Today's business environment involves design decisions with significant uncertainty. To succeed, decision-makers should replace deterministic methods with a risk-based approach that accounts for the decision maker's risk tolerance. In many problems, it is impractical to collect data because rare or one-time events are involved. Therefore, we need a methodology to model uncertainty and make choices when we have limited information. This methodology must use all available information and rely only on assumptions that are supported by evidence. This book explains theories and tools to represent uncertainty using both data and expert judgment. It teaches the reader how to make design or business decisions when there is limited information with these tools. Readers will learn a structured, risk-based approach, which is based on common sense principles, for design and business decisions. These decisions are consistent with the decision-maker's risk attitude. The book is exceptionally suited as educational material because it uses everyday language and real-life examples to elucidate concepts. It demonstrates how these concepts touch our lives through many practical examples, questions and exercises. These are designed to help students learn that first they should understand a problem and then establish a strategy for solving it, instead of using trial-and-error approaches. This volume is intended for undergraduate and graduate courses in mechanical, civil, industrial, aerospace, and ocean engineering and for researchers and professionals in these disciplines. It will also benefit managers and students in business administration who want to make good decisions with limited information.

Reliability methods are becoming increasingly popular in engineering design because they help build safer and more efficient products than traditional deterministic methods. A principal challenge in using these methods in practical design problems is to model uncertainty when little data is available and the underlying mechanism of uncertain events is unknown. There is a need for an integrated presentation of tools for modeling uncertainty and making design decisions under severe uncertainty, which bridges the gap between theory and practice for methods for design under uncertainty. This work presents and compare the most important theories for modeling uncertainty and explains what tools are most suitable for a given design problem. It illustrates how to solve practical design problems in the aerospace and automotive engineering industries with a balanced approach explaining both the theoretical foundations of methods and their application to engineering design. The numerous examples in each section will help to appreciate the importance of design under uncertainty and the theoretical developments of the methods. Readers will learn a structured, risk-based approach for design under uncertainty when limited information is available, which tools are available and which to select and apply given a design decision problem. They will further understand how to improve their overall performance using a structured, risk-based approach for design under uncertainty. Intended for mechanical and civil engineers working in aerospace, automotive, civil, shipbuilding and power engineering, and for graduate level courses and students in reliability analysis and design and decision-making under uncertainty.

Researchers in the engineering industry and academia are making important advances on reliability-based design and modeling of uncertainty when data is limited. Non deterministic approaches have enabled industries to save billions by reducing design and warranty costs and by improving quality. Considering the lack of comprehensive and definitive presentations on the subject, Engineering Design Reliability Handbook is a valuable addition to the reliability literature. It presents the perspectives of experts from the industry, national labs, and academia on non-deterministic approaches including probabilistic, interval and fuzzy sets-based methods, generalized information theory, Dempster-Shaffer evidence theory, and robust reliability. It also presents recent advances in all important fields of reliability design including modeling of uncertainty, reliability assessment of both static and dynamic components and systems, design decision making in the face of uncertainty, and reliability validation. The editors and the authors also discuss documented success stories and quantify the benefits of these approaches. With contributions from a team of respected international authors and the guidance of esteemed editors, this handbook is a distinctive addition to the acclaimed line of handbooks from CRC Press.