This book offers a comprehensive overview of recently developed methods for assessing and optimizing system reliability. It consists of two main parts, for treating assessment methods and optimization methods, respectively.
The first part covers methods of multi-state system reliability modelling and evaluation, Markov processes, Monte Carlo simulation and uncertainty analysis. The methods considered range from piecewise-deterministic Markov processes to belief function analysis. The second part covers optimization methods of mathematical programming and evolutionary algorithms, and problems of multi-objective optimization and optimization under uncertainty. The methods of this part range from non-dominated sorting genetic algorithm to robust optimization.
The book also includes the application of the assessment and optimization methods considered on real case studies, particularly with respect to the reliability assessment and optimization of renewable energy systems, and bridges the gap between theoretical method development and engineering practice.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
Yan-Fu Li is Full Professor at the Department of Industrial Engineering and the Director of the Institute for Quality & Reliability at Tsinghua University, China. He received his Ph.D in Industrial Engineering from National University of Singapore in 2010
Enrico Zio is Full Professor at Mines-Paris, PSL University, and at the Energy Department of Politecnico di Milano, Italy. He received his Ph.D in nuclear engineering from Politecnico di Milano and in Probabilistic Risk Assessment from MIT in 1996 and 1998, respectively.
RELIABILITY ANALYSIS, SAFETY ASSESSMENT AND OPTIMIZATION
METHODS AND APPLICATIONS IN ENERGY SYSTEMS AND OTHER APPLICATIONS
This book is a comprehensive overview of the recently developed methods for assessing and optimizing system reliability and safety. It consists of two main parts, for assessment and optimization methods, respectively. The former covers multi-state system modelling and reliability evaluation, Markov processes, Monte Carlo simulation and uncertainty treatments under poor knowledge. The reviewed methods range from piecewise-deterministic Markov process to belief functions. The latter covers mathematical programs, evolutionary algorithms, multi-objective optimization and optimization under uncertainty. The reviewed methods range from non-dominated sorting genetic algorithm to robust optimization. This book also includes the applications of the assessment and optimization method on real world cases, particularly for the reliability and safety of renewable energy systems. From this point of view, the book bridges the gap between theoretical development and engineering practice.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
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Hardcover. Condizione: new. Hardcover. This book offers a comprehensive overview of recently developed methods for assessing and optimizing system reliability. It consists of two main parts, for treating assessment methods and optimization methods, respectively. The first part covers methods of multi-state system reliability modelling and evaluation, Markov processes, Monte Carlo simulation and uncertainty analysis. The methods considered range from piecewise-deterministic Markov processes to belief function analysis. The second part covers optimization methods of mathematical programming and evolutionary algorithms, and problems of multi-objective optimization and optimization under uncertainty. The methods of this part range from non-dominated sorting genetic algorithm to robust optimization. The book also includes the application of the assessment and optimization methods considered on real case studies, particularly with respect to the reliability assessment and optimization of renewable energy systems, and bridges the gap between theoretical method development and engineering practice. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9781119265870
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