Reliability Assessment and Optimization of Complex Systems delves into a range of tools and techniques for designing optimized complex systems. Each chapter explores system modeling and the implementation of various metaheuristics for optimization purposes. This book provides readers in the domain of applied mathematics with a comprehensive understanding of system reliability analysis and improvement, thereby offering substantial value to their knowledge and expertise. System reliability has become the paramount attribute of any production unit. The process of maximizing system reliability while adhering to multiple constraints is referred to as reliability optimization.There are two primary approaches to enhancing a system's performance and reliability: developing a product with reduced failures (failure avoidance) or incorporating resilience to ensure the system continues functioning even in the event of a failure (fault tolerance). - Explains the process and application of reliability-based design optimization - Covers many metaheuristic approaches such as reliability, cost, and the MTTF of the system - Provides the workings and applications of multi-objective optimizations

Complex System Reliability presents a state-of-the-art treatment of complex multi-channel system reliability assessment and provides the requisite tools, techniques and algorithms required for designing, evaluating and optimizing ultra-reliable redundant systems. Critical topics that make Complex System Reliability a unique and definitive resource include: • redundant system analysis for k-out-of-n systems (including complex systems with embedded k-out-of-n structures) involving both perfect and imperfect fault coverage; • imperfect fault coverage analysis techniques, including algorithms for assessing the reliability of redundant systems in which each element is subject to a given coverage value (element level coverage) or in which the system uses voting to avoid the effects of a failed element (fault level coverage); and • state-of-the-art binary decision diagram analysis techniques, including the latest and most efficient algorithms for the reliability assessment of large, complex redundant systems. This practical presentation includes numerous fully worked examples that provide detailed explanations of both the underlying design principles and the techniques (such as combinatorial, recursive and binary decision diagram algorithms) used to obtain quantitative results. Many of the worked examples are based on the design of modern digital fly-by-wire control system technology. Complex System Reliability provides in-depth coverage of systems subject to either perfect or imperfect fault coverage and also the most recent techniques for correctly assessing the reliability of redundant systems that use mid-value-select voting as their primary means of redundancy management. It is a valuable resource for those involved in the design and reliability assessment of highly reliable systems, particularly in the aerospace and automotive sectors.

Many real systems are composed of multi-state components with different performance levels and several failure modes. These affect the whole system's performance. Most books on reliability theory cover binary models that allow a system only to function perfectly or fail completely. "The Universal Generating Function in Reliability Analysis and Optimization" is the first book that gives a comprehensive description of the universal generating function technique and its applications in binary and multi-state system reliability analysis. Features: - an introduction to basic tools of multi-state system reliability and optimization; - applications of the universal generating function in widely used multi-state systems; - examples of the adaptation of the universal generating function to different systems in mechanical, industrial and software engineering. This monograph will be of value to anyone interested in system reliability, performance analysis and optimization in industrial, electrical and nuclear engineering.

Most books on reliability theory are devoted to traditional binary reliability models allowing for only two possible states for a system and its components: perfect functionality and complete failure. However, many real-world systems are composed of multi-state components, which have different performance levels and several failure modes with various effects on the entire system performance (degradation). Such systems are called Multi-State Systems (MSS). The examples of MSS are power systems where the component performance is characterized by the generating capacity, computer systems where the component performance is characterized by the data processing speed, communication systems, etc.This book is the first to be devoted to Multi-State System (MSS) reliability analysis and optimization. It provides a historical overview of the field, presents basic concepts of MSS, defines MSS reliability measures, and systematically describes the tools for MSS reliability assessment and optimization. Basic methods for MSS reliability assessment, such as a Boolean methods extension, basic random process methods (both Markov and semi-Markov) and universal generating function models, are systematically studied. A universal genetic algorithm optimization technique and all details of its application are described. All the methods are illustrated by numerical examples. The book also contains many examples of application of reliability assessment and optimization methods to real engineering problems.The aim of this book is to give a comprehensive, up-to-date presentation of MSS reliability theory based on modern advances in this field and provide a theoretical summary and examples of engineering applications to a variety of technical problems. From this point of view the book bridges the gap between theoretical advances and practical reliability engineering.

Availability of a system is a crucial factor for planning and optimization. The concept is more challenging for modern systems such as robots and autonomous systems consisting of a complex configuration of components. As complex systems have become global and essential in today’s society, their reliable design and the determination of their availability have turned into a very important task for managers and engineers. Reliability Models of Complex Systems for Robots and Automation offers different models and approaches for reliability evaluation and optimization of a complex autonomous system. Comprehensive fault tree analysis on the critical components of industrial robots and its integration with the reliability block diagram approach is designed in order to investigate the robot system reliability. The cost and hazard decision tree are integrated for the first time in an approach to evaluate the reliability of a complex system. Considers a complex production system composing of several autonomous robots Develops binary state reliability evaluation model for a complex system Introduces new concepts of hazard decision tree Proposes fault tree and reliability block diagram for complex robotic systems Develops stochastic process based reliability evaluation and optimization models Today’s competitive world with increasing customer demands for highly reliable products makes reliability engineering a more challenging task. Reliability analysis is one of the main tools to ensure agreed delivery deadlines which in turn maintains certainty in real tangible factors such as customer goodwill and company reputation.

Safety and Reliability of Complex Engineered Systems contains the Proceedings of the 25th European Safety and Reliability Conference, ESREL 2015, held 7-10 September 2015 in Zurich, Switzerland. It includes about 570 papers accepted for presentation at the conference. These contributions focus on theories and methods in the area of risk, safety and

In the field of engineering, optimization and decision-making have become pivotal concerns. The ever-increasing demand for data processing has given rise to issues such as extended processing times and escalated memory utilization, posing formidable obstacles across various engineering domains. Problems persist, requiring not only solutions but advancements beyond existing best practices. Creating and implementing novel heuristic algorithms is a time-intensive process, yet the imperative to do so remains strong, driven by the potential to significantly lower computational costs even with marginal improvements. This book, titled Metaheuristics Algorithm and Optimization of Engineering and Complex Systems, is a beacon of innovation in this context. It examines the critical need for inventive algorithmic solutions, exploring hyperheuristic approaches that offer solutions such as automating search spaces through integrated heuristics. Designed to cater to a broad audience, this book is a valuable resource for both novice and experienced dynamic optimization practitioners. By addressing the spectrum of theory and practice, as well as discrete versus continuous dynamic optimization, it becomes an indispensable reference in a captivating and emerging field. With a deliberate focus on inclusivity, the book is poised to benefit anyone with an interest in staying abreast of the latest developments in dynamic optimization.