Description: (NOTE:Each chapter begins with The Purpose of This Chapter and concludes with Summary, References, and Problems.) Foreword. 1. Introduction to Engineering Design and Decision Making. The Engineering Process. Historical Perspective. The Decision-Making Process. Beans in a Jar. 2. Design Options. The Role of Options in Decision Making. System Objectives. Options in Physical Design. System Manufacture and Deployment. Options in System Operation. End-of-Life Options. Paring the Option Space. 3. Fundamentals of Probability Theory. The Concept of Probability. Properties of a Random Variable. The Mathematics of Random Variables. Some Important Distributions. Bayes' Formula. Hypothesis Testing. 4. Monte Carlo Modeling. The Notion of a Monte Carlo Model. Random Number Generators. Sampling a Distribution. Formulating Monte Carlo Models. Analysis of Monte Carlo Results. An Alternative Analysis of Monte Carlo Results. Monte Carlo Models with Embedded Decisions. 5. Optimization. The Need for Optimization in Systems Engineering. Notions of Minimum and Maximum. Maximization of a Function. Search Methods. Constrained Optimization. Linear Programming. Integer Programming. Network Flow Optimization. The Transportation Problem. Calculus of Variations. 6. Engineering Microeconomics. The Concept of Preference. Equilibrium Economics. Discounting and Present Value. Interest and Annuities. Inflation and Deflation. The Value of a Forecast. Resource Economics. Shadow Prices. 7. Utility Theory. Rationality. The Notion of Utility. Multiattribute Utility Functions. Arrow's Impossibility theorem. Pareto Optimality. Decision Making in the Presence of Risk. The Value of Better Information. The Value of Improved Safety. 8. Forecasting. Types of Forecasts. Regression Analysis. Logistic Curves. Forecasting Uncertainty. Forecasting Uncertainty with Embedded Decisions. Shortcoming of Forecasts. 9. Engineering Systems Modeling. Engineering Systems and System Models. The Model-Building Process. Sources of Error in Symbolic Models. Design Models. System Life Cycle Modeling. Objective Functions. 10. Analysis of System Reliability. Notions of System Reliability. System Reliability Diagrams. Cartoons and Scenarios. Decision and Event Trees. Two Different Monte Carlo Approaches. Probabilistic Risk Assessment. An Alternative to Probabilistic Risk Assessment. 11. Cost and Benefit Analysis. Costs. Modeling System Cost. Probabilistic Cost Analysis. Benefits. Probabilistic Benefit Analysis. Benefit-Cost Analysis. Project Selection. 12. Methods of Decision Analysis. Decisions and Decision Analysis. Backward Induction. Expected Utility Analysis. The Use of Decision/Event Trees with an Infinite Number of Possible Outcomes. Confidence Level Decision Making. Minimax Decision Making. Regret. Bayes Solutions. 13. State Transition Matrix Models. The State Transition Matrix Model. Example Problems. Including Uncertainty in State Transition Matrix Models. 14. Modeling the Research and Development Process. Attributes of Research and Development Activities. A Simulation Approach to Evaluating Research and Development Activities. Summary of the Simulation Approach. 15. Information.

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