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• COVER
• TITLE PAGE
• PREFACE
• ABOUT THE AUTHORS
• 1 INTRODUCTION
• 1.1 MODELS AND MODELING
• 1.2 THE ROLE OF SPREADSHEETS
• 1.3 THE REAL WORLD AND THE MODEL WORLD
• 1.4 LESSONS FROM EXPERT AND NOVICE MODELERS
• 1.5 ORGANIZATION OF THE BOOK
• 1.6 SUMMARY
• SUGGESTED READINGS
• 2 MODELING IN A PROBLEM‐SOLVING FRAMEWORK
• 2.1 INTRODUCTION
• 2.2 THE PROBLEM-SOLVING PROCESS
• 2.3 INFLUENCE CHARTS
• 2.4 CRAFT SKILLS FOR MODELING
• 2.5 SUMMARY
• SUGGESTED READINGS
• EXERCISES
• 3 SPREADSHEET ENGINEERING
• 3.1 INTRODUCTION
• 3.2 DESIGNING A SPREADSHEET
• 3.3 DESIGNING A WORKBOOK
• 3.4 BUILDING A WORKBOOK
• 3.5 TESTING A WORKBOOK
• 3.6 SUMMARY
• SUGGESTED READINGS
• EXERCISES
• 4 ANALYSIS USING SPREADSHEETS
• 4.1 INTRODUCTION
• 4.2 BASE-CASE ANALYSIS
• 4.3 WHAT-IF ANALYSIS
• 4.4 BREAKEVEN ANALYSIS
• 4.5 OPTIMIZATION ANALYSIS
• 4.6 SIMULATION AND RISK ANALYSIS
• 4.7 SUMMARY
• EXERCISES
• 5 DATA EXPLORATION AND PREPARATION
• 5.1 INTRODUCTION
• 5.2 DATASET STRUCTURE
• 5.3 TYPES OF DATA
• 5.4 DATA EXPLORATION
• 5.5 DATA PREPARATION
• 5.6 SUMMARY
• SUGGESTED READINGS
• EXERCISES
• 6 CLASSIFICATION AND PREDICTION METHODS
• 6.1 INTRODUCTION
• 6.2 PRELIMINARIES
• 6.3 CLASSIFICATION AND PREDICTION TREES
• 6.4 ADDITIONAL ALGORITHMS FOR CLASSIFICATION
• 6.5 ADDITIONAL ALGORITHMS FOR PREDICTION
• 6.6 STRENGTHS AND WEAKNESSES OF ALGORITHMS
• 6.7 PRACTICAL ADVICE
• 6.8 SUMMARY
• SUGGESTED READINGS
• EXERCISES
• 7 SHORT-TERM FORECASTING
• 7.1 INTRODUCTION
• 7.2 FORECASTING WITH TIME-SERIES MODELS
• 7.3 THE EXPONENTIAL SMOOTHING MODEL
• 7.4 EXPONENTIAL SMOOTHING WITH A TREND
• 7.5 EXPONENTIAL SMOOTHING WITH TREND AND CYCLICAL FACTORS
• 7.6 USING XLMiner FOR SHORT-TERM FORECASTING
• 7.7 SUMMARY
• SUGGESTED READINGS
• EXERCISES
• 8 NONLINEAR OPTIMIZATION
• 8.1 INTRODUCTION
• 8.2 AN OPTIMIZATION EXAMPLE
• 8.3 BUILDING MODELS FOR SOLVER
• 8.4 MODEL CLASSIFICATION AND THE NONLINEAR SOLVER
• 8.5 NONLINEAR PROGRAMMING EXAMPLES
• 8.6 SENSITIVITY ANALYSIS FOR NONLINEAR PROGRAMS
• 8.7* THE PORTFOLIO OPTIMIZATION MODEL
• 8.8 SUMMARY
• SUGGESTED READINGS
• EXERCISES
• 9 LINEAR OPTIMIZATION
• 9.1 INTRODUCTION
• 9.2 ALLOCATION MODELS
• 9.3 COVERING MODELS
• 9.4 BLENDING MODELS
• 9.5 SENSITIVITY ANALYSIS FOR LINEAR PROGRAMS
• 9.6 PATTERNS IN LINEAR PROGRAMMING SOLUTIONS
• 9.7* DATA ENVELOPMENT ANALYSIS
• 9.8 SUMMARY
• SUGGESTED READINGS
• EXERCISES
• APPENDIX 9.1 THE SOLVER SENSITIVITY REPORT
• 10 OPTIMIZATION OF NETWORK MODELS
• 10.1 INTRODUCTION
• 10.2 THE TRANSPORTATION MODEL
• 10.3 ASSIGNMENT MODEL
• 10.4 THE TRANSSHIPMENT MODEL
• 10.5 A STANDARD FORM FOR NETWORK MODELS
• 10.6 NETWORK MODELS WITH YIELDS
• 10.7 NETWORK MODELS FOR PROCESS TECHNOLOGIES
• 10.8 SUMMARY
• EXERCISES
• 11 INTEGER OPTIMIZATION
• 11.1 INTRODUCTION
• 11.2 INTEGER VARIABLES AND THE INTEGER SOLVER
• 11.3 BINARY VARIABLES AND BINARY CHOICE MODELS
• 11.4 BINARY VARIABLES AND LOGICAL RELATIONSHIPS
• 11.5 THE FACILITY LOCATION MODEL
• 11.6 SUMMARY
• SUGGESTED READINGS
• EXERCISES
• 12 OPTIMIZATION OF NONSMOOTH MODELS
• 12.1 INTRODUCTION
• 12.2 FEATURES OF THE EVOLUTIONARY SOLVER
• 12.3 CURVE FITTING (REVISITED)
• 12.4 THE ADVERTISING BUDGET PROBLEM (REVISITED)
• 12.5 THE CAPITAL BUDGETING PROBLEM (REVISITED)
• 12.6 THE FIXED COST PROBLEM (REVISITED)
• 12.7 THE MACHINE-SEQUENCING PROBLEM
• 12.8 THE TRAVELING SALESPERSON PROBLEM
• 12.9 GROUP ASSIGNMENT
• 12.10 SUMMARY
• EXERCISES
• 13 DECISION ANALYSIS
• 13.1 INTRODUCTION
• 13.2 PAYOFF TABLES AND DECISION CRITERIA
• 13.3 USING TREES TO MODEL DECISIONS
• 13.4 USING DECISION TREE SOFTWARE
• 13.5* MAXIMIZING EXPECTED UTILITY WITH DECISION TREE
• 13.6 SUMMARY
• SUGGESTED READINGS
• EXERCISES
• 14 MONTE CARLO SIMULATION
• 14.1 INTRODUCTION
• 14.2 A SIMPLE ILLUSTRATION
• 14.3 THE SIMULATION PROCESS
• 14.4 CORPORATE VALUATION USING SIMULATION
• 14.5 OPTION PRICING USING SIMULATION
• 14.6 SELECTING UNCERTAIN PARAMETERS
• 14.7 SELECTING PROBABILITY DISTRIBUTIONS
• 14.8 ENSURING PRECISION IN OUTPUTS
• 14.9 INTERPRETING SIMULATION OUTCOMES
• 14.10 WHEN TO SIMULATE AND WHEN NOT TO SIMULATE
• 14.11 SUMMARY
• SUGGESTED READINGS
• EXERCISES
• 15 OPTIMIZATION IN SIMULATION
• 15.1 INTRODUCTION
• 15.2 OPTIMIZATION WITH ONE OR TWO DECISION VARIABLES
• 15.3 STOCHASTIC OPTIMIZATION
• 15.4 CHANCE CONSTRAINTS
• 15.5 TWO‐STAGE PROBLEMS WITH RECOURSE
• 15.6 SUMMARY
• SUGGESTED READINGS
• EXERCISES
• MODELING CASES
• APPENDIX 1: BASIC EXCEL SKILLS
• INTRODUCTION
• EXCEL PREREQUISITES
• THE EXCEL WINDOW
• CONFIGURING EXCEL
• MANIPULATING WINDOWS AND SHEETS
• NAVIGATION
• SELECTING CELLS
• ENTERING TEXT AND DATA
• EDITING CELLS
• FORMATTING
• BASIC FORMULAS
• BASIC FUNCTIONS
• CHARTING
• PRINTING
• HELP OPTIONS
• KEYBOARD SHORTCUTS
• CELL COMMENTS
• NAMING CELLS AND RANGES
• SOME ADVANCED TOOLS
• APPENDIX 2: MACROS AND VBA
• INTRODUCTION
• RECORDING A MACRO
• EDITING A MACRO
• CREATING A USER-DEFINED FUNCTION
• SUGGESTED READINGS
• APPENDIX 3: BASIC PROBABILITY CONCEPTS
• INTRODUCTION
• PROBABILITY DISTRIBUTIONS
• EXAMPLES OF DISCRETE DISTRIBUTIONS
• EXAMPLES OF CONTINUOUS DISTRIBUTIONS
• EXPECTED VALUES
• CUMULATIVE DISTRIBUTION FUNCTIONS
• TAIL PROBABILITIES
• VARIABILITY
• SAMPLING
• INDEX
• END USER LICENSE AGREEMENT