Spreadsheet Modeling and Decision Analysis A Practical Introduction to Business Analytics, 9th Edition Cliff Ragsdale Solutions Manual

Spreadsheet Modeling and Decision Analysis A Practical Introduction to Business Analytics, 9th Edition Cliff Ragsdale Solutions Manual

Table of contents for Spreadsheet Modeling Solutions Manual

• Cover Page
• Title Page
• Copyright Page
• Dedication
• Frontline Solvers
• Preface
• Acknowledgments
• Chapter 1. Introduction to Modeling and Decision Analysis
• 1-0. Introduction
• 1-1. The Modeling Approach to Decision Making
• 1-2. Characteristics and Benefits of Modeling
• 1-3. Mathematical Models
• 1-4. Categories of Mathematical Models
• 1-5. Business Analytics and the Problem-Solving Process
• 1-6. Anchoring and Framing Effects
• 1-7. Good Decisions vs. Good Outcomes
• 1-8. Summary
• 1-9. References
• The World of Business Analytics: Microsoft Excel Can Help You Learn More about Analytics than You Might Realize
• Questions and Problems
• Case 1-1. Patrick’s Paradox
• Chapter 2. Introduction to Optimization and Linear Programming
• 2-0. Introduction
• 2-1. Applications of Mathematical Optimization
• 2-2. Characteristics of Optimization Problems
• 2-3. Expressing Optimization Problems Mathematically
• 2-3a. Decisions
• 2-3b. Constraints
• 2-3c. Objective
• 2-4. Mathematical Programming Techniques
• 2-5. An Example LP Problem
• 2-6. Formulating LP Models
• 2-6a. Steps in Formulating an LP Model
• 2-7. Summary of the LP Model for the Example Problem
• 2-8. The General Form of an LP Model
• 2-9. Solving LP Problems: An Intuitive Approach
• 2-10. Solving LP Problems: A Graphical Approach
• 2-10a. Plotting the First Constraint
• 2-10b. Plotting the Second Constraint
• 2-10c. Plotting the Third Constraint
• 2-10d. The Feasible Region
• 2-10e. Plotting the Objective Function
• 2-10f. Finding the Optimal Solution Using Level Curves
• 2-10g. Finding the Optimal Solution by Enumerating the Corner Points
• 2-10h. Summary of Graphical Solution to LP Problems
• 2-10i. Understanding How Things Change
• 2-11. Special Conditions in LP Models
• 2-11a. Alternate Optimal Solutions
• 2-11b. Redundant Constraints
• 2-11c. Unbounded Solutions
• 2-11d. Infeasibility
• 2-12. Summary
• 2-13. References
• Questions and Problems
• Case 2-1. For the Lines They Are A-Changin’ (with apologies to Bob Dylan)
• Chapter 3. Modeling and Solving LP Problems in a Spreadsheet
• 3-0. Introduction
• 3-1. Spreadsheet Solvers
• 3-2. Solving LP Problems in a Spreadsheet
• 3-3. The Steps in Implementing an LP Model in a Spreadsheet
• 3-4. A Spreadsheet Model for the Blue Ridge Hot Tubs Problem
• 3-4a. Organizing the Data
• 3-4b. Representing the Decision Variables
• 3-4c. Representing the Objective Function
• 3-4d. Representing the Constraints
• 3-4e. Representing the Bounds on the Decision Variables
• 3-5. How Solver Views the Model
• 3-6. Using Analytic Solver
• 3-6a. Defining the Objective Cell
• 3-6b. Defining the Variable Cells
• 3-6c. Defining the Constraint Cells
• 3-6d. Defining the Nonnegativity Conditions
• 3-6e. Reviewing the Model
• 3-6f. Other Options
• 3-6g. Solving the Problem
• 3-7. Using Excel’s Built-in Solver
• 3-8. Goals and Guidelines for Spreadsheet Design
• 3-9. Make vs. Buy Decisions
• 3-9a. Defining the Decision Variables
• 3-9b. Defining the Objective Function
• 3-9c. Defining the Constraints
• 3-9d. Implementing the Model
• 3-9e. Solving the Problem
• 3-9f. Analyzing the Solution
• 3-10. An Investment Problem
• 3-10a. Defining the Decision Variables
• 3-10b. Defining the Objective Function
• 3-10c. Defining the Constraints
• 3-10d. Implementing the Model
• 3-10e. Solving the Problem
• 3-10f. Analyzing the Solution
• 3-11. A Transportation Problem
• 3-11a. Defining the Decision Variables
• 3-11b. Defining the Objective Function
• 3-11c. Defining the Constraints
• 3-11d. Implementing the Model
• 3-11e. Heuristic Solution for the Model
• 3-11f. Solving the Problem
• 3-11g. Analyzing the Solution
• 3-12. A Blending Problem
• 3-12a. Defining the Decision Variables
• 3-12b. Defining the Objective Function
• 3-12c. Defining the Constraints
• 3-12d. Some Observations about Constraints, Reporting, and Scaling
• 3-12e. Re-Scaling the Model
• 3-12f. Implementing the Model
• 3-12g. Solving the Problem
• 3-12h. Analyzing the Solution
• 3-13. A Production and Inventory Planning Problem
• 3-13a. Defining the Decision Variables
• 3-13b. Defining the Objective Function
• 3-13c. Defining the Constraints
• 3-13d. Implementing the Model
• 3-13e. Solving the Problem
• 3-13f. Analyzing the Solution
• 3-14. A Multiperiod Cash Flow Problem
• 3-14a. Defining the Decision Variables
• 3-14b. Defining the Objective Function
• 3-14c. Defining the Constraints
• 3-14d. Implementing the Model
• 3-14e. Solving the Problem
• 3-14f. Analyzing the Solution
• 3-14g. Modifying the Taco-Viva Problem to Account for Risk (Optional)
• 3-14h. Implementing the Risk Constraints
• 3-14i. Solving the Problem
• 3-14j. Analyzing the Solution
• 3-15. Data Envelopment Analysis
• 3-15a. Defining the Decision Variables
• 3-15b. Defining the Objective
• 3-15c. Defining the Constraints
• 3-15d. Implementing the Model
• 3-15e. Solving the Problem
• 3-15f. Analyzing the Solution
• 3-16. Summary
• 3-17. References
• The World of Business Analytics: Optimizing Production, Inventory, and Distribution at Kellogg
• Questions and Problems
• Case 3-1. Putting the Link in the Supply Chain
• Case 3-2. Foreign Exchange Trading at Baldwin Enterprises
• Case 3-3. The Wolverine Retirement Fund
• Case 3-4. Saving the Manatees
• Chapter 4. Sensitivity Analysis and the Simplex Method
• 4-0. Introduction
• 4-1. The Purpose of Sensitivity Analysis
• 4-2. Approaches to Sensitivity Analysis
• 4-3. An Example Problem
• 4-4. The Answer Report
• 4-5. The Sensitivity Report
• 4-5a. Changes in the Objective Function Coefficients
• 4-5b. A Comment about Constancy
• 4-5c. Alternate Optimal Solutions
• 4-5d. Changes in the RHS Values
• 4-5e. Shadow Prices for Nonbinding Constraints
• 4-5f. A Note about Shadow Prices
• 4-5g. Shadow Prices and the Value of Additional Resources
• 4-5h. Other Uses of Shadow Prices
• 4-5i. The Meaning of the Reduced Costs
• 4-5j. Analyzing Changes in Constraint Coefficients
• 4-5k. Simultaneous Changes in Objective Function Coefficients
• 4-5l. A Warning about Degeneracy
• 4-6. Ad Hoc Sensitivity Analysis
• 4-6a. Creating Spider Plots and Tables
• 4-6b. Creating a Solver Table
• 4-6c. Comments
• 4-7. Robust Optimization
• 4-8. The Simplex Method
• 4-8a. Creating Equality Constraints Using Slack Variables
• 4-8b. Basic Feasible Solutions
• 4-8c. Finding the Best Solution
• 4-9. Summary
• 4-10. References
• The World of Business Analytics: Fuel Management and Allocation Model Helps National Airlines Adapt to Cost and Supply Changes
• Questions and Problems
• Case 4-1. A Nut Case
• Case 4-2. Parket Sisters
• Case 4-3. Kamm Industries
• Chapter 5. Network Modeling
• 5-0. Introduction
• 5-1. The Transshipment Problem
• 5-1a. Characteristics of Network Flow Problems
• 5-1b. The Decision Variables for Network Flow Problems
• 5-1c. The Objective Function for Network Flow Problems
• 5-1d. The Constraints for Network Flow Problems
• 5-1e. Implementing the Model in a Spreadsheet
• 5-1f. Analyzing the Solution
• 5-2. The Shortest Path Problem
• 5-2a. An LP Model for the Example Problem
• 5-2b. The Spreadsheet Model and Solution
• 5-2c. Network Flow Models and Integer Solutions
• 5-3. The Equipment Replacement Problem
• 5-3a. The Spreadsheet Model and Solution
• 5-4. Transportation/Assignment Problems
• 5-5. Generalized Network Flow Problems
• 5-5a. Formulating an LP Model for the Recycling Problem
• 5-5b. Implementing the Model
• 5-5c. Analyzing the Solution
• 5-5d. Generalized Network Flow Problems and Feasibility
• 5-6. Maximal Flow Problems
• 5-6a. An Example of a Maximal Flow Problem
• 5-6b. The Spreadsheet Model and Solution
• 5-7. Special Modeling Considerations
• 5-8. Minimal Spanning Tree Problems
• 5-8a. An Algorithm for the Minimal Spanning Tree Problem
• 5-8b. Solving the Example Problem
• 5-9. Summary
• 5-10. References
• The World of Business Analytics: Yellow Freight System Boosts Profits and Quality with Network Optimization
• Questions and Problems
• Case 5-1. Hamilton & Jovanovich
• Case 5-2. Old Dominion Energy
• Case 5-3. US Express
• Case 5-4. The Major Electric Corporation
• Chapter 6. Integer Linear Programming
• 6-0. Introduction
• 6-1. Integrality Conditions
• 6-2. Relaxation
• 6-3. Solving the Relaxed Problem
• 6-4. Bounds
• 6-5. Rounding
• 6-6. Stopping Rules
• 6-7. Solving ILP Problems Using Solver
• 6-8. Other ILP Problems
• 6-9. An Employee Scheduling Problem
• 6-9a. Defining the Decision Variables
• 6-9b. Defining the Objective Function
• 6-9c. Defining the Constraints
• 6-9d. A Note about the Constraints
• 6-9e. Implementing the Model
• 6-9f. Solving the Model
• 6-9g. Analyzing the Solution
• 6-10. Binary Variables
• 6-11. A Capital Budgeting Problem
• 6-11a. Defining the Decision Variables
• 6-11b. Defining the Objective Function
• 6-11c. Defining the Constraints
• 6-11d. Setting up the Binary Variables
• 6-11e. Implementing the Model
• 6-11f. Solving the Model
• 6-11g. Comparing the Optimal Solution to a Heuristic Solution
• 6-12. Binary Variables and Logical Conditions
• 6-13. The Line Balancing Problem
• 6-13a. Defining the Decision Variables
• 6-13b. Defining the Constraints
• 6-13c. Defining the Objective
• 6-13d. Implementing the Model
• 6-13e. Analyzing the Solution
• 6-13f. Extension
• 6-14. The Fixed-Charge Problem
• 6-14a. Defining the Decision Variables
• 6-14b. Defining the Objective Function
• 6-14c. Defining the Constraints
• 6-14d. Determining Values for “Big M”
• 6-14e. Implementing the Model
• 6-14f. Solving the Model
• 6-14g. Analyzing the Solution
• 6-14h. A Comment on if( ) Functions
• 6-15. Minimum Order/Purchase Size
• 6-16. Quantity Discounts
• 6-16a. Formulating the Model
• 6-16b. The Missing Constraints
• 6-17. A Contract Award Problem
• 6-17a. Formulating the Model: The Objective Function and Transportation Constraints
• 6-17b. Implementing the Transportation Constraints
• 6-17c. Formulating the Model: The Side Constraints
• 6-17d. Implementing the Side Constraints
• 6-17e. Solving the Model
• 6-17f. Analyzing the Solution
• 6-18. The Branch-and-Bound Algorithm (Optional)
• 6-18a. Branching
• 6-18b. Bounding
• 6-18c. Branching Again
• 6-18d. Bounding Again
• 6-18e. Summary of B&B Example
• 6-19. Summary
• 6-20. References
• The World of Business Analytics: Who Eats the Float?—Maryland National Improves Check Clearing Operations and Cuts Costs
• Questions and Problems
• Case 6-1. Optimizing a Timber Harvest
• Case 6-2. Power Dispatching at Old Dominion
• Case 6-3. The MasterDebt Lockbox Problem
• Case 6-4. Removing Snow in Montreal
• Chapter 7. Goal Programming and Multiple Objective Optimization
• 7-0. Introduction
• 7-1. Goal Programming
• 7-2. A Goal Programming Example
• 7-2a. Defining the Decision Variables
• 7-2b. Defining the Goals
• 7-2c. Defining the Goal Constraints
• 7-2d. Defining the Hard Constraints
• 7-2e. GP Objective Functions
• 7-2f. Defining the Objective
• 7-2g. Implementing the Model
• 7-2h. Solving the Model
• 7-2i. Analyzing the Solution
• 7-2j. Revising the Model
• 7-2k. Trade-Offs: The Nature of GP
• 7-3. Comments about Goal Programming
• 7-4. Multiple Objective Optimization
• 7-5. An MOLP Example
• 7-5a. Defining the Decision Variables
• 7-5b. Defining the Objectives
• 7-5c. Defining the Constraints
• 7-5d. Implementing the Model
• 7-5e. Determining Target Values for the Objectives
• 7-5f. Summarizing the Target Solutions
• 7-5g. Determining a GP Objective
• 7-5h. The Minimax Objective
• 7-5i. Implementing the Revised Model
• 7-5j. Solving the Model
• 7-6. Comments on MOLP
• 7-7. Summary
• 7-8. References
• The World of Business Analytics: Truck Transport Corporation Controls Costs and Disruptions While Relocating a Terminal
• Questions and Problems
• Case 7-1. Removing Snow in Montreal
• Case 7-2. Planning Diets for the Food Stamp Program
• Case 7-3. Sales Territory Planning at Caro-Life
• Chapter 8. Nonlinear Programming and Evolutionary Optimization
• 8-0. Introduction
• 8-1. The Nature of NLP Problems
• 8-2. Solution Strategies for NLP Problems
• 8-3. Local vs. Global Optimal Solutions
• 8-4. Economic Order Quantity Models
• 8-4a. Implementing The Model
• 8-4b. Solving the Model
• 8-4c. Analyzing the Solution
• 8-4d. Comments on the EOQ Model
• 8-5. Location Problems
• 8-5a. Defining The Decision Variables
• 8-5b. Defining the Objective
• 8-5c. Defining the Constraints
• 8-5d. Implementing the Model
• 8-5e. Solving the Model and Analyzing the Solution
• 8-5f. Another Solution to the Problem
• 8-5g. Some Comments about the Solution to Location Problems
• 8-6. Nonlinear Network Flow Problem
• 8-6a. Defining the Decision Variables
• 8-6b. Defining the Objective
• 8-6c. Defining the Constraints
• 8-6d. Implementing the Model
• 8-6e. Solving the Model and Analyzing the Solution
• 8-7. Project Selection Problems
• 8-7a. Defining the Decision Variables
• 8-7b. Defining the Objective Function
• 8-7c. Defining the Constraints
• 8-7d. Implementing the Model
• 8-7e. Solving the Model
• 8-8. Optimizing Existing Financial Spreadsheet Models
• 8-8a. Implementing the Model
• 8-8b. Optimizing the Spreadsheet Model
• 8-8c. Analyzing the Solution
• 8-8d. Comments on Optimizing Existing Spreadsheets
• 8-9. The Portfolio Selection Problem
• 8-9a. Defining the Decision Variables
• 8-9b. Defining the Objective
• 8-9c. Defining the Constraints
• 8-9d. Implementing the Model
• 8-9e. Analyzing the Solution
• 8-9f. Handling Conflicting Objectives in Portfolio Problems
• 8-10. Sensitivity Analysis
• 8-10a. Lagrange Multipliers
• 8-10b. Reduced Gradients
• 8-11. Solver Options for Solving NLPs
• 8-12. Evolutionary Algorithms
• 8-13. Forming Fair Teams
• 8-13a. A Spreadsheet Model For the Problem
• 8-13b. Solving the Model
• 8-13c. Analyzing the Solution
• 8-14. The Traveling Salesperson Problem
• 8-14a. A Spreadsheet Model For the Problem
• 8-14b. Solving the Model
• 8-14c. Analyzing the Solution
• 8-15. Summary
• 8-16. References
• The World of Business Analytics: Water Spilled Is Energy Lost: Pacific Gas and Electric Uses Nonlinear Optimization to Manage Power Generation
• Questions and Problems
• Case 8-1. Tour de Europe
• Case 8-2. Electing the Next President
• Case 8-3. Making Windows at Wella
• Case 8-4. Newspaper Advertising Insert Scheduling
• Chapter 9. Regression Analysis
• 9-0. Introduction
• 9-1. An Example
• 9-2. Regression Models
• 9-3. Simple Linear Regression Analysis
• 9-4. Defining “Best Fit”
• 9-5. Solving the Problem Using Solver
• 9-6. Solving the Problem Using the Regression Tool
• 9-7. Evaluating the Fit
• 9-8. The R 2 Statistic
• 9-9. Making Predictions
• 9-9a. The Standard Error
• 9-9b. Prediction Intervals for New Values of Y
• 9-9c. Confidence Intervals for Mean Values of Y
• 9-9d. Extrapolation
• 9-10. Statistical Tests for Population Parameters
• 9-10a. Analysis of Variance
• 9-10b. Assumptions for the Statistical Tests
• 9-10c. Statistical Tests
• 9-11. Introduction to Multiple Regression
• 9-12. A Multiple Regression Example
• 9-13. Selecting the Model
• 9-13a. Models with One Independent Variable
• 9-13b. Models with Two Independent Variables
• 9-13c. Inflating R 2
• 9-13d. The Adjusted- R 2 Statistic
• 9-13e. The Best Model with Two Independent Variables
• 9-13f. Multicollinearity
• 9-13g. The Model with Three Independent Variables
• 9-14. Making Predictions
• 9-15. Other Model Selection Issues
• 9-16. Binary Independent Variables
• 9-17. Statistical Tests for the Population Parameters
• 9-18. Polynomial Regression
• 9-18a. Expressing Nonlinear Relationships Using Linear Models
• 9-18b. Summary of Nonlinear Regression
• 9-19. Summary
• 9-20. References
• The World of Business Analytics: Better Predictions Create Cost Savings for Ohio National Bank
• Questions and Problems
• Case 9-1. Diamonds Are Forever
• Case 9-2. Fiasco in Florida
• Case 9-3. The Georgia Public Service Commission
• Chapter 10. Data Mining
• 10-0. Introduction
• 10-1. Data Mining Overview
• 10-2. Classification
• 10-2a. A Classification Example
• 10-3. Classification Data Partitioning
• 10-4. Discriminant Analysis
• 10-4a. Discriminant Analysis Example
• 10-5. Logistic Regression
• 10-5a. Logistic Regression Example
• 10-6. k-Nearest Neighbor
• 10-6a. k-Nearest Neighbor Example
• 10-7. Classification Trees
• 10-7a. Classification Tree Example
• 10-8. Neural Networks
• 10-8a. Neural Network Example
• 10-9. Naïve Bayes
• 10-9a. Naïve Bayes Example
• 10-10. Comments on Classification
• 10-10a. Combining Classifications with Ensemble Methods
• 10-10b. The Role of Test Data
• 10-11. Prediction
• 10-12. Association Rules (Affinity Analysis)
• 10-12a. Association Rules Example
• 10-13. Cluster Analysis
• 10-13a. Cluster Analysis Example
• 10-13b. k-Mean Clustering Example
• 10-13c. Hierarchical Clustering Example
• 10-14. Time Series
• 10-15. Summary
• 10-16. References
• The World of Business Analytics: La Quinta Motor Inns Predicts Successful Sites with Discriminant Analysis
• Questions and Problems
• Case 10-1. Detecting Management Fraud
• Chapter 11. Time Series Forecasting
• 11-0. Introduction
• 11-1. Time Series Methods
• 11-2. Measuring Accuracy
• 11-3. Stationary Models
• 11-4. Moving Averages
• 11-4a. Forecasting with the Moving Average Model
• 11-5. Weighted Moving Averages
• 11-5a. Forecasting with the Weighted Moving Average Model
• 11-6. Exponential Smoothing
• 11-6a. Forecasting with the Exponential Smoothing Model
• 11-7. Seasonality
• 11-8. Stationary Data with Additive Seasonal Effects
• 11-8a. Forecasting with the Model
• 11-9. Stationary Data with Multiplicative Seasonal Effects
• 11-9a. Forecasting with the Model
• 11-10. Trend Models
• 11-10a. An Example
• 11-11. Double Moving Average
• 11-11a. Forecasting with the Model
• 11-12. Double Exponential Smoothing (Holt’s Method)
• 11-12a. Forecasting with Holt’s Method
• 11-13. Holt-Winter’s Method for Additive Seasonal Effects
• 11-13a. Forecasting with Holt-Winter’s Additive Method
• 11-14. Holt-Winter’s Method for Multiplicative Seasonal Effects
• 11-14a. Forecasting with Holt-Winter’s Multiplicative Method
• 11-15. Modeling Time Series Trends Using Regression
• 11-16. Linear Trend Model
• 11-16a. Forecasting with the Linear Trend Model
• 11-17. Quadratic Trend Model
• 11-17a. Forecasting with the Quadratic Trend Model
• 11-18. Modeling Seasonality with Regression Models
• 11-19. Adjusting Trend Predictions with Seasonal Indices
• 11-19a. Computing Seasonal Indices
• 11-19b. Forecasting with Seasonal Indices
• 11-19c. Refining the Seasonal Indices
• 11-20. Seasonal Regression Models
• 11-20a. The Seasonal Model
• 11-20b. Forecasting with the Seasonal Regression Model
• 11-21. Combining Forecasts
• 11-22. Summary
• 11-13. References
• The World of Business Analytics: Check Processing Revisited: The Chemical Bank Experience
• Questions and Problems
• Case 11-1. PB Chemical Corporation
• Case 11-2. Forecasting COLAs
• Case 11-3. Strategic Planning at Fysco Foods
• Chapter 12. Introduction to Simulation Using Analytic Solver
• 12-0. Introduction
• 12-1. Random Variables and Risk
• 12-2. Why Analyze Risk?
• 12-3. Methods of Risk Analysis
• 12-3a. Best-Case/Worst-Case Analysis
• 12-3b. What-If Analysis
• 12-3c. Simulation
• 12-4. A Corporate Health Insurance Example
• 12-4a. A Critique of the Base Case Model
• 12-5. Spreadsheet Simulation Using Analytic Solver
• 12-5a. Starting Analytic Solver
• 12-6. Random Number Generators
• 12-6a. Discrete vs. Continuous Random Variables
• 12-7. Preparing the Model for Simulation
• 12-7a. Alternate RNG Entry
• 12-8. Running the Simulation
• 12-8a. Selecting the Output Cells to Track
• 12-8b. Selecting the Number of Replications
• 12-8c. Selecting What Gets Displayed on the Worksheet
• 12-8d. Running the Simulation
• 12-9. Data Analysis
• 12-9a. The Best Case and the Worst Case
• 12-9b. The Frequency Distribution of the Output Cells
• 12-9c. The Cumulative Distribution of the Output Cells
• 12-9d. Obtaining other Cumulative Probabilities
• 12-9e. Sensitivity Analysis
• 12-10. The Uncertainty of Sampling
• 12-10a. Constructing a Confidence Interval for the True Population Mean
• 12-10b. Constructing a Confidence Interval for a Population Proportion
• 12-10c. Sample Sizes and Confidence Interval Widths
• 12-11. Interactive Simulation
• 12-12. The Benefits of Simulation
• 12-13. Additional Uses of Simulation
• 12-14. A Reservation Management Example
• 12-14a. Implementing the Model
• 12-14b. Details for Multiple Simulations
• 12-14c. Running the Simulations
• 12-14d. Data Analysis
• 12-15. An Inventory Control Example
• 12-15a. Creating the RNGs
• 12-15b. Implementing the Model
• 12-15c. Replicating the Model
• 12-15d. Optimizing the Model
• 12-15e. Analyzing the Solution
• 12-15f. Other Measures of Risk
• 12-16. A Project Selection Example
• 12-16a. A Spreadsheet Model
• 12-16b. Solving and Analyzing the Problem with Analytic Solver
• 12-16c. Considering Another Solution
• 12-17. A Portfolio Optimization Example
• 12-17a. A Spreadsheet Model
• 12-17b. Solving the Problem with Analytic Solver
• 12-18. Summary
• 12-19. References
• The World of Business Analytics: The U.S. Postal Service Moves to the Fast Lane
• Questions and Problems
• Case 12-1. Live Well, Die Broke
• Case 12-2. Death and Taxes
• Case 12-3. The Sound’s Alive Company
• Case 12-4. The Foxridge Investment Group
• Chapter 13. Queuing Theory
• 13-0. Introduction
• 13-1. The Purpose of Queuing Models
• 13-2. Queuing System Configurations
• 13-3. Characteristics of Queuing Systems
• 13-3a. Arrival Rate
• 13-3b. Service Rate
• 13-4. Kendall Notation
• 13-5. Queuing Models
• 13-6. The M/M/s Model
• 13-6a. An Example
• 13-6b. The Current Situation
• 13-6c. Adding a Server
• 13-6d. Economic Analysis
• 13-7. The M/M/s Model with Finite Queue Length
• 13-7a. The Current Situation
• 13-7b. Adding a Server
• 13-8. The M/M/s Model with Finite Population
• 13-8a. An Example
• 13-8b. The Current Situation
• 13-8c. Adding Servers
• 13-9. The M/G/1 Model
• 13-9a. The Current Situation
• 13-19b. Adding the Automated Dispensing Device
• 13-10. The M/D/1 Model
• 13-11. Simulating Queues and the Steady-State Assumption
• 13-12. Summary
• 13-13. References
• The World of Business Analytics: “Wait Watchers” Try to Take Stress Out of Standing in Line
• Questions and Problems
• Case 13-1. May the (Police) Force Be with You
• Case 13-2. Call Center Staffing at Vacations Inc.
• Case 13-3. Bullseye Department Store
• Chapter 14. Decision Analysis
• 14-0. Introduction
• 14-1. Good Decisions vs. Good Outcomes
• 14-2. Characteristics of Decision Problems
• 14-3. An Example
• 14-4. The Payoff Matrix
• 14-4a. Decision Alternatives
• 14-4b. States of Nature
• 14-4c. The Payoff Values
• 14-5. Decision Rules
• 14-6. Nonprobabilistic Methods
• 14-6a. The Maximax Decision Rule
• 14-6b. The Maximin Decision Rule
• 14-6c. The Minimax Regret Decision Rule
• 14-7. Probabilistic Methods
• 14-7a. Expected Monetary Value
• 14-7b. Expected Regret
• 14-7c. Sensitivity Analysis
• 14-8. The Expected Value of Perfect Information
• 14-9. Decision Trees
• 14-9a. Rolling Back a Decision Tree
• 14-10. Creating Decision Trees with Analytic Solver
• 14-10a. Adding Event Nodes
• 14-10b. Determining the Payoffs and EMVs
• 14-10c. Other Features
• 14-11. Multistage Decision Problems
• 14-11a. A Multistage Decision Tree
• 14-11b. Developing a Risk Profile
• 14-12. Sensitivity Analysis
• 14-12a. Tornado Charts
• 14-12b. Strategy Tables
• 14-12c. Strategy Charts
• 14-13. Using Sample Information in Decision Making
• 14-13a. Conditional Probabilities
• 14-13b. The Expected Value of Sample Information
• 14-14. Computing Conditional Probabilities
• 14-14a. Bayes’ Theorem
• 14-15. Utility Theory
• 14-15a. Utility Functions
• 14-15b. Constructing Utility Functions
• 14-15c. Using Utilities to Make Decisions
• 14-15d. The Exponential Utility Function
• 14-15e. Incorporating Utilities in Decision Trees
• 14-16. Multicriteria Decision Making
• 14-17. The Multicriteria Scoring Model
• 14-18. The Analytic Hierarchy Process
• 14-18a. Pairwise Comparisons
• 14-18b. Normalizing the Comparisons
• 14-18c. Consistency
• 14-18d. Obtaining Scores for the Remaining Criteria
• 14-18e. Obtaining Criterion Weights
• 14-18f. Implementing the Scoring Model
• 14-19. Summary
• 14-20. References
• The World of Business Analytics: Decision Theory Helps Hallmark Trim Discards
• Questions and Problems
• Case 14-1. Prezcott Pharma
• Case 14-2. Hang On or Give Up?
• Case 14-3. Should Larry Junior Go to Court or Settle?
• Case 14-4. The Spreadsheet Wars
• Chapter 15. Project Management
• 15-0. Introduction
• 15-1. An Example
• 15-2. Creating the Project Network
• 15-2a. Start and Finish Points
• 15-3. CPM: An Overview
• 15-4. The Forward Pass
• 15-5. The Backward Pass
• 15-6. Determining the Critical Path
• 15-6a. A Note on Slack
• 15-7. Project Management Using Spreadsheets
• 15-7a. Important Implementation Issue
• 15-8. Gantt Charts
• 15-9. Project Crashing
• 15-9a. An LP Approach to Crashing
• 15-9b. Determining the Earliest Crash Completion Time
• 15-9c. Implementing the Model
• 15-9d. Solving the Model
• 15-9e. Determining a Least Costly Crash Schedule
• 15-9f. Crashing as an MOLP
• 15-10. Pert: An Overview
• 15-10a. The Problems with PERT
• 15-10b. Implications
• 15-11. Simulating Project Networks
• 15-11a. An Example
• 15-11b. Generating Random Activity Times
• 15-11c. Implementing the Model
• 15-11d. Running the Simulation
• 15-11e. Analyzing the Results
• 15-12. Microsoft Project
• 15-13. Summary
• 15-14. References
• The World of Business Analytics: Food and Drug Administration Uses PERT to Control the Timeliness of Research and Development Projects
• Questions and Problems
• Case 15-1. Project Management at a Crossroad
• Case 15-2. The World Trade Center Clean-Up
• Case 15-3. The Imagination Toy Corporation

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