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内容简介 · · · · · ·
目录 · · · · · ·
C 0 N T E N T S
Chapter l Proactive
Decision Making
Routine Decisions
The Challenges of Proactive
Decision Making
· · · · · · (更多)
Chapter l Proactive
Decision Making
Routine Decisions
The Challenges of Proactive
Decision Making
· · · · · · (更多)
C 0 N T E N T S
Chapter l Proactive
Decision Making
Routine Decisions
The Challenges of Proactive
Decision Making
Alternatives
Assumptions-Structure
Assumptions-Assessments
Performance
Summary
Chapter 2 Alternatives
Small Number of Alternatives
Sequential Decisions
A Single Decision Quantity
Two or More Decision Quantities
Decision Rules
Summary
Chapter 3 Structuring Assumptions
in Decision Making
Structuring Relationships llsing an
Influence Diagram
Structuring a Sequence of Decisions
and Uncertainties Using a
Decision Tree
Influence Diagrams with
Uncertain Quantities
Final Examples of How to Develop an
Influence Diagram
The Use oflnfluence Diagrams and
Decision Trees
Case: Destiny Consulting Group
Chapter 4 Assessment
Sensitivity Analysis
The Language of Probability
Uncertainties with a Pew
Potential Outcomes
Uncertainties with Many
Potential Outcomes
Summary Measures of
Probability Distributions
Deriving the Probability Distribution
for Performance
Summary
Chapter 5 Performance
Relevant Monetary Flows
Evaluating Alternatives
under Uncertainty
Few Potential Outcomes
Many Potential Outcomes
Summary
Chapter 6 Risk Management
Value of Information
Perfect Information
Imperfect Information
Value of Control
Perfect Control
Control ofContinuously
Ranging Quantities
Adding Value and Reducing Risk
Summary
Chapter 7 Evaluating
Multiperiod Performance
Cash Flow
An Example
Time Value of Money '
Accumulated Value
Present Value and Net Present Value
Formulas for Accumulated and Present
Value Calculations
Streams in Perpetuity
Pretax versus Aftertax Analyses
The Reinvestment Rate
Hurdle Rate
Internal Rate ot Return
Nominal versus Effective Rates
of Return
Chapter 8 Multiobjective and
Multistakeholder Choice
The Generic Choice Problem
Example
First-Rbund Eliminations
Dominance
Decision Rules without Trade-
offJudgments
The Lexicographic Rule
Satisficing
Rate and Weight: Lin"'"' Additive
Scoring Rules
Rating Alternatives
Weighting Attributes
Assumptions ofRate and Weight
Multiple Stakeholder Problems
Appendix l Comments on the
Dependence ofWeights on the
Scaling of Attributes
Exercises
Chapter 9 Risk Preference
and Utility
The Utility of
Monetary Consequences
Risk Aversion
Constant Risk Aversion: Negative
Exponential Utility
Decreasing Risk Aversion:
Logarithmic Utility
Using a Utility Curve for Risk
Analysis
Separation of Risk-Return and Mean-
Variance Analysis
Corporate Risk Policy
Exercises
Chapter lO Competitor Analysis
Characterizing Competitive
Situations
Matrix Format
Classical Structures
No (or Little) Conflict
Prisoner's Dilemma
Preemption
Summary
Chapter ll Probability
Distributions
The Language ofProbability
Distributions
The Probability Mass Function
The Cumulative Distribution
Function - .,
Continuous and Many-Valued
Uncertain Quantities
Assessment: Capturing Personal
Judgment
An Example of Assessing a
Probability Distribution
Assessment: Using Historical Data as
a Guide
Identifying Suitable Data
Using the Suitable Data as a Guide
Adjusting Data for One
Distinguishing Factor
Assessment: Appealing to Underlying
Structure
The Binomial Distribution
The Normal Distribution
The Poisson Distribution
The Exponential Distribution
Subjective Biases and Assessment
Summary
Chapter 12 Sampling
Forecasting Sample Results
Forecasting a Sample Average
Forecasting a Sample Proportion
Using Sample Results to Draw
Inferences about the Underlying
Probability Distribution
Inferences about the Mean of
the Underlying Probability
Distribution
Inferences about the
Underlying Probability
Using Sample Results to Forecast Future
Sample Results
Using Sample Results to Forecast a
Future Sample Average
Using Sample Results to Forecast a
Future Sample Proportion
Summary 198
Chapter 13 Time-Series Forecasting
Basic Approaches for
One-Period Forecasts
Simple Approaches
Moving Average
Smoothed Average
Comparison of Forecasts
Precision
Bias
Exploiting Multiperiod Patterns
Treating Seasonality
Deseasonalizing a Time Series
Forecasting the
Deseasonalized Series
Reseasonalizing the Forecast
Generating the Probability
Distribution Forecast
Decomposition of Time Series
into Seasonality and
Trend Components
Separating out Seasonality
Extrapolating Trend and
Cycle Components
Holt's Model: Exponential Smoothing
with Trend
Winter's Model: Exponential Smoothing
with Trend and Seasonality
Other Advanced Techniques
Considerations in Preparing and Using
a Forecast
Chapter 14 Regression: Forecasting
Using Explanatory Factors
The Simple Linear Model
Fitting the Model Using
"Least Squares"
Important Properties ofthe Least-Squares
Regression Line
Summary Regression Statistics
Standard Error of Estimate
Adjusted R Square
Standard Error of the Coefficients
Assumptions behind the l .inear
Regression Model
Linearity
Independence
Homoscedasticit"
Normality
Summary of
Regression Assumptions
Model-Building Philosophy
Uses ofthe Linear Model
Nature ofthe Relationship
among Variables
The Importance ofthe Underlying
Relationship to the Use of
the Model
Model-Building Procedure
Common Mistakes
Summary
Forecasting Using the Linear
Regression Model
Point Forecast
Interval Forecast
Analogy to Simole Random
Sampling
Using Dummy Variables to Represent
Categorical Variables
Example
Dummy Variables for More than
Two Groups
Useful Data Transforrmations
Example
Choosing a Transformation
Transforming the Y-Variable
Chapter 15 Discrete-Event
Simulation
An Example Application of
Discrete-Event Simulation
The Model
Important Issues in Discrete-
Event Simulation
Calibrating the Uncertainties
Validating the Model
Avoiding Peculiarities Associated with
Start-up
Terminating the Model Run
Summary
Chapter 16 Introduction to
Optimization Models
Transforming an Evaluation Model into an
Optimization Model
Example l: Optimal Order
Quantity
Example 2: Product Mix Planning
Example 3: Facility Location
Summary ofExamples
Categorizing and Solving Optimization
Models
Example l: Nonlinear Programming
Example 2: Linear Programming
Example 3: Integer Programming
Uncertainty in Optimization Models:
Sensitivity Analysis
Lagrange Multipliers
Linear Programming Models
Building an Optimization Model
from Scratch
Chapter 17 The Mathematics
of Optimization
Algebraic Framework for
Optimization Models
Functions
General Structure of an
Optimization Model
Integer Programming
Linear Programnung (LP)
Graphical Representation of
Example 2
The Simplex Algorithm
Some Final Comments on the Simplex
Algorithm and LP
Karmarkar's Algorithm: An Alternative
Approach to Solving
LP Models
Nonlinear Programming (NLP)
Levers to Control the GS
Solution Approach
Integer Programming (IP)
Final Observations: LP, NLP, and IP
Summary
Cases
Case l: American Lawbook
Corporation (A)
Case 2: American Lawbook
Corporation (B)
Case 3: Amore Prozen Foods
Case 4: Athens Glass Works
Case 5: Buckeye Power & Light
Company
Case 6: Buckeye Power & Light
Company Supplement
Case 7: California Oil Company
Case 8: C.K. Coolidge, Inc. (A)
Case 9: The Commerce Tavern
Case lO: CyberLab: A New Business
Opportunity for PRlCO (A)
Case ll: CyberLab: Supplement
Case 12: CyberLab: A New Business
Opportunity for PRlCO (B)
Case 13: Dhahran Roads (A)
Case 14 Dhahran Roads (B)
Case 15: Discounted Cash1
Flow Exercises
Case 16, Edgcomb Metals (A)
Case l7: Florida Glass Company (A)
Case 18: Florida Glass Company (A)
Supplement
Case 19: Foulke Consumer
Products, Inc.
Case 20: Foulke Consumer
Products, Inc., Supplement
Case 21: Freemark Abbey Winery
Case 22: Galaxy Micro Systems
Case 23: Galaxy Micro Systems
Supplement
Case 24: George's T-Shirts
Case 25: Harimann International
Case 26: Hightower Department Stores:
Imported Stuffed Animals
Case 27: International Guidance
and Controls
Case 28: Jade Shampoo (A)
Case 29: Jade Shampoo (B)
Case 30: Jaikumar Textiles, Ltd.'
The Nylon Division (A)
Case 31: Jaikumar Textiles, Ltd.:
The Nylon Division (B)
Case 32: Lesser Antilles Lines: The Island
of San Huberto
Case 33: Lightweight Aluminum
Company: The Lebanon Plant
Case 34: Lorex Pharmaceuticals
Case 35: Maxco, Inc., and the
Gambit Company
Case 36: The Oakland A's (A)
Case 37: The Oakland A's (A)
Supplement
Case38:TheOaklandA's(B)
Case 39: Piedmont Airlines:
Discount Seat Allocation (A)
Case 40: Piedmont Airlines:
Discount Seat Allocation (B)
Case 41: Probability Assessment
Exercise
Case 42: Problems in Regression
Case 43: Roadway Construction
Company
Case 44: Shumway, Horch, and
Sager(A)
Case 45: Shumway, Horch, and
Sager (B)
Case 46: Sleepmore Mattress
Manufacturing: Plant
Consolidation
Case 47: Sprigg Lane (A)
Case 48: T. Rowe Price Assoclates
Case 49: Wachovia Bank and Trust
Company, N.A. (B)
Case 50: Wachovia Bank and Trust
Company, N.A. (B): Supplement
Case 51: Waite First Securities
Case 52: The WaldorfProperty
· · · · · · (收起)
Chapter l Proactive
Decision Making
Routine Decisions
The Challenges of Proactive
Decision Making
Alternatives
Assumptions-Structure
Assumptions-Assessments
Performance
Summary
Chapter 2 Alternatives
Small Number of Alternatives
Sequential Decisions
A Single Decision Quantity
Two or More Decision Quantities
Decision Rules
Summary
Chapter 3 Structuring Assumptions
in Decision Making
Structuring Relationships llsing an
Influence Diagram
Structuring a Sequence of Decisions
and Uncertainties Using a
Decision Tree
Influence Diagrams with
Uncertain Quantities
Final Examples of How to Develop an
Influence Diagram
The Use oflnfluence Diagrams and
Decision Trees
Case: Destiny Consulting Group
Chapter 4 Assessment
Sensitivity Analysis
The Language of Probability
Uncertainties with a Pew
Potential Outcomes
Uncertainties with Many
Potential Outcomes
Summary Measures of
Probability Distributions
Deriving the Probability Distribution
for Performance
Summary
Chapter 5 Performance
Relevant Monetary Flows
Evaluating Alternatives
under Uncertainty
Few Potential Outcomes
Many Potential Outcomes
Summary
Chapter 6 Risk Management
Value of Information
Perfect Information
Imperfect Information
Value of Control
Perfect Control
Control ofContinuously
Ranging Quantities
Adding Value and Reducing Risk
Summary
Chapter 7 Evaluating
Multiperiod Performance
Cash Flow
An Example
Time Value of Money '
Accumulated Value
Present Value and Net Present Value
Formulas for Accumulated and Present
Value Calculations
Streams in Perpetuity
Pretax versus Aftertax Analyses
The Reinvestment Rate
Hurdle Rate
Internal Rate ot Return
Nominal versus Effective Rates
of Return
Chapter 8 Multiobjective and
Multistakeholder Choice
The Generic Choice Problem
Example
First-Rbund Eliminations
Dominance
Decision Rules without Trade-
offJudgments
The Lexicographic Rule
Satisficing
Rate and Weight: Lin"'"' Additive
Scoring Rules
Rating Alternatives
Weighting Attributes
Assumptions ofRate and Weight
Multiple Stakeholder Problems
Appendix l Comments on the
Dependence ofWeights on the
Scaling of Attributes
Exercises
Chapter 9 Risk Preference
and Utility
The Utility of
Monetary Consequences
Risk Aversion
Constant Risk Aversion: Negative
Exponential Utility
Decreasing Risk Aversion:
Logarithmic Utility
Using a Utility Curve for Risk
Analysis
Separation of Risk-Return and Mean-
Variance Analysis
Corporate Risk Policy
Exercises
Chapter lO Competitor Analysis
Characterizing Competitive
Situations
Matrix Format
Classical Structures
No (or Little) Conflict
Prisoner's Dilemma
Preemption
Summary
Chapter ll Probability
Distributions
The Language ofProbability
Distributions
The Probability Mass Function
The Cumulative Distribution
Function - .,
Continuous and Many-Valued
Uncertain Quantities
Assessment: Capturing Personal
Judgment
An Example of Assessing a
Probability Distribution
Assessment: Using Historical Data as
a Guide
Identifying Suitable Data
Using the Suitable Data as a Guide
Adjusting Data for One
Distinguishing Factor
Assessment: Appealing to Underlying
Structure
The Binomial Distribution
The Normal Distribution
The Poisson Distribution
The Exponential Distribution
Subjective Biases and Assessment
Summary
Chapter 12 Sampling
Forecasting Sample Results
Forecasting a Sample Average
Forecasting a Sample Proportion
Using Sample Results to Draw
Inferences about the Underlying
Probability Distribution
Inferences about the Mean of
the Underlying Probability
Distribution
Inferences about the
Underlying Probability
Using Sample Results to Forecast Future
Sample Results
Using Sample Results to Forecast a
Future Sample Average
Using Sample Results to Forecast a
Future Sample Proportion
Summary 198
Chapter 13 Time-Series Forecasting
Basic Approaches for
One-Period Forecasts
Simple Approaches
Moving Average
Smoothed Average
Comparison of Forecasts
Precision
Bias
Exploiting Multiperiod Patterns
Treating Seasonality
Deseasonalizing a Time Series
Forecasting the
Deseasonalized Series
Reseasonalizing the Forecast
Generating the Probability
Distribution Forecast
Decomposition of Time Series
into Seasonality and
Trend Components
Separating out Seasonality
Extrapolating Trend and
Cycle Components
Holt's Model: Exponential Smoothing
with Trend
Winter's Model: Exponential Smoothing
with Trend and Seasonality
Other Advanced Techniques
Considerations in Preparing and Using
a Forecast
Chapter 14 Regression: Forecasting
Using Explanatory Factors
The Simple Linear Model
Fitting the Model Using
"Least Squares"
Important Properties ofthe Least-Squares
Regression Line
Summary Regression Statistics
Standard Error of Estimate
Adjusted R Square
Standard Error of the Coefficients
Assumptions behind the l .inear
Regression Model
Linearity
Independence
Homoscedasticit"
Normality
Summary of
Regression Assumptions
Model-Building Philosophy
Uses ofthe Linear Model
Nature ofthe Relationship
among Variables
The Importance ofthe Underlying
Relationship to the Use of
the Model
Model-Building Procedure
Common Mistakes
Summary
Forecasting Using the Linear
Regression Model
Point Forecast
Interval Forecast
Analogy to Simole Random
Sampling
Using Dummy Variables to Represent
Categorical Variables
Example
Dummy Variables for More than
Two Groups
Useful Data Transforrmations
Example
Choosing a Transformation
Transforming the Y-Variable
Chapter 15 Discrete-Event
Simulation
An Example Application of
Discrete-Event Simulation
The Model
Important Issues in Discrete-
Event Simulation
Calibrating the Uncertainties
Validating the Model
Avoiding Peculiarities Associated with
Start-up
Terminating the Model Run
Summary
Chapter 16 Introduction to
Optimization Models
Transforming an Evaluation Model into an
Optimization Model
Example l: Optimal Order
Quantity
Example 2: Product Mix Planning
Example 3: Facility Location
Summary ofExamples
Categorizing and Solving Optimization
Models
Example l: Nonlinear Programming
Example 2: Linear Programming
Example 3: Integer Programming
Uncertainty in Optimization Models:
Sensitivity Analysis
Lagrange Multipliers
Linear Programming Models
Building an Optimization Model
from Scratch
Chapter 17 The Mathematics
of Optimization
Algebraic Framework for
Optimization Models
Functions
General Structure of an
Optimization Model
Integer Programming
Linear Programnung (LP)
Graphical Representation of
Example 2
The Simplex Algorithm
Some Final Comments on the Simplex
Algorithm and LP
Karmarkar's Algorithm: An Alternative
Approach to Solving
LP Models
Nonlinear Programming (NLP)
Levers to Control the GS
Solution Approach
Integer Programming (IP)
Final Observations: LP, NLP, and IP
Summary
Cases
Case l: American Lawbook
Corporation (A)
Case 2: American Lawbook
Corporation (B)
Case 3: Amore Prozen Foods
Case 4: Athens Glass Works
Case 5: Buckeye Power & Light
Company
Case 6: Buckeye Power & Light
Company Supplement
Case 7: California Oil Company
Case 8: C.K. Coolidge, Inc. (A)
Case 9: The Commerce Tavern
Case lO: CyberLab: A New Business
Opportunity for PRlCO (A)
Case ll: CyberLab: Supplement
Case 12: CyberLab: A New Business
Opportunity for PRlCO (B)
Case 13: Dhahran Roads (A)
Case 14 Dhahran Roads (B)
Case 15: Discounted Cash1
Flow Exercises
Case 16, Edgcomb Metals (A)
Case l7: Florida Glass Company (A)
Case 18: Florida Glass Company (A)
Supplement
Case 19: Foulke Consumer
Products, Inc.
Case 20: Foulke Consumer
Products, Inc., Supplement
Case 21: Freemark Abbey Winery
Case 22: Galaxy Micro Systems
Case 23: Galaxy Micro Systems
Supplement
Case 24: George's T-Shirts
Case 25: Harimann International
Case 26: Hightower Department Stores:
Imported Stuffed Animals
Case 27: International Guidance
and Controls
Case 28: Jade Shampoo (A)
Case 29: Jade Shampoo (B)
Case 30: Jaikumar Textiles, Ltd.'
The Nylon Division (A)
Case 31: Jaikumar Textiles, Ltd.:
The Nylon Division (B)
Case 32: Lesser Antilles Lines: The Island
of San Huberto
Case 33: Lightweight Aluminum
Company: The Lebanon Plant
Case 34: Lorex Pharmaceuticals
Case 35: Maxco, Inc., and the
Gambit Company
Case 36: The Oakland A's (A)
Case 37: The Oakland A's (A)
Supplement
Case38:TheOaklandA's(B)
Case 39: Piedmont Airlines:
Discount Seat Allocation (A)
Case 40: Piedmont Airlines:
Discount Seat Allocation (B)
Case 41: Probability Assessment
Exercise
Case 42: Problems in Regression
Case 43: Roadway Construction
Company
Case 44: Shumway, Horch, and
Sager(A)
Case 45: Shumway, Horch, and
Sager (B)
Case 46: Sleepmore Mattress
Manufacturing: Plant
Consolidation
Case 47: Sprigg Lane (A)
Case 48: T. Rowe Price Assoclates
Case 49: Wachovia Bank and Trust
Company, N.A. (B)
Case 50: Wachovia Bank and Trust
Company, N.A. (B): Supplement
Case 51: Waite First Securities
Case 52: The WaldorfProperty
· · · · · · (收起)
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