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Test Bank for Practical Management Science 6th by Winston

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This is completed downloadable of Test Bank for Practical Management Science 6th by Winston

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ISBN-10 ‏ : ‎ 1337406651
ISBN-13 ‏ : ‎ 978-1337406659
Author: Wayne L. Winston

Learn to take full advantage of the power of spreadsheet modeling with PRACTICAL MANAGEMENT SCIENCE, 6E, geared entirely to Excel 2016. This edition uses an active-learning approach and realistic problems with the right amount of theory to ensure you establish a strong foundation. Exercises offer practical, hands-on experience with the methodologies. Examples and problems from finance, marketing, and operations management, and other areas of business illustrate how management science applies to your chosen profession — and how you can use these skills on the job. The authors emphasize modeling rather than algebraic formulations and memorization of particular models. This edition also includes access to Palisade DecisionTools Suite (BigPicture, @RISK, PrecisionTree, StatTools, TopRank, NeuralTools, and Evolver) as well as SolverTable, for sensitivity analysis on optimization models. Chapters 15-17 are available online via MindTap.

Table of Content:

Chapter 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
Chapter 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
Chapter 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 Optimization Software
Case 3.1 Shelby Shelving
Chapter 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
Chapter 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 Optimized Motor Carrier Selection at Westvaco
Chapter 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
Chapter 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
Chapter 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 Optimal Permutation Models
8.9 Conclusion
Case 8.1 Assigning MBA Students to Teams
Case 8.2 Project Selection at Ewing Natural Gas
Chapter 9: Decision Making under Uncertainty
9.1 Introduction
9.2 Elements of Decision Analysis
9.3 Single-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
Chapter 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
Chapter 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 Ewing Natural Gas
Chapter 12: Queueing Models
12.1 Introduction
12.2 Elements of Queueing Models
12.3 The Exponential Distribution
12.4 Important Queueing Relationships
12.5 Analytic Steady-State Queueing Models
12.6 Queueing Simulation Models
12.7 Conclusion
Case 12.1 Catalog Company Phone Orders
Chapter 13: Regression and Forecasting Models
13.1 Introduction
13.2 Overview of Regression Models
13.3 Simple Regression Models
13.4 Multiple Regression Models
13.5 Overview of Time Series Models
13.6 Moving Averages Models
13.7 Exponential Smoothing Models
13.8 Conclusion
Case 13.1 Demand for French Bread at Howie’s Bakery
Case 13.2 Forecasting Overhead at Wagner Printers
Case 13.3 Arrivals at the Credit Union
Chapter 14: Data Mining
14.1 Introduction
14.2 Classification Methods
14.3 Clustering Methods
14.4 Conclusion
Case 14.1 Houston Area Survey
References
Index

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