Solution manual for Practical Management Science 6th Edition by Wayne Winston 9781337406659 1337406651

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• ISBN 10:1337406651

• ISBN 13:9781337406659

• Author:Wayne Winston

Practical Management Science
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.

Practical Management Science 6th Table of contents:

Chapter 1. Introduction to Modeling
1.1. Introduction
1.2. A Capital Budgeting Example
A Descriptive Model
An Optimization Model
Incorporating Uncertainty
1.3. Modeling Versus Models
1.4. A Seven-Step Modeling Process
Step 1: Problem Definition
Step 2: Data Collection
Step 3: Model Development
Step 4: Model Verification
Step 5: Optimization and Decision Making
Step 6: Model Communication to Management
Step 7: Model Implementation
Flowchart of Procedure and Discussion of Steps
The Model as a Beginning, Not an End
1.5. A Great Source for Management Science Applications: Interfaces
1.6. Why Study Management Science?
1.7. Software Included with this Book
Excel Tutorial
Solver Add-in
SolverTable Add-in
Palisade DecisionTools Suite
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
Problems
2.5. Ordering with Quantity Discounts and Demand Uncertainty
Problems
2.6. Estimating the Relationship between Price and Demand
Problems
2.7. Decisions Involving the Time Value of Money
Problems
2.8. Conclusion
Summary of Key Management Science Terms
Summary of Key Excel Terms
Problems
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
Solver’s Sensitivity Report
SolverTable Add-In
Comparison of Solver’s Sensitivity Report and SolverTable
3.5. Properties of Linear Models
Proportionality
Additivity
Divisibility
Discussion of Linear Properties
Linear Models and Scaling
3.6. Infeasibility and Unboundedness
Infeasibility
Unboundedness
Comparison of Infeasibility and Unboundedness
Problems
3.7. A Larger Product Mix Model
Problems
3.8. A Multiperiod Production Model
Problems
3.9. A Comparison of Algebraic and Spreadsheet Models
3.10. A Decision Support System
3.11. Conclusion
Summary of Key Terms
Summary of Key Excel Terms
Problems
Appendix Information on Optimization Software
Frontline Systems Solvers
OpenSolver Add-In
Palisade’s Evolver Add-In
Case 3.1. Shelby Shelving
Chapter 4. Linear Programming Models
4.1. Introduction
4.2. Advertising Models
Problems
4.3. Employee Scheduling Models
Problems
4.4. Aggregate Planning Models
Problems
4.5. Blending Models
Problems
4.6. Production Process Models
Problems
4.7. Financial Models
Problems
4.8. Data Envelopment Analysis (DEA)
Problems
4.9. Conclusion
Summary of Key Management Science Terms
Summary of Key Excel Terms
Problems
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
Problems
5.3. Assignment Models
Problems
5.4. Other Logistics Models
Problems
5.5. Shortest Path Models
Geographical Shortest Path Models
Equipment Replacement Models
Problems
5.6. Network Models in the Airline Industry
Problems
5.7. Conclusion
Summary of Key Management Science Terms
Summary of Key Excel Terms
Problems
Modeling Problems
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
Branch and Bound Algorithm
The Solver Integer Optimality Setting
Solver Messages
6.3. Capital Budgeting Models
Problems
6.4. Fixed-Cost Models
Problems
6.5. Set-Covering and Location-Assignment Models
Problems
6.6. Cutting Stock Models
Problems
6.7. Conclusion
Summary of Key Management Science Terms
Summary of Key Excel Terms
Problems
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
Convex and Concave Functions
Problems That Solver Always Solves Correctly
Conditions for Maximization Problems
Conditions for Minimization Problems
When the Assumptions Do Not Hold
Multistart Option
7.3. Pricing Models
Problems
7.4. Advertising Response and Selection Models
Problems
7.5. Facility Location Models
Problems
7.6. Models for Rating Sports Teams
Problems
7.7. Portfolio Optimization Models
Weighted Sums of Random Variables
Matrix Functions in Excel
The Portfolio Selection Model
Problems
7.8. Estimating the Beta of a Stock
Criterion 1: Sum of Squared Errors (Least Squares)
Criterion 2: Weighted Sum of Squared Errors
Criterion 3: Sum of Absolute Errors (SAE)
Criterion 4: Minimax
Problems
7.9. Conclusion
Summary of Key Management Science Terms
Summary of Key Excel Terms
Problems
Case 7.1. GMS Stock Hedging
Chapter 8. Evolutionary Solver: An Alternative Optimization Procedure
8.1. Introduction
8.2. Introduction to Genetic Algorithms
Strengths and Weaknesses of GAs
8.3. Introduction to Evolutionary Solver
Limits on Decision Variable Cells: Required?
Problems
8.4. Nonlinear Pricing Models
Other Forms of Nonlinear Pricing
Problems
8.5. Combinatorial Models
Loading Products on a Truck
Finding a Good Production Schedule
Problems
8.6. Fitting an S-Shaped Curve
Problems
8.7. Portfolio Optimization
Problems
8.8. Optimal Permutation Models
Problems
8.9. Conclusion
Summary of Key Management Science Terms
Summary of Key Excel Terms
Problems
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
Identifying the Problem
Possible Decisions
Possible Outcomes
Probabilities of Outcomes
Payoffs and Costs
Decision Criterion
More about the EMV Criterion
Decision Trees
Problems
9.3. Single-Stage Decision Problems
Problems
9.4. The PrecisionTree Add-In
Problems
9.5. Multistage Decision Problems
Problems
9.6. The Role of Risk Aversion
Utility Functions
Exponential Utility
Is Expected Utility Maximization Used?
Problems
9.7. Conclusion
Summary of Key Terms
Problems
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
Types of Probability Distributions
Common Probability Distributions
Using @RISK to Explore Probability Distributions
Problems
10.3. Simulation and the Flaw of Averages
10.4. Simulation with Built-in Excel Tools
Problems
10.5. Introduction to @RISK
@RISK Features
Loading @RISK
@RISK Models with a Single Random Input Variable
Some Limitations of @RISK
@RISK Models with Several Random Input Variables
Problems
10.6. The Effects of Input Distributions on Results
Effect of the Shape of the Input Distribution(s)
Effect of Correlated Input Variables
Problems
10.7. Conclusion
Summary of Key Management Science Terms
Key Excel Terms
Problems
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
Bidding for Contracts
Warranty Costs
Drug Production with Uncertain Timing and Yield
Problems
11.3. Financial Models
Financial Planning Models
Cash Balance Models
Investment Models
Simulating Stock Prices and Options
Modeling the Price of a Stock
Valuing a European Option
Problems
11.4. Marketing Models
Models of Customer Loyalty
Marketing and Sales Models
Problems
11.5. Simulating Games of Chance
Simulating the Game of Craps
Simulating the NCAA Basketball Tournament
Problems
11.6. Conclusion
Summary of Key Terms
Problems
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
Chapter 12. Queueing Models
12.1. Introduction
12.2. Elements of Queueing Models
Characteristics of Arrivals
Service Discipline
Service Characteristics
Short-Run versus Steady-State Behavior
12.3. The Exponential Distribution
The Memoryless Property
The Poisson Process Model
Problems
12.4. Important Queueing Relationships
Little’s Formula
Other Relationships
Problems
12.5. Analytic Steady-State Queueing Models
The Basic Single-Server Model
The Basic Multiple-Server Model
A Comparison of Models
The Effect of the Traffic Intensity
Other Exponential Models
Erlang Loss Model
General Multiple-Server Model
Problems
12.6. Queueing Simulation Models
Problems
12.7. Conclusion
Summary of Key Management Science Terms
Summary of Key Excel Terms
Problems
Case 12.1. Catalog Company Phone Orders
Chapter 13. Regression and Forecasting Models
13.1. Introduction
13.2. Overview of Regression Models
The Least-Squares Line
Prediction and Fitted Values
Measures of Goodness-of-Fit
13.3. Simple Regression Models
Regression-Based Trend Models
Using an Explanatory Variable Other than Time
Problems
13.4. Multiple Regression Models
Incorporating Categorical Variables
A Caution about Regression Assumptions
Problems
13.5. Overview of Time Series Models
Measures of Forecast Error
13.6. Moving Averages Models
Problems
13.7. Exponential Smoothing Models
Simple Exponential Smoothing
Holt’s Method for Trend
Winters’ Method for Seasonality
Problems
13.8. Conclusion
Summary of Key Management Science Terms
Summary of Key Excel Terms
Problems
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
Logistic Regression
Neural Networks
Naïve Bayes
Classification Trees
Measures of Classification Accuracy
Classification with Rare Events
Problems
14.3. Clustering Methods
Problems
14.4. Conclusion
Summary of Key Terms
Problems
Case 14.1. Houston Area Survey
Chapter 15. Project Management
15.1. Introduction
15.2. The Basic CPM Model
Problems
15.3. Modeling Allocation of Resources
Monitoring the Use of Resources
Crashing the Activities
Scheduling Multiple Projects
Problems
15.4. Models with Uncertain Activity Times
Problems
15.5. A Brief Look at Microsoft Project
15.6. Conclusion
Summary of Key Management Science Terms
Summary of Key Excel Terms
Problems
Chapter 16. Multiobjective Decision Making
16.1. Introduction
16.2. Goal Programming
Problems
16.3. Pareto Optimality and Trade-Off Curves
Finding a Trade-off Curve
Problems
16.4. The Analytic Hierarchy Process (AHP)
Modeling Issues
16.5. Conclusion
Summary of Key Management Science Terms
Problems
Chapter 17. Inventory and Supply Chain Models
17.1. Introduction
17.2. Categories of Inventory and Supply Chain Models
Deterministic versus Probabilistic Models
External versus Internal Demand
Ordering versus Production
Continuous versus Periodic Review
Single-Product versus Multiple-Product Models
17.3. Types of Costs in Inventory and Supply Chain Models
Ordering (or Setup) Cost
Unit Purchasing (or Production) Cost
Holding (or Carrying) Cost
Shortage (or Penalty) Cost
Revenue
17.4. Economic Order Quantity (EOQ) Models
The Basic EOQ Model
EOQ Models with Quantity Discounts
EOQ Models with Shortages Allowed
Synchronizing Orders for Several Products
Problems
17.5. Probabilistic Inventory Models
Newsvendor Model
The (R,Q) Ordering Policy
Problems
17.6. Ordering Simulation Models
Problems
17.7. Supply Chain Models
Problems
17.8. Conclusion
Summary of Key Management Science Terms
Problems
Case 17.1. Subway Token Hoarding
References

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