Download or read book Decision Making in Design Under Uncertainty with Multiobjective Robust Design Optimization written by Sirisha Rangavajhala and published by ProQuest. This book was released on 2007 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Design Optimization Under Uncertainty written by Weifei Hu and published by Springer Nature. This book was released on 2023-12-22 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces the fundamentals of probability, statistical, and reliability concepts, the classical methods of uncertainty quantification and analytical reliability analysis, and the state-of-the-art approaches of design optimization under uncertainty (e.g., reliability-based design optimization and robust design optimization). The topics include basic concepts of probability and distributions, uncertainty quantification using probabilistic methods, classical reliability analysis methods, time-variant reliability analysis methods, fundamentals of deterministic design optimization, reliability-based design optimization, robust design optimization, other methods of design optimization under uncertainty, and engineering applications of design optimization under uncertainty.
Download or read book Bi Level Integrated System Synthesis BLISS written by Jaroslaw Sobieszczanski-Sobieski and published by . This book was released on 1998 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Robust Optimization written by Aharon Ben-Tal and published by Princeton University Press. This book was released on 2009-08-10 with total page 565 pages. Available in PDF, EPUB and Kindle. Book excerpt: Robust optimization is still a relatively new approach to optimization problems affected by uncertainty, but it has already proved so useful in real applications that it is difficult to tackle such problems today without considering this powerful methodology. Written by the principal developers of robust optimization, and describing the main achievements of a decade of research, this is the first book to provide a comprehensive and up-to-date account of the subject. Robust optimization is designed to meet some major challenges associated with uncertainty-affected optimization problems: to operate under lack of full information on the nature of uncertainty; to model the problem in a form that can be solved efficiently; and to provide guarantees about the performance of the solution. The book starts with a relatively simple treatment of uncertain linear programming, proceeding with a deep analysis of the interconnections between the construction of appropriate uncertainty sets and the classical chance constraints (probabilistic) approach. It then develops the robust optimization theory for uncertain conic quadratic and semidefinite optimization problems and dynamic (multistage) problems. The theory is supported by numerous examples and computational illustrations. An essential book for anyone working on optimization and decision making under uncertainty, Robust Optimization also makes an ideal graduate textbook on the subject.
Download or read book A Novel Approach to Robust Design Using Recent Advances in Robust and Multiobjective Optimization Methods written by Gregory Joseph and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Current advances in the fi eld of Robust Optimization (RO) from such authors as Azarm, Ben-Tal, Elishakoff , Zhang, Renaud and others have led to new and interesting approaches to the treatment of uncertainty in traditional engineering problems. This paper presents the Budget of Uncertainty (BoU) design method; a new method by which such approaches can be applied in a manner which balances the need for optimization with the desire for robust solutions. Where previous work has focused on immunizing an optimization problem against pre-set uncertainty ranges, the BoU method adds additional design variables in an eff ort to solve for an appropriate uncertainty range. The BoU method simultaneously determines an optimum solution and an allowed uncertainty budget within a restricted feasibility space. The result is a solution that guarantees fi rst order satisfaction of uncertain constraints and provides a measure of problem sensitivity to its uncertain parameters. This provides additional insight to early problem development, and can potentially create alternatives to traditional approaches such as Monte Carlo analysis. Within this work we will present a summary of current RO research and introduce the BoU method. We will then apply the BoU method to a simple 2D geometric problem to illustrate its application. Finally, we tackle two well-studied engineering design problems, the Golinksi Speed Reducer and the simple Helical Spring design problem to show a more realistic application of the new method.
Download or read book Decision Making under Deep Uncertainty written by Vincent A. W. J. Marchau and published by Springer. This book was released on 2019-04-04 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book focuses on both the theory and practice associated with the tools and approaches for decisionmaking in the face of deep uncertainty. It explores approaches and tools supporting the design of strategic plans under deep uncertainty, and their testing in the real world, including barriers and enablers for their use in practice. The book broadens traditional approaches and tools to include the analysis of actors and networks related to the problem at hand. It also shows how lessons learned in the application process can be used to improve the approaches and tools used in the design process. The book offers guidance in identifying and applying appropriate approaches and tools to design plans, as well as advice on implementing these plans in the real world. For decisionmakers and practitioners, the book includes realistic examples and practical guidelines that should help them understand what decisionmaking under deep uncertainty is and how it may be of assistance to them. Decision Making under Deep Uncertainty: From Theory to Practice is divided into four parts. Part I presents five approaches for designing strategic plans under deep uncertainty: Robust Decision Making, Dynamic Adaptive Planning, Dynamic Adaptive Policy Pathways, Info-Gap Decision Theory, and Engineering Options Analysis. Each approach is worked out in terms of its theoretical foundations, methodological steps to follow when using the approach, latest methodological insights, and challenges for improvement. In Part II, applications of each of these approaches are presented. Based on recent case studies, the practical implications of applying each approach are discussed in depth. Part III focuses on using the approaches and tools in real-world contexts, based on insights from real-world cases. Part IV contains conclusions and a synthesis of the lessons that can be drawn for designing, applying, and implementing strategic plans under deep uncertainty, as well as recommendations for future work. The publication of this book has been funded by the Radboud University, the RAND Corporation, Delft University of Technology, and Deltares.
Download or read book Uncertainty Based Ship Design Optimization written by Zuyuan Liu and published by Springer Nature. This book was released on with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Progressive Decision Making Tools and Applications in Project and Operation Management written by Mohammad Yazdi and published by Springer Nature. This book was released on with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Optimization in Practice with MATLAB written by Achille Messac and published by Cambridge University Press. This book was released on 2015-03-19 with total page 503 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optimization in Practice with MATLAB® provides a unique approach to optimization education. It is accessible to both junior and senior undergraduate and graduate students, as well as industry practitioners. It provides a strongly practical perspective that allows the student to be ready to use optimization in the workplace. It covers traditional materials, as well as important topics previously unavailable in optimization books (e.g. numerical essentials - for successful optimization). Written with both the reader and the instructor in mind, Optimization in Practice with MATLAB® provides practical applications of real-world problems using MATLAB®, with a suite of practical examples and exercises that help the students link the theoretical, the analytical, and the computational in each chapter. Additionally, supporting MATLAB® m-files are available for download via www.cambridge.org.messac. Lastly, adopting instructors will receive a comprehensive solution manual with solution codes along with lectures in PowerPoint with animations for each chapter, and the text's unique flexibility enables instructors to structure one- or two-semester courses.
Download or read book Optimization Under Uncertainty written by Giovanni Petrone and published by . This book was released on 2012 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Reliability based Structural Design written by Seung-Kyum Choi and published by Springer Science & Business Media. This book was released on 2006-11-15 with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides readers with an understanding of the fundamentals and applications of structural reliability, stochastic finite element method, reliability analysis via stochastic expansion, and optimization under uncertainty. It examines the use of stochastic expansions, including polynomial chaos expansion and Karhunen-Loeve expansion for the reliability analysis of practical engineering problems.
Download or read book Decision Making in Engineering Design written by Evangelos Papageorgiou and published by Cambridge Scholars Publishing. This book was released on 2018-12-13 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents an operational tool for decision making under uncertainty in any engineering design. It synthesizes classical decision making methods, such as multi-attribute utility theory, analytic hierarchy process with game theory and quantum decision theory. It demonstrates the implementation of the value driven design philosophy in the engineering design framework. Value, related to the designed system’s capabilities and lifecycle cost, is used to compare different alternatives through the appropriate value model. Game Theory as an optimization tool is used to successfully address the stakeholders’ preferences in a functional outcome-focused way. A Quantum-based Decision Making model is also developed to capture the complexity of human decision making related with risk attitude in the presence of ambiguity and uncertainty. Apart from rationality, the decision makers’ biases, emotions and subjective feelings are also captured in this model.
Download or read book Distributionally Robust Optimization for Design Under Partially Observable Uncertainty written by Michael George Kapteyn and published by . This book was released on 2018 with total page 83 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deciding how to represent and manage uncertainty is a vital part of designing complex systems. Widely used is a probabilistic approach: assigning a probability distribution to each uncertain parameter. However, this presents the designer with the task of assuming these probability distributions or estimating them from data, tasks which are inevitably prone to error. This thesis addresses this challenge by formulating a distributionally robust design optimization problem, and presents computationally efficient algorithms for solving the problem. In distributionally robust optimization (DRO) methods, the designer acknowledges that they are unable to exactly specify a probability distribution for the uncertain parameters, and instead specifies a so-called ambiguity set of possible distributions. This work uses an acoustic horn design problem to explore how the error incurred in estimating a probability distribution from limited data affects the realized performance of designs found using traditional approaches to optimization under uncertainty, such as multi-objective optimization. It is found that placing some importance on a risk reduction objective results in designs that are more robust to these errors, and thus have a better mean performance realized under the true distribution than if the designer were to focus all efforts on optimizing for mean performance alone. In contrast, the DRO approach is able to uncover designs that are not attainable using the multi-objective approach when given the same data. These DRO designs in some cases significantly outperform those designs found using the multi-objective approach.
Download or read book Multicriteria Decision Making Under Conditions of Uncertainty written by Petr Ekel and published by John Wiley & Sons. This book was released on 2019-12-12 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: A guide to the various models and methods to multicriteria decision-making in conditions of uncertainty presented in a systematic approach Multicriteria Decision-Making under Conditions of Uncertainty presents approaches that help to answer the fundamental questions at the center of all decision-making problems: "What to do?" and "How to do it?" The book explores methods of representing and handling diverse manifestations of the uncertainty factor and a multicriteria nature of problems that can arise in system design, planning, operation, and control. The authors—noted experts on the topic—and their book covers essential questions, including notions and fundamental concepts of fuzzy sets, models and methods of multiobjective as well as multiattribute decision-making, the classical approach to dealing with uncertainty of information and its generalization for analyzing multicriteria problems in condition of uncertainty, and more. This comprehensive book contains information on "harmonious solutions" in multiobjective problem-solving (analyzing “i>X, F> models), construction and analysis of “i>X, R/i” models, results aimed at generating robust solutions in analyzing multicriteria problems under uncertainty, and more. In addition, the book includes illustrative examples of various applications, including real-world case studies related to the authors’ various industrial projects. This important resource: Explains the design and processing aspect of fuzzy sets, including construction of membership functions, fuzzy numbers, fuzzy relations, aggregation operations, and fuzzy sets transformations Describes models of multiobjective decision-making (“i>X. M/i” models), their analysis on the basis of using the Bellman-Zadeh approach to decision-making in a fuzzy environment, and their diverse applications, including multicriteria allocation of resources Investigates models of multiattribute decision-making (“i>X, R/i” models) and their analysis on the basis of the construction and processing of fuzzy preference relations as well as demonstrating their applications to solve diverse classes of multiattribute problems Explores notions of payoff matrices and fuzzy-set-based generalization and modification of the classic approach to decision-making under conditions of uncertainty to generate robust solutions in analyzing multicriteria problems Written for students, researchers and practitioners in disciplines in which decision-making is of paramount relevance, Multicriteria Decision-Making under Conditions of Uncertainty presents a systematic and current approach that encompasses a range of models and methods as well as new applications.
Download or read book Handbook of Research on Predictive Modeling and Optimization Methods in Science and Engineering written by Kim, Dookie and published by IGI Global. This book was released on 2018-06-15 with total page 644 pages. Available in PDF, EPUB and Kindle. Book excerpt: The disciplines of science and engineering rely heavily on the forecasting of prospective constraints for concepts that have not yet been proven to exist, especially in areas such as artificial intelligence. Obtaining quality solutions to the problems presented becomes increasingly difficult due to the number of steps required to sift through the possible solutions, and the ability to solve such problems relies on the recognition of patterns and the categorization of data into specific sets. Predictive modeling and optimization methods allow unknown events to be categorized based on statistics and classifiers input by researchers. The Handbook of Research on Predictive Modeling and Optimization Methods in Science and Engineering is a critical reference source that provides comprehensive information on the use of optimization techniques and predictive models to solve real-life engineering and science problems. Through discussions on techniques such as robust design optimization, water level prediction, and the prediction of human actions, this publication identifies solutions to developing problems and new solutions for existing problems, making this publication a valuable resource for engineers, researchers, graduate students, and other professionals.
Download or read book Evolutionary Multi Criterion Optimization written by Robin Purshouse and published by Springer. This book was released on 2013-03-12 with total page 859 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 7th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2013 held in Sheffield, UK, in March 2013. The 57 revised full papers presented were carefully reviewed and selected from 98 submissions. The papers are grouped in topical sections on plenary talks; new horizons; indicator-based methods; aspects of algorithm design; pareto-based methods; hybrid MCDA; decomposition-based methods; classical MCDA; exploratory problem analysis; product and process applications; aerospace and automotive applications; further real-world applications; and under-explored challenges.
Download or read book Decision Making Under Uncertainty written by Mykel J. Kochenderfer and published by MIT Press. This book was released on 2015-07-24 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.
