple Regression Models. Overview of Time Series Models. Moving Averages Models. Exponential Smoothing Models. Conclusion.
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