Evolutionary Learning In Strategy Project Systems

WHAT DOES A SUCCESSFUL KNOWLEDGE MANAGEMENT PRACTITIONER DO?Evolutionary Learning in Strategy-Project Systems explores the gap between the theory and practice of knowledge management in organizations and analyzes how learning happens and how knowledge is created. The authors take a practitioner-driven approach, one that unites organizational strategy with the learning of organizational lessons—the kind of knowledge management that enhances project performance and ultimately business success.Through a survey of the literature and an analysis of original case-study research, Evolutionary Learning in Strategy-Project Systems develops a model of learning capability that proceeds exactly as its title implies, not as a line, but as a cycle—from codifying individual knowledge and putting it into practice within a context that values social relationships and networks. The conclusions offered in this book build on the rethinking of project management literature in today’s world—creating a strategy-project learning model that not only improves current knowledge capabilities, but also develops new ones.

What does a successful knowledge management practitioner do? Evolutionary Learning in Strategy-Project Systems explores the gap between the theory and practice of knowledge management in organizations and analyzes how learning happens and how knowledge is created. The authors take a practitioner-driven approach, one that unites organizational strategy with the learning of organizational lessons -- the kind of knowledge management that enhances project performance and ultimately business success. Through a survey of the literature and an analysis of original case-study research, Evolutionary Learning in Strategy-Project Systems develops a model of learning capability that proceeds exactly as its title implies, not as a line, but as a cycle -- from codifying individual knowledge and putting it into practice within a context that values social relationships and networks. The conclusions offered in this book build on the rethinking of project management literature in today's world -- creating a strategy-project learning model that not only improves current knowledge capabilities, but also develops new ones

This book constitutes the refereed proceedings of the 6th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2008, held in Naples, Italy, in March 2008 colocated with the Evo* 2008 events. The 18 revised full papers were carefully reviewed and selected from 63 submissions. EvoBio is the premiere European event for experts in computer science meeting with experts in bioinformatics and the biological sciences, all interested in the interface between evolutionary computation, machine learning, data mining, bioinformatics, and computational biology. Topics addressed by the papers include biomarker discovery, cell simulation and modeling, ecological modeling, uxomics, gene networks, biotechnology, metabolomics, microarray analysis, phylogenetics, protein interactions, proteomics, sequence analysis and alignment, as well as systems biology.

This book consists of eight chapters, authored by distinguished researchers and practitioners, that highlight the state of the art and recent trends in addressing the project portfolio selection and scheduling problem (PPSSP) across a variety of domains, particularly defense, social programs, supply chains, and finance. Many organizations face the challenge of selecting and scheduling a subset of available projects subject to various resource and operational constraints. In the simplest scenario, the primary objective for an organization is to maximize the value added through funding and implementing a portfolio of projects, subject to the available budget. However, there are other major difficulties that are often associated with this problem such as qualitative project benefits, multiple conflicting objectives, complex project interdependencies, workforce and manufacturing constraints, and deep uncertainty regarding project costs, benefits, and completion times. It is well known that the PPSSP is an NP-hard problem and, thus, there is no known polynomial-time algorithm for this problem. Despite the complexity associated with solving the PPSSP, many traditional approaches to this problem make use of exact solvers. While exact solvers provide definitive optimal solutions, they quickly become prohibitively expensive in terms of computation time when the problem size is increased. In contrast, evolutionary and memetic computing afford the capability for autonomous heuristic approaches and expert knowledge to be combined and thereby provide an efficient means for high-quality approximation solutions to be attained. As such, these approaches can provide near real-time decision support information for portfolio design that can be used to augment and improve existing human-centric strategic decision-making processes. This edited book provides the reader with a broad overview of the PPSSP, its associated challenges, and approaches to addressing the problem using evolutionary and memetic computing.

With an emphasis on global advantage, the text offers a comprehensive examination of regional and international issues to provide a complete, accurate and up-to-date explanation of the strategic management process. New coverage on environmental concerns and emerging technologies as well as examples and cases from Australia, New Zealand and Asia-Pacific serve to engage students while updated international content demonstrates how strategic management is used in the global economy. The text takes a 'resource-based' approach, which requires the examining of a firm's unique bundling of its internal resources. This text is appropriate for upper-level undergrad, usually third year; post grad in Masters courses.

This book highlights the innovative applications of electromagnetics, optics, thermodynamics theories in creating methods for physical-layer collision prevention- "physical anti-collision" in radio frequency identification (RFID) systems. Using engineering mathematical methods as the core of detection and control algorithm design, it proposes semi-physical verification and detection techniques to the dynamic performance testing in RFID systems. The book also introduces the methods to build semi-physical hardware platforms using photoelectric sensing technology. The book provides valuable ideas to the applications of Internet of Things (IOT) systems in smart logistics, car networking, food traceability, anti-counterfeiting and other livelihood fields. It is worth reading for all researchers in IOT and optoelectronic engineering related industries.

What is the true source of a firm's long-term competitive advantage in manufacturing? Through original field studies, historical research, and statistical analyses, this book shows how Toyota Motor Corporation, one of the world's largest automobile companies, built distinctive capabilities in production, product development, and supplier management. Fujimoto asserts that it is Toyota's evolutionary learning capability that gives the company its advantage and demonstrates how this learning is put to use in daily work.

The first complete overview of evolutionary computing, the collective name for a range of problem-solving techniques based on principles of biological evolution, such as natural selection and genetic inheritance. The text is aimed directly at lecturers and graduate and undergraduate students. It is also meant for those who wish to apply evolutionary computing to a particular problem or within a given application area. The book contains quick-reference information on the current state-of-the-art in a wide range of related topics, so it is of interest not just to evolutionary computing specialists but to researchers working in other fields.