Efnisyfirlit

• 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

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