Spreadsheet Modeling and Decision Analysis A Practical Introduction to Business Analytics, 9th Edition Test bank

PRINT ISBN: 9780357132098, 0357132092, ETEXT ISBN: 9798214353142

Table of contents for Spreadsheet Modeling Test Bank

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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