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ISBN 10: 130594741X
ISBN 13: 9781305947412
Author: Cliff Ragsdale
Written by an innovator in teaching spreadsheets and a highly regarded leader in business analytics, Cliff Ragsdale’s SPREADSHEET MODELING AND DECISION ANALYSIS: A PRACTICAL INTRODUCTION TO BUSINESS ANALYTICS, 8E helps readers master important spreadsheet and business analytics skills. Readers find everything needed to become proficient in today’s most widely used business analytics techniques using Microsoft Office Excel 2016. Learning to make effective decisions in today’s business world takes training and experience. Author Cliff Ragsdale guides learners through the skills needed, using the latest Excel for Windows. Readers apply what they’ve learned to real business situations with step-by-step instructions and annotated screen images that make examples easy to follow. The World of Management Science sections further demonstrates how each topic applies to a real company.
Test Bank for Spreadsheet Modeling and Decision Analysis 8th Table of contents:
Chapter 1. Introduction to Modeling and Decision Analysis
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
Questions and Problems
Case 1.1. Patrick’s Paradox
Chapter 2. Introduction to Optimization and Linear Programming
2.1. Applications of Mathematical Optimization
2.2. Characteristics of Optimization Problems
2.3. Expressing Optimization Problems Mathematically
2.3.1. Decisions
2.3.2. Constraints
2.3.3. Objective
2.4. Mathematical Programming Techniques
2.5. An Example LP Problem
2.6. Formulating LP Models
2.6.1. 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
2.10.1. Plotting the First Constraint
2.10.2. Plotting the Second Constraint
2.10.3. Plotting the Third Constraint
2.10.4. The Feasible Region
2.10.5. Plotting the Objective Function
2.10.6. Finding the Optimal Solution Using Level Curves
2.10.7. Finding the Optimal Solution by Enumerating the Corner Points
2.10.8. Summary of Graphical Solution to LP Problems
2.10.9. Understanding How Things Change
2.11. Special Conditions in LP Models
2.11.1. Alternate Optimal Solutions
2.11.2. Redundant Constraints
2.11.3. Unbounded Solutions
2.11.4. 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
3.1. 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
3.4.1. Organizing the Data
3.4.2. Representing the Decision Variables
3.4.3. Representing the Objective Function
3.4.4. Representing the Constraints
3.4.5. Representing the Bounds on the Decision Variables
3.5. How Solver Views the Model
3.6. Using Analytic Solver Platform
3.6.1. Defining the Objective Cell
3.6.2. Defining the Variable Cells
3.6.3. Defining the Constraint Cells
3.6.4. Defining the Nonnegativity Conditions
3.6.5. Reviewing the Model
3.6.6. Other Options
3.6.7. 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
3.9.1. Defining the Decision Variables
3.9.2. Defining the Objective Function
3.9.3. Defining the Constraints
3.9.4. Implementing the Model
3.9.5. Solving the Problem
3.9.6. Analyzing the Solution
3.10. An Investment Problem
3.10.1. Defining the Decision Variables
3.10.2. Defining the Objective Function
3.10.3. Defining the Constraints
3.10.4. Implementing the Model
3.10.5. Solving the Problem
3.10.6. Analyzing the Solution
3.11. A Transportation Problem
3.11.1. Defining the Decision Variables
3.11.2. Defining the Objective Function
3.11.3. Defining the Constraints
3.11.4. Implementing the Model
3.11.5. Heuristic Solution for the Model
3.11.6. Solving the Problem
3.11.7. Analyzing the Solution
3.12. A Blending Problem
3.12.1. Defining the Decision Variables
3.12.2. Defining the Objective Function
3.12.3. Defining the Constraints
3.12.4. Some Observations about Constraints, Reporting, and Scaling
3.12.5. Re-Scaling the Model
3.12.6. Implementing the Model
3.12.7. Solving the Problem
3.12.8. Analyzing the Solution
3.13. A Production and Inventory Planning Problem
3.13.1. Defining the Decision Variables
3.13.2. Defining the Objective Function
3.13.3. Defining the Constraints
3.13.4. Implementing the Model
3.13.5. Solving the Problem
3.13.6. Analyzing the Solution
3.14. A Multiperiod Cash Flow Problem
3.14.1. Defining the Decision Variables
3.14.2. Defining the Objective Function
3.14.3. Defining the Constraints
3.14.4. Implementing the Model
3.14.5. Solving the Problem
3.14.6. Analyzing the Solution
3.14.7. Modifying the Taco-Viva Problem to Account for Risk (Optional)
3.14.8. Implementing the Risk Constraints
3.14.9. Solving the Problem
3.14.10. Analyzing the Solution
3.15. Data Envelopment Analysis
3.15.1. Defining the Decision Variables
3.15.2. Defining the Objective
3.15.3. Defining the Constraints
3.15.4. Implementing the Model
3.15.5. Solving the Problem
3.15.6. Analyzing the Solution
3.16. Summary
3.17. References
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
4.1. 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
4.5.1. Changes in the Objective Function Coefficients
4.5.2. A Comment about Constancy
4.5.3. Alternate Optimal Solutions
4.5.4. Changes in the RHS Values
4.5.5. Shadow Prices for Nonbinding Constraints
4.5.6. A Note about Shadow Prices
4.5.7. Shadow Prices and the Value of Additional Resources
4.5.8. Other Uses of Shadow Prices
4.5.9. The Meaning of the Reduced Costs
4.5.10. Analyzing Changes in Constraint Coefficients
4.5.11. Simultaneous Changes in Objective Function Coefficients
4.5.12. A Warning about Degeneracy
4.6. The Limits Report
4.7. Ad Hoc Sensitivity Analysis
4.7.1. Creating Spider Plots and Tables
4.7.2. Creating a Solver Table
4.7.3. Comments
4.8. Robust Optimization
4.9. The Simplex Method
4.9.1. Creating Equality Constraints Using Slack Variables
4.9.2. Basic Feasible Solutions
4.9.3. Finding the Best Solution
4.10. Summary
4.11. References
Questions and Problems
Case 4.1. A Nut Case
Case 4.2. Parket Sisters
Case 4.3. Kamm Industries
Chapter 5. Network Modeling
5.1. The Transshipment Problem
5.1.1. Characteristics of Network Flow Problems
5.1.2. The Decision Variables for Network Flow Problems
5.1.3. The Objective Function for Network Flow Problems
5.1.4. The Constraints for Network Flow Problems
5.1.5. Implementing the Model in a Spreadsheet
5.1.6. Analyzing the Solution
5.2. The Shortest Path Problem
5.2.1. An LP Model for the Example Problem
5.2.2. The Spreadsheet Model and Solution
5.2.3. Network Flow Models and Integer Solutions
5.3. The Equipment Replacement Problem
5.3.1. The Spreadsheet Model and Solution
5.4. Transportation/Assignment Problems
5.5. Generalized Network Flow Problems
5.5.1. Formulating an LP Model for the Recycling Problem
5.5.2. Implementing the Model
5.5.3. Analyzing the Solution
5.5.4. Generalized Network Flow Problems and Feasibility
5.6. Maximal Flow Problems
5.6.1. An Example of a Maximal Flow Problem
5.6.2. The Spreadsheet Model and Solution
5.7. Special Modeling Considerations
5.8. Minimal Spanning Tree Problems
5.8.1. An Algorithm for the Minimal Spanning Tree Problem
5.8.2. Solving the Example Problem
5.9. Summary
5.10. References
Questions and Problems
Case 5.1. Hamilton & Jacobs
Case 5.2. Old Dominion Energy
Case 5.3. US Express
Case 5.4. The Major Electric Corporation
Chapter 6. Integer Linear Programming
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
6.9.1. Defining the Decision Variables
6.9.2. Defining the Objective Function
6.9.3. Defining the Constraints
6.9.4. A Note about the Constraints
6.9.5. Implementing the Model
6.9.6. Solving the Model
6.9.7. Analyzing the Solution
6.10. Binary Variables
6.11. A Capital Budgeting Problem
6.11.1. Defining the Decision Variables
6.11.2. Defining the Objective Function
6.11.3. Defining the Constraints
6.11.4. Setting up the Binary Variables
6.11.5. Implementing the Model
6.11.6. Solving the Model
6.11.7. Comparing the Optimal Solution to a Heuristic Solution
6.12. Binary Variables and Logical Conditions
6.13. The Line Balancing Problem
6.13.1. Defining the Decision Variables
6.13.2. Defining the Constraints
6.13.3. Defining the Objective
6.13.4. Implementing the Model
6.13.5. Analyzing the Solution
6.13.6. Extension
6.14. The Fixed-Charge Problem
6.14.1. Defining the Decision Variables
6.14.2. Defining the Objective Function
6.14.3. Defining the Constraints
6.14.4. Determining Values for “Big M”
6.14.5. Implementing the Model
6.14.6. Solving the Model
6.14.7. Analyzing the Solution
6.14.8. A Comment on IF( ) Functions
6.15. Minimum Order/Purchase Size
6.16. Quantity Discounts
6.16.1. Formulating the Model
6.16.2. The Missing Constraints
6.17. A Contract Award Problem
6.17.1. Formulating the Model: The Objective Function and Transportation Constraints
6.17.2. Implementing the Transportation Constraints
6.17.3. Formulating the Model: The Side Constraints
6.17.4. Implementing the Side Constraints
6.17.5. Solving the Model
6.17.6. Analyzing the Solution
6.18. The Branch-and-Bound Algorithm (Optional)
6.18.1. Branching
6.18.2. Bounding
6.18.3. Branching Again
6.18.4. Bounding Again
6.18.5. Summary of B&B Example
6.19. Summary
6.20. References
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
7.1. Goal Programming
7.2. A Goal Programming Example
7.2.1. Defining the Decision Variables
7.2.2. Defining the Goals
7.2.3. Defining the Goal Constraints
7.2.4. Defining the Hard Constraints
7.2.5. GP Objective Functions
7.2.6. Defining the Objective
7.2.7. Implementing the Model
7.2.8. Solving the Model
7.2.9. Analyzing the Solution
7.2.10. Revising the Model
7.2.11. Trade-Offs: The Nature of GP
7.3. Comments about Goal Programming
7.4. Multiple Objective Optimization
7.5. An MOLP Example
7.5.1. Defining the Decision Variables
7.5.2. Defining the Objectives
7.5.3. Defining the Constraints
7.5.4. Implementing the Model
7.5.5. Determining Target Values for the Objectives
7.5.6. Summarizing the Target Solutions
7.5.7. Determining a GP Objective
7.5.8. The MINIMAX Objective
7.5.9. Implementing the Revised Model
7.5.10. Solving the Model
7.6. Comments on MOLP
7.7. Summary
7.8. References
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 & Evolutionary Optimization
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
8.4.1. Implementing the Model
8.4.2. Solving the Model
8.4.3. Analyzing the Solution
8.4.4. Comments on the EOQ Model
8.5. Location Problems
8.5.1. Defining the Decision Variables
8.5.2. Defining the Objective
8.5.3. Defining the Constraints
8.5.4. Implementing the Model
8.5.5. Solving the Model and Analyzing the Solution
8.5.6. Another Solution to the Problem
8.5.7. Some Comments about the Solution to Location Problems
8.6. Nonlinear Network Flow Problem
8.6.1. Defining the Decision Variables
8.6.2. Defining the Objective
8.6.3. Defining the Constraints
8.6.4. Implementing the Model
8.6.5. Solving the Model and Analyzing the Solution
8.7. Project Selection Problems
8.7.1. Defining the Decision Variables
8.7.2. Defining the Objective Function
8.7.3. Defining the Constraints
8.7.4. Implementing the Model
8.7.5. Solving the Model
8.8. Optimizing Existing Financial Spreadsheet Models
8.8.1. Implementing the Model
8.8.2. Optimizing the Spreadsheet Model
8.8.3. Analyzing the Solution
8.8.4. Comments on Optimizing Existing Spreadsheets
8.9. The Portfolio Selection Problem
8.9.1. Defining the Decision Variables
8.9.2. Defining the Objective
8.9.3. Defining the Constraints
8.9.4. Implementing the Model
8.9.5. Analyzing the Solution
8.9.6. Handling Conflicting Objectives in Portfolio Problems
8.10. Sensitivity Analysis
8.10.1. Lagrange Multipliers
8.10.2. Reduced Gradients
8.11. Solver Options for Solving NLPs
8.12. Evolutionary Algorithms
8.13. Forming Fair Teams
8.13.1. A Spreadsheet Model for the Problem
8.13.2. Solving the Model
8.13.3. Analyzing the Solution
8.14. The Traveling Salesperson Problem
8.14.1. A Spreadsheet Model for the Problem
8.14.2. Solving the Model
8.14.3. Analyzing the Solution
8.15. Summary
8.16. References
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
9.1. 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
9.9.1. The Standard Error
9.9.2. Prediction Intervals for New Values of Y
9.9.3. Confidence Intervals for Mean Values of Y
9.9.4. Extrapolation
9.10. Statistical Tests for Population Parameters
9.10.1. Analysis of Variance
9.10.2. Assumptions for the Statistical Tests
9.10.3. Statistical Tests
9.11. Introduction to Multiple Regression
9.12. A Multiple Regression Example
9.13. Selecting the Model
9.13.1. Models with One Independent Variable
9.13.2. Models with Two Independent Variables
9.13.3. Inflating R 2
9.13.4. The Adjusted- R 2 Statistic
9.13.5. The Best Model with Two Independent Variables
9.13.6. Multicollinearity
9.13.7. The Model with Three Independent Variables
9.14. Making Predictions
9.15. Binary Independent Variables
9.16. Statistical Tests for the Population Parameters
9.17. Polynomial Regression
9.17.1. Expressing Nonlinear Relationships Using Linear Models
9.17.2. Summary of Nonlinear Regression
9.18. Summary
9.19. References
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
10.1. Data Mining Overview
10.2. Classification
10.2.1. A Classification Example
10.3. Classification Data Partitioning
10.4. Discriminant Analysis
10.4.1. Discriminant Analysis Example
10.5. Logistic Regression
10.5.1. Logistic Regression Example
10.6. k-Nearest Neighbor
10.6.1. k-Nearest Neighbor Example
10.7. Classification Trees
10.7.1. Classification Tree Example
10.8. Neural Networks
10.8.1. Neural Network Example
10.9. Naïve Bayes
10.9.1. Naïve Bayes Example
10.10. Comments on Classification
10.10.1. Combining Classifications
10.10.2. The Role of Test Data
10.11. Prediction
10.12. Association Rules (Affinity Analysis)
10.12.1. Association Rules Example
10.12. Cluster Analysis
10.12.1. Cluster Analysis Example
10.12.2. k-Mean Clustering Example
10.12.3. Hierarchical Clustering Example
10.13. Time Series
10.14. Summary
10.15. References
Questions and Problems
Case 10.1. Detecting Management Fraud
Chapter 11. Time Series Forecasting
11.1. Time Series Methods
11.2. Measuring Accuracy
11.3. Stationary Models
11.4. Moving Averages
11.4.1. Forecasting with the Moving Average Model
11.5. Weighted Moving Averages
11.5.1. Forecasting with the Weighted Moving Average Model
11.6. Exponential Smoothing
11.6.1. Forecasting with the Exponential Smoothing Model
11.7. Seasonality
11.8. Stationary Data with Additive Seasonal Effects
11.8.1. Forecasting with the Model
11.9. Stationary Data with Multiplicative Seasonal Effects
11.9.1. Forecasting with the Model
11.10. Trend Models
11.10.1. An Example
11.11. Double Moving Average
11.11.1. Forecasting with the Model
11.12. Double Exponential Smoothing (Holt’s Method)
11.12.1. Forecasting with Holt’s Method
11.13. Holt-Winter’s Method for Additive Seasonal Effects
11.13.1. Forecasting with Holt-Winter’s Additive Method
11.14. Holt-Winter’s Method for Multiplicative Seasonal Effects
11.14.1. Forecasting with Holt-Winter’s Multiplicative Method
11.15. Modeling Time Series Trends Using Regression
11.16. Linear Trend Model
11.16.1. Forecasting with the Linear Trend Model
11.17. Quadratic Trend Model
11.17.1. Forecasting with the Quadratic Trend Model
11.18. Modeling Seasonality with Regression Models
11.19. Adjusting Trend Predictions with Seasonal Indices
11.19.1. Computing Seasonal Indices
11.19.2. Forecasting with Seasonal Indices
11.19.3. Refining the Seasonal Indices
11.20. Seasonal Regression Models
11.20.1. The Seasonal Model
11.20.2. Forecasting with the Seasonal Regression Model
11.21. Combining Forecasts
11.22. Summary
11.23. References
Questions and Problems
Case 11.1. PB Chemical Company
Case 11.2. Forecasting COLAs
Case 11.3. Strategic Planning at Fysco Foods
Chapter 12. Introduction to Simulation Using Analytic Solver Platform
12.1. Random Variables and Risk
12.2. Why Analyze Risk?
12.3. Methods of Risk Analysis
12.3.1. Best-Case/Worst-Case Analysis
12.3.2. What-If Analysis
12.3.3. Simulation
12.4. A Corporate Health Insurance Example
12.4.1. A Critique of the Base Case Model
12.5. Spreadsheet Simulation Using Analytic Solver Platform
12.5.1. Starting Analytic Solver Platform
12.6. Random Number Generators
12.6.1. Discrete vs. Continuous Random Variables
12.7. Preparing the Model for Simulation
12.7.1. Alternate RNG Entry
12.8. Running the Simulation
12.8.1. Selecting the Output Cells to Track
12.8.2. Selecting the Number of Replications
12.8.3. Selecting What Gets Displayed on the Worksheet
12.8.4. Running the Simulation
12.9. Data Analysis
12.9.1. The Best Case and the Worst Case
12.9.2. The Frequency Distribution of the Output Cells
12.9.3. The Cumulative Distribution of the Output Cells
12.9.4. Obtaining Other Cumulative Probabilities
12.9.5. Sensitivity Analysis
12.10. The Uncertainty of Sampling
12.10.1. Constructing a Confidence Interval for the True Population Mean
12.10.2. Constructing a Confidence Interval for a Population Proportion
12.10.3. 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
12.14.1. Implementing the Model
12.14.2. Details for Multiple Simulations
12.14.3. Running the Simulations
12.14.4. Data Analysis
12.15. An Inventory Control Example
12.15.1. Creating the RNGs
12.15.2. Implementing the Model
12.15.3. Replicating the Model
12.15.4. Optimizing the Model
12.15.5. Analyzing the Solution
12.15.6. Other Measures of Risk
12.16. A Project Selection Example
12.16.1. A Spreadsheet Model
12.16.2. Solving and Analyzing the Problem with Analytic Solver Platform
12.16.3. Considering Another Solution
12.17. A Portfolio Optimization Example
12.17.1. A Spreadsheet Model
12.17.2. Solving the Problem with Analytic Solver Platform
12.18. Summary
12.19. References
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
13.1. The Purpose of Queuing Models
13.2. Queuing System Configurations
13.3. Characteristics of Queuing Systems
13.3.1. Arrival Rate
13.3.2. Service Rate
13.4. Kendall Notation
13.5. Queuing Models
13.6. The M/M/s Model
13.6.1. An Example
13.6.2. The Current Situation
13.6.3. Adding a Server
13.6.4. Economic Analysis
13.7. The M/M/s Model with Finite Queue Length
13.7.1. The Current Situation
13.7.2. Adding a Server
13.8. The M/M/s Model with Finite Population
13.8.1. An Example
13.8.2. The Current Situation
13.8.3. Adding Servers
13.9. The M/G/1 Model
13.9.1. The Current Situation
13.9.2. 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
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
14.1. Good Decisions vs. Good Outcomes
14.2. Characteristics of Decision Problems
14.3. An Example
14.4. The Payoff Matrix
14.4.1. Decision Alternatives
14.4.2. States of Nature
14.4.3. The Payoff Values
14.5. Decision Rules
14.6. Nonprobabilistic Methods
14.6.1. The Maximax Decision Rule
14.6.2. The Maximin Decision Rule
14.6.3. The Minimax Regret Decision Rule
14.7. Probabilistic Methods
14.7.1. Expected Monetary Value
14.7.2. Expected Regret
14.7.3. Sensitivity Analysis
14.8. The Expected Value of Perfect Information
14.9. Decision Trees
14.9.1. Rolling Back a Decision Tree
14.10. Creating Decision Trees with Analytic Solver Platform
14.10.1. Adding Event Nodes
14.10.2. Determining the Payoffs and EMVs
14.10.3. Other Features
14.11. Multistage Decision Problems
14.11.1. A Multistage Decision Tree
14.11.2. Developing A Risk Profile
14.12. Sensitivity Analysis
14.12.1. Tornado Charts
14.12.2. Strategy Tables
14.12.3. Strategy Charts
14.13. Using Sample Information in Decision Making
14.13.1. Conditional Probabilities
14.13.2. The Expected Value of Sample Information
14.14. Computing Conditional Probabilities
14.14.1. Bayes’s Theorem
14.15. Utility Theory
14.15.1. Utility Functions
14.15.2. Constructing Utility Functions
14.15.3. Using Utilities to Make Decisions
14.15.4. The Exponential Utility Function
14.15.5. Incorporating Utilities in Decision Trees
14.16. Multicriteria Decision Making
14.17. The Multicriteria Scoring Model
14.18. The Analytic Hierarchy Process
14.18.1. Pairwise Comparisons
14.18.2. Normalizing the Comparisons
14.18.3. Consistency
14.18.4. Obtaining Scores for the Remaining Criteria
14.18.5. Obtaining Criterion Weights
14.18.6. Implementing the Scoring Model
14.19. Summary
14.20. References
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
15.1. An Example
15.2. Creating the Project Network
15.2.1. 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
15.6.1. A Note on Slack
15.7. Project Management Using Spreadsheets
15.7.1. Important Implementation Issue
15.8. Gantt Charts
15.9. Project Crashing
15.9.1. An LP Approach to Crashing
15.9.2. Determining the Earliest Crash Completion Time
15.9.3. Implementing the Model
15.9.4. Solving the Model
15.9.5. Determining a Least Costly Crash Schedule
15.9.6. Crashing as an MOLP
15.10. PERT: An Overview
15.10.1. The Problems with PERT
15.10.2. Implications
15.11. Simulating Project Networks
15.11.1. An Example
15.11.2. Generating Random Activity Times
15.11.3. Implementing the Model
15.11.4. Running the Simulation
15.11.5. Analyzing the Results
15.12. Microsoft Project
15.13. Summary
15.14. References
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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