Synopses & Reviews
In the Second Edition of their popular text, Wayne Winston and Chris Albright continue to build on their highly successful approach of teaching by example while using spreadsheets to model a wide variety of business problems. The authors show the relevance of topics through numerous examples of real-world implementation of management science. The ideal solution for people who want to teach by example and who want to solve real problems with spreadsheets and professional spreadsheet add-ins, this text is always interesting, in part due to the useful cases added to this edition.
Description
System requirements for accompanying computer disks: IBM PC or compatible; Microsoft Excel. Includes bibliographical references and index.
About the Author
Wayne L. Winston is Professor of Operations and Decision Technologies in the Kelley School of Business at Indiana University, where he has taught since 1975. Wayne received his B.S. degree in Mathematics from MIT and his Ph.D. degree in Operations Research from Yale. He has written the successful textbooks OPERATIONS RESEARCH: APPLICATIONS AND ALGORITHMS, MATHEMATICAL PROGRAMMING: APPLICATIONS AND ALGORITHMS, SIMULATION MODELING WITH @RISK, DATA ANALYSIS AND DECISION MAKING, DATA ANALYSIS FOR MANAGERS, SPREADSHEET MODELING AND APPLICATIONS, AND FINANCIAL MODELS USING SIMULATION AND OPTIMIZATION. Wayne has published over 20 articles in leading journals and has won many teaching awards, including the school-wide MBA award four times. His current interest is in showing how spreadsheet models can be used to solve business problems in all disciplines, particularly in finance and marketing.S. Christian Albright received his B.S. degree in mathematics from Stanford in 1968 and his Ph.D. in operations research from Stanford in 1972. Since then, he has been teaching in the Operations and Decision Technologies Department in the Kelley School of Business at Indiana University. He has taught courses in management science, computer simulation, and statistics to all levels of business students: undergraduates, MBAs, and doctoral students. He has published over twenty articles in leading operations research journals in the area of applied probability, and he has authored other successful South-Western titles, including DATA ANALYSIS AND DECISION MAKING, DATA ANALYSIS FOR MANAGERS, PRACTICAL MANAGEMENT SCIENCE, and SPREADSHEET MODELING AND APPLICATIONS. His current interest is in spreadsheet modeling, including development of VBA applications in Excel?.
Table of Contents
1. Introduction To Modeling. Introduction. A Waiting-Line Example. Modeling Versus Models. The Seven-Step Modeling Process. Successful Management Science Applications. Why Study Management Science?. Software Included In This Book. Conclusion. 2. Introductory Spreadsheet Modeling. Introduction. Basic Spreadsheet Modeling Concepts. Modeling Examples. Conclusions. 3. Introduction To Optimization Modeling. Introduction. A Brief History Of Linear Programming. Introduction To Lp Modeling. Sensitivity Analysis And The Solvertable Add-In. The Linear Assumptions. Graphical Solution Method. Infeasibility And Unboundedness. A Multiperiod Production Problem. A Decision Support System. Conclusion.. Appendix: Information On Solvers. 4. Linear Programming Models. Introduction. Static Workforce Scheduling Models. Aggregate Planning Models. Dynamic Workforce Planning Models. Blending Models. Production Process Models. Dynamic Financial Models. Data Envelopment Analysis (Dea). Conclusion. 5. Network Models. Introduction. Transportation Models. More General Logistics Models. Non-Logistics Network Models. Project Scheduling Models. Conclusion. 6. Linear Optimization Models With Integer Variables. Introduction. Approaches To Optimization With Integer Variables. Capital Budgeting Models. Fixed-Cost Models. Lockbox Models. Plant And Warehouse Location Models. Set Covering Models. Models With Either-Or Constraints. Cutting Stock Models. Conclusion. 7. Nonlinear Optimization Models. Introduction. Basic Ideas Of Nonlinear Optimization. Pricing Models. Salesforce Allocation Models. Facility Location Models. Rating Sports Teams. Estimating The Beta Of A Stock. Portfolio Optimization. Conclusion. 8. Evolutionary Solver: An Alternative Optimization Procedure. Introduction. Introduction To Genetic Algorithms. Introduction To The Evolutionary Solver. Nonlinear Pricing Models. Combinatorial Models. Fitting An S-Shaped Curve. Portfolio Optimization. Cluster Analysis. Discriminant Analysis. Conclusion. 9. Multi-Objective Decision Making. Introduction. Goal Programming. Pareto Optimality And Trace-Off Curves. The Analytic Hierarchy Process. Conclusion. 10. Decision Making Under Uncertainty. Introduction. Elements Of A Decision Analysis. More Single-Stage Examples. Mulitstage Decision Problems. Bayes Rule. Incorporating Attitudes Toward Risk. Conclusion. 11. Introduction To Simulation Modeling. Introduction. Real Applications Of Simulation. Generating Uniformly Distributed Random Numbers. Simulation With Built-In Excel Tools. Generating Random Numbers From Other Probability Distributions. Introduction To @Risk. Correlation In @Risk. Conclusion. 12. Simulation Models. Introduction. Operations Models. Financial Models. Marketing Models. Simulating Games Of Chance. Using Toprank With @Risk For Powerful Modeling. Conclusion. 13. Inventory Models. Introduction. Categories Of Inventory Models. Types Of Costs In Inventory Models. Economic Order Quantity (Eoq) Models. Probabilistic Inventory Models. Ordering Simulation Models. Supply Chain Models. Conclusion. 14. Queueing Models. Introduction. Elements Of Queuing Models. The Exponential Distribution. Important Queuing Relationships. Analytical Models. Queuing Simulation Models. Conclusion. 15. Regression Analysis. Introduction. Scatterplots: Graphing Relationships. Correlations: Indicators Of Linear Relationships. Simple Linear Regression. Multiple Regression. The Statistical Model. Inferences About The Regression Coefficients. Multicollinearity. Modeling Possibilities. Prediction. Conclusion. 16. Time Series Analysis And Forecasting. Introduction. General Concepts. Random Series. The Random Walk Model. Autoregression Models. Regression-Based Trend Models. Moving Averages. Exponential Smoothing. Deseasonalizing: The Ratio-To-Moving-Averages Method. Estimating Seasonality With Regression. Conclusion. References. Index.