Knowledge Unplugged announces the results of a major survey of knowledge management practice within the most influential companies in the world, by the most influential management consultancy group in the world. The McKinsey Knowledge Management team interviewed top executives and also investigated how far their plans were implemented in practice, in 40 companies in the US, Europe and Japan. In many companies they discovered a significant gap between the vision at the top and the reality on the shop floor. Knowledge Unplugged draws together their findings and presents a practical guide to improving knowledge building and sharing at all levels within an organization, vividly illustrated with case studies of best practice and common pitfalls. They argue that knowledge management is much more than simply installing a new database and can only be successful when it is at the heart of everyday personal exchanges, personal incentives and personal responsibilities at every level of the firm.

Until recently most observers were of the opinion that firms had to adopt a Japanese model of management or perish. They overlooked the fact that there are a number of efficient productive models and that there is no single 'best way'. This book shows the diversity of productive models and discusses the optimum macro and micro economic and social conditions that a firm needs to stay profitable. In conclusion the authors suggest an analytical framework of profitability conditions, easily accessible to practitioners, academics and students.

This book explains the notational system NUSAP (Numeral, Unit, Spread, Assessment, Pedigree) and applies it to several examples from the environmental sciences. The authors are now making further extensions of NUSAP, including an algorithm for the propagation of quality-grades through models used in risk and safety studies. They are also developing the concept of Post-normal Science', in which quality assurance of information requires the participation of extended peer-communities' lying outside the traditional expertise.

The subject theory is important in finance, economics, investment strategies, health sciences, environment, industrial engineering, etc.

This edited book focuses on recent developments in Dynamic Network Modeling, including aspects of route guidance and traffic control as they relate to transportation systems and other complex infrastructure networks. Dynamic Network Modeling is generally understood to be the mathematical modeling of time-varying vehicular flows on networks in a fashion that is consistent with established traffic flow theory and travel demand theory. Dynamic Network Modeling as a field has grown over the last thirty years, with contributions from various scholars all over the field. The basic problem which many scholars in this area have focused on is related to the analysis and prediction of traffic flows satisfying notions of equilibrium when flows are changing over time. In addition, recent research has also focused on integrating dynamic equilibrium with traffic control and other mechanism designs such as congestion pricing and network design. Recently, advances in sensor deployment, availability of GPS-enabled vehicular data and social media data have rapidly contributed to better understanding and estimating the traffic network states and have contributed to new research problems which advance previous models in dynamic modeling. A recent National Science Foundation workshop on "Dynamic Route Guidance and Traffic Control" was organized in June 2010 at Rutgers University by Prof. Kaan Ozbay, Prof. Satish Ukkusuri , Prof. Hani Nassif, and Professor Pushkin Kachroo. This workshop brought together experts in this area from universities, industry and federal/state agencies to present recent findings in this area. Various topics were presented at the workshop including dynamic traffic assignment, traffic flow modeling, network control, complex systems, mobile sensor deployment, intelligent traffic systems and data collection issues. This book is motivated by the research presented at this workshop and the discussions that followed.

Risk management is often complicated by situational uncertainties and the subjective preferences of decision makers. Fuzzy Hierarchical Model for Risk Assessment introduces a fuzzy-based hierarchical approach to solve risk management problems considering both qualitative and quantitative criteria to tackle imprecise information. This approach is illustrated through number of case studies using examples from the food, fashion and electronics sectors to cover a range of applications including supply chain management, green product design and green initiatives. These practical examples explore how this method can be adapted and fine tuned to fit other industries as well. Supported by an extensive literature review, Fuzzy Hierarchical Model for Risk Assessment comprehensively introduces a new method for project managers across all industries as well as researchers in risk management. this area.

The last two decades increasingly have challenged the field of management by confronting it with rapidly growing levels of dynamism, inter-connectedness, and complexity. Systems-based management approaches, their promise already proven, offer great potentials for influencing and coping with this development. This collection of essays offers ideas and exemplary case studies from experts in systemic management, organiza-tional cybernetics, and system dynamics for meeting the challenges in so-cio-economic systems. This book was compiled to honor the academic achievement of Markus Schwaninger, a leading protagonist in developing the field of systemic management and organizational cybernetics. His stature in the field is demonstrated in the forewords by Raul Espejo and John Sterman. The efforts of 18 researchers and practitioners, all closely related to Markus Schwaninger, offer conceptual and empirical approaches that will allow managers and advanced students of the management profession to analyze, understand, and design intelligent organizations. The book weaves its content from both theory and practice and offers hints for improving a variety of organizations, both private and public, profit and non-profit, and large and small.

Planning, operating, and policy making in the electric utility and natural gas sectors involves important trade-offs among economic, social, and environmental criteria. These trade-offs figure prominently in ongoing debates about how to meet growing energy demands and how to restructure the world's power industry. Energy Decisions and the Environment: A Guide to the Use of Multicriteria Methods reviews practical tools for multicriteria (also called multiobjective) decision analysis that can be used to quantify trade-offs and contribute to more consistent, informed, and transparent decision making. These methods are designed to generate and effectively communicate information about trade-offs; to help people form, articulate, and apply value judgments in decision making; and to promote effective negotiation among stakeholders with competing interests. Energy Decisions and the Environment: A Guide to the Use of Multicriteria Methods includes explanations of a wide range of methods, tutorial applications that readers can duplicate, a detailed review of energy-environment applications, and three in-depth case studies.

This valuable source for graduate students and researchers provides a comprehensive introduction to current theories and applications in optimization methods and network models. Contributions to this book are focused on new efficient algorithms and rigorous mathematical theories, which can be used to optimize and analyze mathematical graph structures with massive size and high density induced by natural or artificial complex networks. Applications to social networks, power transmission grids, telecommunication networks, stock market networks, and human brain networks are presented. Chapters in this book cover the following topics: Linear max min fairness Heuristic approaches for high-quality solutions Efficient approaches for complex multi-criteria optimization problems Comparison of heuristic algorithms New heuristic iterative local search Power in network structures Clustering nodes in random graphs Power transmission grid structure Network decomposition problems Homogeneity hypothesis testing Network analysis of international migration Social networks with node attributes Testing hypothesis on degree distribution in the market graphs Machine learning applications to human brain network studies This proceeding is a result of The 6th International Conference on Network Analysis held at the Higher School of Economics, Nizhny Novgorod in May 2016. The conference brought together scientists and engineers from industry, government, and academia to discuss the links between network analysis and a variety of fields.

This book develops an innovative system, in the form of an "app", that harnesses the power of the internet to predict which sorts of people will prefer which policy in ANY planning situation. It chronicles the accumulated research wisdom behind the system's reasoning, along with several less successful approaches to policy making that have been found wanting in the past - including the myth, usually peddled by strategic planners, that it is possible to find a "best" plan which optimally satisfies everybody. The book lays out an entirely new kind of Planning Support System (PSS). It will facilitate decision-making that is far more community-sensitive than previously, and it will drastically improve the performance of anyone who needs to plan within socially-sensitive contexts - which is all of us. A standout feature of the system is its commitment to "scientific rigour", as shown by its predicted plan scores always being graphically presented within error margins so that true statistical significance is instantly observable. Moreover, the probabilities that its predictions are correct are always shown - a refreshing change from most, if not all other Decision Support Systems (DSS) that simply expect users to accept their outputs on faith alone.

The world's governments are overwhelmed with climate change, war and unrest, the global financial crisis and poverty but there is a promising invention in Global Action Networks (GANs). GANs mobilize resources, bridge divides and promote the long-term deep change and innovation work that is needed to address the global challenges.

Evidence of lean thinking implementation is found in various areas such as services, healthcare, and different industries like the automotive industry, aerospace industry, textile industry, food industry, and oil and gas industry. Such evidence points to the universality of lean thinking and how its use in different contexts increases its importance as an approach to continuous improvement. Lean Thinking in Industry 4.0 and Services for Society presents an insight into lean thinking as a philosophy that can identify problems and wastes in various areas, analyze them, and identify activities that could improve processes. Covering key topics such as industrial systems, lean safety, and lean sustainability, this reference work is ideal for industry professionals, business owners, managers, policymakers, researchers, scholars, academicians, practitioners, instructors, and students.

The overwhelming data produced everyday and the increasing performance and cost requirements of applicationsare transversal to a wide range of activities in society, from science to industry. In particular, the magnitude and complexity of the tasks that Machine Learning (ML) algorithms have to solve are driving the need to devise adaptive many-core machines that scale well with the volume of data, or in other words, can handle Big Data.

This book gives a concise view on how to extend the applicability of well-known ML algorithms in Graphics Processing Unit (GPU) with data scalability in mind. It presents a series of new techniques to enhance, scale and distribute data in a Big Learning framework. It is not intended to be a comprehensive survey of the state of the art of the whole field of machine learning for Big Data. Its purpose is less ambitious and more practical: to explain and illustrate existing and novel GPU-based ML algorithms, not viewed as a universal solution for the Big Data challenges but rather as part of the answer, which may require the use of different strategies coupled together."

Problems with multiple objectives and criteria are generally known as multiple criteria optimization or multiple criteria decision-making (MCDM) problems. So far, these types of problems have typically been modelled and solved by means of linear programming. However, many real-life phenomena are of a nonlinear nature, which is why we need tools for nonlinear programming capable of handling several conflicting or incommensurable objectives. In this case, methods of traditional single objective optimization and linear programming are not enough; we need new ways of thinking, new concepts, and new methods - nonlinear multiobjective optimization. Nonlinear Multiobjective Optimization provides an extensive, up-to-date, self-contained and consistent survey, review of the literature and of the state of the art on nonlinear (deterministic) multiobjective optimization, its methods, its theory and its background. The amount of literature on multiobjective optimization is immense. The treatment in this book is based on approximately 1500 publications in English printed mainly after the year 1980. Problems related to real-life applications often contain irregularities and nonsmoothnesses. The treatment of nondifferentiable multiobjective optimization in the literature is rather rare. For this reason, this book contains material about the possibilities, background, theory and methods of nondifferentiable multiobjective optimization as well. This book is intended for both researchers and students in the areas of (applied) mathematics, engineering, economics, operations research and management science; it is meant for both professionals and practitioners in many different fields of application. The intention has been to provide a consistent summary that may help in selecting an appropriate method for the problem to be solved. It is hoped the extensive bibliography will be of value to researchers.

This volume describes how frontier efficiency methodologies such as Data Envelopment Analysis (DEA) and other techniques such as multi-criteria decision makingcan help service industries to improve their performance by providing a ranking of best-practice efficient service units and by identifying sources of inefficiency for each service unit. It explains how they can be used to determine potential improvement targets for each of the inefficient service units, to identify peers for each service organization and to provide a basis for continuous performance improvement. Presenting applications in a variety of industries, this book will be useful for the service management to improve service productivity, profitability, sustainability and quality and effectiveness of service deliveries. A free trial version of the World s leading Data Envelopment Analysis Software (PIM-DEA) is available for readers of this book. "

Advanced communications and information technologies provide the basis for operational risk management. In order to support managers in real-time risk assessment and decision-making, the advanced technologies must be complemented by an appropriate reasoning logic. This book presents such a reasoning logic for operational risk management. Chapter 1 discusses the need for operational risk management and the feasibility of its use based upon advances in sensing, mobile communications, and satellite positioning technologies. Chapter II presents a reasoning logic for operational risk management that capitalizes upon these developments. Chapter III illustrates the integration of the reasoning logic in hypermedia, multimedia, and virtual reality systems, coupled with the capabilities provided by the Internet. Chapters IV-VI illustrate the realism of operational risk management for hazardous material transportation, emergency response, air raid command, and emergency response at a nuclear power generation facility. The book closes with an experimental assessment of the logic and associated decision aids in Chapter VII. Audience: Researchers, who will find the most recent advances in operational risk management with experimental assessments. Practitioners, who are provided with a detailed description of operational risk management and the latest advances in information and communications technologies to implement this new approach for managing risks in operational settings, such as transportation of hazardous materials and emergency response. Students, who will learn the basic concepts in theory and practice of building models for decision and risk analysis, and embedding them into commercial software as decision support systems.

The first edition of Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques was originally put together to offer a basic introduction to the various search and optimization techniques that students might need to use during their research, and this new edition continues this tradition. Search Methodologies has been expanded and brought completely up to date, including new chapters covering scatter search, GRASP, and very large neighborhood search. The chapter authors are drawn from across Computer Science and Operations Research and include some of the world's leading authorities in their field. The book provides useful guidelines for implementing the methods and frameworks described and offers valuable tutorials to students and researchers in the field. "As I embarked on the pleasant journey of reading through the chapters of this book, I became convinced that this is one of the best sources of introductory material on the search methodologies topic to be found. The book's subtitle, "Introductory Tutorials in Optimization and Decision Support Techniques", aptly describes its aim, and the editors and contributors to this volume have achieved this aim with remarkable success. The chapters in this book are exemplary in giving useful guidelines for implementing the methods and frameworks described." Fred Glover, Leeds School of Business, University of Colorado Boulder, USA "[The book] aims to present a series of well written tutorials by the leading experts in their fields. Moreover, it does this by covering practically the whole possible range of topics in the discipline. It enables students and practitioners to study and appreciate the beauty and the power of some of the computational search techniques that are able to effectively navigate through search spaces that are sometimes inconceivably large. I am convinced that this second edition will build on the success of the first edition and that it will prove to be just as popular." Jacek Blazewicz, Institute of Computing Science, Poznan University of Technology and Institute of Bioorganic Chemistry, Polish Academy of Sciences

Computer Science and Operations Research continue to have a synergistic relationship and this book - as a part of the Operations Research and Computer Science Interface Series - sits squarely in the center of the confluence of these two technical research communities. The research presented in the volume is evidence of the expanding frontiers of these two intersecting disciplines and provides researchers and practitioners with new work in the areas of logic programming, stochastic optimization, heuristic search and post-solution analysis for integer programs. The chapter topics span the spectrum of application level. Some of the chapters are highly applied and others represent work in which the application potential is only beginning. In addition, each chapter contains expository material and reviews of the literature designed to enhance the participation of the reader in this expanding interface.

The financial results of any manufacturing company can be dramatically impacted by the repetitive decisions required to control a complex production network be it a network of machines in a factory; a network of factories in a company; or a network of companies in a supply chain. Decision Policies for Production Networks presents recent convergent research on developing policies for operating production networks including details of practical control and decision techniques which can be applied to improve the effectiveness and economic efficiency of production networks worldwide. Researchers and practitioners come together to explore a wide variety of approaches to a range of topics including:

WIP and equipment management policies,

Material release policies,

Machine, factory, and supply chain network policies for delivery in the face of supply and demand variability, and

Conflicts between complex production network models and their controlling policies.

Case studies and relevant mathematical techniques are included to support and explain techniques such as heuristics, global and hierarchical optimization, control theory and filtering approaches related to complex systems or traffic flows. Decision Policies for Production Networks acts as handbook for researchers and practitioners alike, providing findings and information which can be applied to develop methods and advance further research across production networks.
"

This book covers three fundamental problems at the interface of multi-project management and human resource management: the selection of projects, the composition of small project teams, and workload leveling. Matthias Walter proposes optimization models and solution methods for these problems, assuming multi-skilled workers with heterogeneous skill levels. For the first time, the author presents exact and heuristic methods that support managers to form small teams. Additionally, he outlines a new skill chaining strategy that increases workforce flexibility.

The connected dominating set has been a classic subject studied in graph theory since 1975. Since the 1990s, it has been found to have important applications in communication networks, especially in wireless networks, as a virtual backbone. Motivated from those applications, many papers have been published in the literature during last 15 years. Now, the connected dominating set has become a hot research topic in computer science. In this book, we are going to collect recent developments on the connected dominating set, which presents the state of the art in the study of connected dominating sets. The book consists of 16 chapters. Except the 1st one, each chapter is devoted to one problem, and consists of three parts, motivation and overview, problem complexity analysis, and approximation algorithm designs, which will lead the reader to see clearly about the background, formulation, existing important research results, and open problems. Therefore, this would be a very valuable reference book for researchers in computer science and operations research, especially in areas of theoretical computer science, computer communication networks, combinatorial optimization, and discrete mathematics.

This book introduces data-driven remaining useful life prognosis techniques, and shows how to utilize the condition monitoring data to predict the remaining useful life of stochastic degrading systems and to schedule maintenance and logistics plans. It is also the first book that describes the basic data-driven remaining useful life prognosis theory systematically and in detail. The emphasis of the book is on the stochastic models, methods and applications employed in remaining useful life prognosis. It includes a wealth of degradation monitoring experiment data, practical prognosis methods for remaining useful life in various cases, and a series of applications incorporated into prognostic information in decision-making, such as maintenance-related decisions and ordering spare parts. It also highlights the latest advances in data-driven remaining useful life prognosis techniques, especially in the contexts of adaptive prognosis for linear stochastic degrading systems, nonlinear degradation modeling based prognosis, residual storage life prognosis, and prognostic information-based decision-making.

Organizations - whether profit or nonprofit, services or manufacturing - need to be able to adapt and transform their cultures to succeed. Yet cultural transformation can seem either too easy or completely overwhelming. "Transforming Culture" shows how effective and sustainable cultural transformation can be achieved even in a challenging environment such as a General Motors manufacturing plant. The authors offer both a practical approach and tools to draw on the energy and ideas of employees and executives, remove obstacles to change, and create durable improvements.