For Systems Engineering courses in industrial engineering departments. While being an experiment within itself to teach normative design theory, this comprehensive text treats engineering design as a decision-making process, which it is, from a quantitative point of view. This opens a host of well- developed methods to application, including a mathematically rigorous treatment of risk and uncertainty in design. Largely, these methods come from other fields, such as economics, operations research, decision theory, and game theory. The book is designed to assist the student by defining the boundaries of a discipline, providing order for the learning process, and assisting the student in self testing.

Table of Contents:
(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. A Quantitative Measure of Information. 16. System Life-Cycle Modeling and Optimization. Objective Functions of a Firm. Objective Functions of Consumers. System Optimization and Improvement. Long-Term Planning. Model Validation. The Use of Model Results in the Design Process. 17. Game Theory. The Description of a Game. Types of Games. Example Games. Information in the Context of Game Theory. Mixed Strategy Games. The Minimax Theorem. Solution of a Two-Player, Zero-Sum Game by Linear Programming. 18. Management of Engineering Systems Design and Operation. The Management of Engineering Systems Design. The Management of Engineering Systems Operation. Continuing Product Improvement. 19. Case Studies. Solution of the Bean Jar Problem. An Undersea Cable Design. 20. Concluding Remarks. Appendix A: Vectors and Matrices. Appendix B: A Test for the Utility Independence of Attributes. Appendix C: Determination of the Weighting Factors in a Linearly Additive Utility Function. Name Index. Topic Index.