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ISBN 10: 1305734845
ISBN 13: 9781305734845
Author: Wayne Winston, Christian Albright
Geared entirely to Excel 2013, PRACTICAL MANAGEMENT SCIENCE, 5e helps students understand and take full advantage of the power of spreadsheet modeling. It integrates modeling into all functional areas of business–finance, marketing, operations management–using real examples and real data. Emphasizing applied, relevant learning, the text presents just the right amount of theory to ensure students understand the foundation of the topic, followed by exercises that give them practical, hands-on experience with the methodologies. It focuses on modeling over algebraic formulations and memorization of particular models. The Fifth Edition includes the latest changes in the accompanying @RISK and PrecisionTree add-ins, incorporates “”BigPicture”” diagrams of spreadsheet models into the optimization chapters, and provides new and updated cases throughout. The online Chapter 16: Multiobjective Decision Making is now more conceptual, while Chapter 9: Decision Making Under Uncertainty extends a single “”new product decisions”” example throughout the chapter. In addition almost 30 new tutorial videos explain concepts and work through examples.
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Practical Management Science 5th Table of contents:
Ch 1: Introduction to Modeling
1.1 Introduction
1.2 A Capital Budgeting Example
1.3 Modeling versus Models
1.4 A Seven-Step Modeling Process
1.5 A Great Source for Management Science Applications: Interfaces
1.6 Why Study Management Science?
1.7 Software Included with This Book
1.8 Conclusion
Ch 2: Introduction to Spreadsheet Modeling
2.1 Introduction
2.2 Basic Spreadsheet Modeling: Concepts and Best Practices
2.3 Cost Projections
2.4 Breakeven Analysis
2.5 Ordering with Quantity Discounts and Demand Uncertainty
2.6 Estimating the Relationship between Price and Demand
2.7 Decisions Involving the Time Value of Money
2.8 Conclusion
Appendix Tips for Editing and Documenting Spreadsheets
Case 2.1: Project Selection at Ewing Natural Gas
Case 2.2: New Product Introduction at eTech
Ch 3: Introduction to Optimization Modeling
3.1 Introduction
3.2 Introduction to Optimization
3.3 A Two-Variable Product Mix Model
3.4 Sensitivity Analysis
3.5 Properties of Linear Models
3.6 Infeasibility and Unboundedness
3.7 A Larger Product Mix Model
3.8 A Multiperiod Production Model
3.9 A Comparison of Algebraic and Spreadsheet Models
3.10 A Decision Support System
3.11 Conclusion
Appendix Information on Solvers
Case 3.1: Shelby Shelving
Case 3.2: Sonoma Valley Wines
Ch 4: Linear Programming Models
4.1 Introduction
4.2 Advertising Models
4.3 Employee Scheduling Models
4.4 Aggregate Planning Models
4.5 Blending Models
4.6 Production Process Models
4.7 Financial Models
4.8 Data Envelopment Analysis (DEA)
4.9 Conclusion
Case 4.1: Blending Aviation Gasoline at Jansen Gas
Case 4.2: Delinquent Accounts at GE Capital
Case 4.3: Foreign Currency Trading
Ch 5: Network Models
5.1 Introduction
5.2 Transportation Models
5.3 Assignment Models
5.4 Other Logistics Models
5.5 Shortest Path Models
5.6 Network Models in the Airline Industry
5.7 Conclusion
Case 5.1: International Textile Company, Ltd.
Case 5.2: Optimized Motor Carrier Selection at Westvaco
Ch 6: Optimization Models with Integer Variables
6.1 Introduction
6.2 Overview of Optimization with Integer Variables
6.3 Capital Budgeting Models
6.4 Fixed-Cost Models
6.5 Set-Covering and Location-Assignment Models
6.6 Cutting Stock Models
6.7 Conclusion
Case 6.1: Giant Motor Company
Case 6.2: Selecting Telecommunication Carriers to Obtain Volume Discounts
Case 6.3: Project Selection at Ewing Natural Gas
Ch 7: Nonlinear Optimization Models
7.1 Introduction
7.2 Basic Ideas of Nonlinear Optimization
7.3 Pricing Models
7.4 Advertising Response and Selection Models
7.5 Facility Location Models
7.6 Models for Rating Sports Teams
7.7 Portfolio Optimization Models
7.8 Estimating the Beta of a Stock
7.9 Conclusion
Case 7.1: GMS Stock Hedging
Ch 8: Evolutionary Solver: An Alternative Optimization Procedure
8.1 Introduction
8.2 Introduction to Genetic Algorithms
8.3 Introduction to Evolutionary Solver
8.4 Nonlinear Pricing Models
8.5 Combinatorial Models
8.6 Fitting an S-Shaped Curve
8.7 Portfolio Optimization
8.8 Cluster Analysis
8.9 Discriminant Analysis
8.10 The Traveling Salesperson Problem
8.11 Conclusion
Case 8.1: Assigning MBA Students to Teams
Case 8.2: Project Selection at Ewing Natural Gas
Ch 9: Decision Making under Uncertainty
9.1 Introduction
9.2 Elements of Decision Analysis
9.3 One-Stage Decision Problems
9.4 The Precisiontree Add-In
9.5 Multistage Decision Problems
9.6 The Role of Risk Aversion
9.7 Conclusion
Case 9.1: Jogger Shoe Company
Case 9.2: Westhouser Paper Company
Case 9.3: Electronic Timing System for Olympics
Case 9.4: Developing a Helicopter Component for the Army
Ch 10: Introduction to Simulation Modeling
10.1 Introduction
10.2 Probability Distributions for Input Variables
10.3 Simulation and the Flaw of Averages
10.4 Simulation with Built-In Excel Tools
10.5 Introduction to @Risk
10.6 The Effects of Input Distributions on Results
10.7 Conclusion
Appendix Learning More about @Risk
Case 10.1: Ski Jacket Production
Case 10.2: Ebony Bath Soap
Case 10.3: Advertising Effectiveness
Case 10.4: New Product Introduction at eTech
Ch 11: Simulation Models
11.1 Introduction
11.2 Operations Models
11.3 Financial Models
11.4 Marketing Models
11.5 Simulating Games of Chance
11.6 Conclusion
Appendix Other Palisade Tools for Simulation
Case 11.1: College Fund Investment
Case 11.2: Bond Investment Strategy
Case 11.3: Project Selection at Ewing Natural Gas
Ch 12: Inventory and Supply Chain Models
12.1 Introduction
12.2 Categories of Inventory and Supply Chain Models
12.3 Types of Costs in Inventory and Supply Chain Models
12.4 Economic Order Quantity (EOQ) Models
12.5 Probabilistic Inventory Models
12.6 Ordering Simulation Models
12.7 Supply Chain Models
12.8 Conclusion
Case 12.1: Subway Token Hoarding
Ch 13: Queueing Models
13.1 Introduction
13.2 Elements of Queueing Models
13.3 The Exponential Distribution
13.4 Important Queueing Relationships
13.5 Analytic Steady-State Queueing Models
13.6 Queueing Simulation Models
13.7 Conclusion
Case 13.1: Catalog Company Phone Orders
Case 13.2: Pacific National Bank
Ch 14: Regression and Forecasting Models
14.1 Introduction
14.2 Overview of Regression Models
14.3 Simple Regression Models
14.4 Multiple Regression Models
14.5 Overview of Time Series Models
14.6 Moving Averages Models
14.7 Exponential Smoothing Models
14.8 Conclusion
Case 14.1: Demand for French Bread at Howie’s Bakery
Case 14.2: Forecasting Overhead at Wagner Printers
Case 14.3: Arrivals at the Credit Union
References
Index
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