This book presents the theory and methods of flexible and generalized uncertainty optimization. Particularly, it describes the theory of generalized uncertainty in the context of optimization modeling. The book starts with an overview of flexible and generalized uncertainty optimization. It covers uncertainties that are both associated with lack of information and are more general than stochastic theory, where well-defined distributions are assumed. Starting from families of distributions that are enclosed by upper and lower functions, the book presents construction methods for obtaining flexible and generalized uncertainty input data that can be used in a flexible and generalized uncertainty optimization model. It then describes the development of the associated optimization model in detail. Written for graduate students and professionals in the broad field of optimization and operations research, this second edition has been revised and extended to include more worked examples and a section on interval multi-objective mini-max regret theory along with its solution method.

This book introduces readers to an evolutionary learning approach, specifically genetic programming (GP), for production scheduling. The book is divided into six parts. In Part I, it provides an introduction to production scheduling, existing solution methods, and the GP approach to production scheduling. Characteristics of production environments, problem formulations, an abstract GP framework for production scheduling, and evaluation criteria are also presented. Part II shows various ways that GP can be employed to solve static production scheduling problems and their connections with conventional operation research methods. In turn, Part III shows how to design GP algorithms for dynamic production scheduling problems and describes advanced techniques for enhancing GP's performance, including feature selection, surrogate modeling, and specialized genetic operators. In Part IV, the book addresses how to use heuristics to deal with multiple, potentially conflicting objectives in production scheduling problems, and presents an advanced multi-objective approach with cooperative coevolution techniques or multi-tree representations. Part V demonstrates how to use multitask learning techniques in the hyper-heuristics space for production scheduling. It also shows how surrogate techniques and assisted task selection strategies can benefit multitask learning with GP for learning heuristics in the context of production scheduling. Part VI rounds out the text with an outlook on the future. Given its scope, the book benefits scientists, engineers, researchers, practitioners, postgraduates, and undergraduates in the areas of machine learning, artificial intelligence, evolutionary computation, operations research, and industrial engineering.

This book assesses potential developments of terrorism and ways to prevent it-the growing threats as new technologies become available - and how the same new technologies may help trap those with potential mal-intent. The drumbeat of terror resonates from everywhere; how can we stop it? What are the tripping points along the road and how can we avoid them? Increasingly more people have access to increasingly more information and increasingly more destructive technologies. In the meantime, increasingly advanced technologies help us create an increasingly safer and more harmonious world. Advantages and disadvantages are accelerating each other. While hybrid threats are intensifying, so are the opportunities to address them. But what are the compromises and how can we mitigate them? This book also looks at the unexpected and often random success and failure of policies to counter the evolving terror threat. The various aspects of the terrorism phenomena are presented in a unique way using scenario vignettes, which give the reader a realistic perception of the threat. The combination of positive and negative implications of emerging technologies is describing what might well be one of the most important dimensions of our common future.

This book discusses corporate citizenship, corporate responsibility and business ethics across Africa generally, and Botswana specifically. It begins by contextualizing Botswana within the broader context of Africa, using nine other countries - Angola, Cameroon, Ghana, Kenya, Nigeria, South Africa, Zambia and Zimbabwe - to provide a comparative perspective, examining the common factor: that weak legalization makes it challenging for corporate social responsibility to be actualized.From this background, the book then discusses Botswana as a key study. Botswana has been described as 'Africa's economic miracle' due to its growing economy since independence This puts it in a unique position for the implementation and study of corporate social responsibility. The interdisciplinary team of authors employ various research methods to examine the complex relationship between business, society, corporations and social justice issues.This book will be valuable reading for any academic working on corporate social responsibility in Africa, and will present an interesting insight to an often neglected area of study. France Maphosa is a Professor of Sociology at the University of Botswana. His research interests include migration and transnationalism, the sociology of entrepreneurship, corporate social responsibility, urban and rural livelihoods, labour studies and alternative dispute resolution (ADR). Langtone Maunganidze is a Senior Lecturer in the Faculty of Social Sciences at the Midlands State University in Zimbabwe. His research interests include industrial sociology, business and society, rural livelihoods and sustainable development, and entrepreneurship.

Based on the "Fourth International Conference on Dynamics of Disasters" (Kalamata, Greece, July 2019), this volume includes contributions from experts who share their latest discoveries on natural and unnatural disasters. Authors provide overviews of the tactical points involved in disaster relief, outlines of hurdles from mitigation and preparedness to response and recovery, and uses for mathematical models to describe natural and man-made disasters. Topics covered include economics, optimization, machine learning, government, management, business, humanities, engineering, medicine, mathematics, computer science, behavioral studies, emergency services, and environmental studies will engage readers from a wide variety of fields and backgrounds.

This book examines inventory and production strategies that can reduce unexpected breakdown costs. It highlights different EPQ models to deal with such problems, providing optimal value derivations for decision variables. It provides proofs for concavity or convexity of objective functions. The chapters also include numerical examples for all the developed mathematical models. Imperfect Inventory Systems: Inventory and Production Management and Breakdown should be useful for professionals working on supply chains, but also researchers in operations research and inventory management.

Applications of queueing network models have multiplied in the last generation, including scheduling of large manufacturing systems, control of patient flow in health systems, load balancing in cloud computing, and matching in ride sharing. These problems are too large and complex for exact solution, but their scale allows approximation. This book is the first comprehensive treatment of fluid scaling, diffusion scaling, and many-server scaling in a single text presented at a level suitable for graduate students. Fluid scaling is used to verify stability, in particular treating max weight policies, and to study optimal control of transient queueing networks. Diffusion scaling is used to control systems in balanced heavy traffic, by solving for optimal scheduling, admission control, and routing in Brownian networks. Many-server scaling is studied in the quality and efficiency driven Halfin-Whitt regime and applied to load balancing in the supermarket model and to bipartite matching in ride-sharing applications.

This book takes the reader through real-world examples for how to characterize and measure the productivity and performance of NFPs and education institutions-that is, organisations that produce value for society, which cannot be measured accurately in financial KPIs. It focuses on how best to frame non-profit performance and productivity, and provides a suite of tools for measurement and benchmarking. It further challenges the reader to consider alternative and appropriate uses of quantitative measures, which are fit-for-purpose in individual contexts. It is true that the risk of misusing quantitative measures is ever-present. But does that risk outweigh the benefits of forming a more precise and shared understanding of what could generate better outcomes? There will always be concerns about policy and performance management. Goodheart's Law states that once a measure becomes a target, it is no longer a good measure. This book helps to strike a meaningful balance between what can be measured, what cannot, and how best to use quantitative information in sectors that are often averse to being held up to the light and put on a scale by outsiders.

The book consolidates systems thinking as a new world-hypothesis that is already suggesting itself behind the advancement of quantum mechanics and Ashby's cybernetics. In particular, it shows how Einstein's misgivings about quantum mechanics boil down to his persistence in defending the principle of contiguity at the root of the modern cosmology and, in relation to neo-cybernetics, the book rediscovers Ashby's theory of adaptive behaviour enabling a new synthesis between physiology, psychology and ethology that has implications for systems practice. Furthermore, this new "cosmology" comes with a new "anthropology" that informs utopics, the science of utopic systems, and sheds new light on the actual founding fathers of the domain of human science. In particular, the book provides an understanding of how our human world works and how it is being constituted by utopic systems that look into the future to realize something possible. Finally, it points the way to the future unification of knowledge bringing together systems philosophy and systems science given that world-hypothesis is what makes logically possible the development and consolidation of all the different domains of science.

This book tackles the perplexing problem of how to capture the qualitative differences that exist in entrepreneurship at any given point in time or across time, by presenting a novel qualitative index: Entrepreneurship Quality Index (EQI). This comprehensive composite index is based on recognized interactions among different factors affecting intensity and types of entrepreneurial activity, which in turn is impacting the well-being. It brings qualitative differences in entrepreneurship depending on time and space into calculation of the composite index. Besides, EQI is the missing piece of the entrepreneurship puzzle, and the quality of entrepreneurship is a significant factor that shows why less developed countries do not achieve higher levels of economic growth, despite their high rate of entrepreneurial activities. This book does a masterful job in explaining the diversity of existing perspectives, their contributions, and their shortfalls. It applies rigorous tools of mathematical statistics and quality engineering, such as Bayes' rule, maximum likelihood estimation, six sigma algorithm, and standardization equation, to derive and introduce EQI, as a novel operations research model. It offers a number of important ideas and insights as to how the quality of entrepreneurship can be measured more accurately. It also illustrates the proposed approaches showing their efficacy across time. The proposed solutions are logical and empirically sound; they represent an important leap in thinking about the quality of entrepreneurship. Its contribution is crucial and timely - one that will be well recognized by researchers worldwide. They give a much-needed framework along with the necessary tools to evaluate and measure entrepreneurial activities.

The book offers a comprehensive survey of interval-valued intuitionistic fuzzy sets. It reports on cutting-edge research carried out by the founder of the intuitionistic fuzzy sets, Prof. Krassimir Atanassov, giving a special emphasis to the practical applications of this extension. A few interesting case studies, such as in the area of data mining, decision making and pattern recognition, among others, are discussed in detail. The book offers the first comprehensive guide on interval-valued intuitionistic fuzzy sets. By providing the readers with a thorough survey and important practical details, it is expected to support them in carrying out applied research and to encourage them to test the theory behind the sets for new advanced applications. The book is a valuable reference resource for graduate students and researchers alike.

This book presents a collection of mathematical models that deals with the real scenario in the industries. The primary objective of this book is to explore various effective methods for inventory control and management using soft computing techniques. Inventory control and management is a very tedious task faced by all the organizations in any sector of the economy. It makes decisions for policies, activities, and procedures in order to make sure that the right amount of each item is held in stock at any time. Many industries suffer from indiscipline while ordering and production mismatch. It is essential to provide best ordering policy to control such kind of mismatch in the industries. All the mathematical model solutions are provided with the help of various soft computing optimization techniques to determine optimal ordering policy. This book is beneficial for practitioners, educators, and researchers. It is also helpful for retailers/managers for improving business functions and making more accurate and realistic decisions.

This book focuses on the adoption of a Dynamic Performance Management (DPM) approach to support the planning and management of urban transportation systems. DPM allows one to embrace a dynamic and systemic perspective and, as a result, to frame the contribution of different stakeholders, in terms of outcome-based performance, at an inter-institutional level. The discussed DPM approach allows one to focus on the causal relationships and feedback processes that characterize urban transportation systems' governance. Particularly, through the adoption of such an approach, it is possible to identify outcome-based performance measures that help to monitor and drive the governance network toward the creation of public value for the reference communities.Strategic Planning for Urban Transportation: A Dynamic Performance Management Approach begins with an examination of urban transportation, highlighting the main criticalities and future challenges of managing it. Next, the book examines the governance of urban transportation including how to identify and engage stakeholders. Finally, the book introduces the main application of DPM and system dynamics to urban areas, with specific regards to transportation. The framework is outlined, and a case study is provided as a practical example of how to apply the model. This book should be useful to urban transportation decision-makers at both the managerial and political level.

Elgar Research Agendas outline the future of research in a given area. Leading scholars are given the space to explore their subject in provocative ways, and map out the potential directions of travel. They are relevant but also visionary. Managing and organizing are now central phenomena in contemporary societies. It is essential they are studied from a variety of perspectives, and with equal attention paid to their past, their present, and their future. This book collects opinions of trailblazing scholars concerning the most important research topics, essential for study in the next 15-20 years. The opinions concern both traditional functions, such as accounting and marketing, personnel management and strategy, technology and communication, but also new challenges, such as diversity, equality, waste and cultural encounters. The collection is intended to be inspiration for young scholars and an invitation to a dialogue with practitioners. The book's contributions are written by well-established scholars. Each is a leader in their field and will remain important figures for the next twenty years and beyond. Each chapter starts with a short summary of the present situation but focuses on the future of the discipline. The contributors cover practically all subfields of what is called business administration, or management and organization studies and include contain topics that are new, such as invisible organizations or encounters between art, popular culture and organizing. Outlining the future and the state of the art, this comprehensive and innovative book is an essential resource for students and academics seeking to be at the forefront of future research in management and organization studies. Contributors include: Y. Benschop, T. Beyes, F. Cochoy, F. Cooren, H. Corvellec, J. Costas, A. Diedrich, M.-L. Djelic, G.S. Drori, C. Grey, M. Kornberger, M. Kostera, W.J. Orlikowski, M. Parker, P. Quattrone, C. Rhodes, S.V. Scott, J. Smolinski, J.-S. Vayre

This book analyses decision-making in dynamic economic environments. By applying a wide range of methodological approaches, combining both analytical and computational methods, the contributors examine various aspects of optimal firm behaviour and relevant policy areas. Topics covered include optimal control, dynamic games, economic decision-making, and applications in finance and economics, as well as policy implications in areas such as pollution regulation. This book is dedicated to Christophe Deissenberg, a well-known and distinguished scholar of economic dynamics and computational economics. It appeals to academics in the areas of optimal control, dynamic games and computational economics as well as to decision-makers working in policy domains such as environmental policy.

This book demonstrates that innovative ideas are systematically constructed in the creative space spanned by the dimensions of systems thinking and knowledge management. Readers will be introduced to this proposition in the final chapter, after learning about the key innovation theories, design thinking, systems thinking, and idea creation methods in systems science and knowledge science. The content provided throughout the book supports knowledge creation in various fields, the management of research and business projects, and the creation of promotion stories for products and services. Practitioners who are seeking to create innovative ideas can systematically learn the minimum theories and methods required, while graduate students will be equipped to link their research to innovation by learning the essence of systems science and knowledge science and considering selected issues. Lastly, the book includes suggestions for future research directions in knowledge science.

Technological advancements in recent years have led to significant developments within a variety of business applications. In particular, data-driven research provides ample opportunity for enterprise growth, if utilized efficiently. Supply Chain Management in the Big Data Era is an authoritative reference source for the latest scholarly material on the implementation of big data analytics for improved operations and supply chain processes. Highlighting emerging strategies from different industry perspectives, this book is ideally designed for managers, professionals, practitioners, and students interested in the most recent research on supply chain innovations.

This book summarizes years of research in the field of fuzzy relational programming, with a special emphasis on geometric models. It discusses the state-of-the-art in fuzzy relational geometric problems, together with key open issues that must be resolved to achieve a more efficient application of this method. Though chiefly based on research conducted by the authors, who were the first to introduce fuzzy geometric problems, it also covers important findings obtained in the field of linear and non-linear programming. Thanks to its balance of basic and advanced concepts, and its wealth of practical examples, the book offers a valuable guide for both newcomers and experienced researcher in the fields of soft computing and mathematical optimization.

Dynamic programming (DP) has a relevant history as a powerful and flexible optimization principle, but has a bad reputation as a computationally impractical tool. This book fills a gap between the statement of DP principles and their actual software implementation. Using MATLAB throughout, this tutorial gently gets the reader acquainted with DP and its potential applications, offering the possibility of actual experimentation and hands-on experience. The book assumes basic familiarity with probability and optimization, and is suitable to both practitioners and graduate students in engineering, applied mathematics, management, finance and economics.

This proceedings volume presents recent theoretical and practical advances in operational research (OR). The papers focus on a number of key areas including combinatorial optimization, integer programming, heuristics, and mathematical programming. In addition, this volume highlights OR applications in different areas such as financial decision making, marketing, e-business, project management, scheduling, traffic and transportation. The chapters are based on papers presented at the 13th Balkan Conference on Operations Research (BALCOR). BALCOR is an established biennial conference. The selected papers promote international collaboration among researchers and practitioners, with a particular focus on the Balkan countries.

This book presents the latest trends and developments in multimodal optimization and niching techniques. Most existing optimization methods are designed for locating a single global solution. However, in real-world settings, many problems are "multimodal" by nature, i.e., multiple satisfactory solutions exist. It may be desirable to locate several such solutions before deciding which one to use. Multimodal optimization has been the subject of intense study in the field of population-based meta-heuristic algorithms, e.g., evolutionary algorithms (EAs), for the past few decades. These multimodal optimization techniques are commonly referred to as "niching" methods, because of the nature-inspired "niching" effect that is induced to the solution population targeting at multiple optima. Many niching methods have been developed in the EA community. Some classic examples include crowding, fitness sharing, clearing, derating, restricted tournament selection, speciation, etc. Nevertheless, applying these niching methods to real-world multimodal problems often encounters significant challenges. To facilitate the advance of niching methods in facing these challenges, this edited book highlights the latest developments in niching methods. The included chapters touch on algorithmic improvements and developments, representation, and visualization issues, as well as new research directions, such as preference incorporation in decision making and new application areas. This edited book is a first of this kind specifically on the topic of niching techniques. This book will serve as a valuable reference book both for researchers and practitioners. Although chapters are written in a mutually independent way, Chapter 1 will help novice readers get an overview of the field. It describes the development of the field and its current state and provides a comparative analysis of the IEEE CEC and ACM GECCO niching competitions of recent years, followed by a collection of open research questions and possible research directions that may be tackled in the future.

This textbook treats graph colouring as an algorithmic problem, with a strong emphasis on practical applications. The author describes and analyses some of the best-known algorithms for colouring graphs, focusing on whether these heuristics can provide optimal solutions in some cases; how they perform on graphs where the chromatic number is unknown; and whether they can produce better solutions than other algorithms for certain types of graphs, and why. The introductory chapters explain graph colouring, complexity theory, bounds and constructive algorithms. The author then shows how advanced, graph colouring techniques can be applied to classic real-world operational research problems such as designing seating plans, sports scheduling, and university timetabling. He includes many examples, suggestions for further reading, and historical notes, and the book is supplemented by an online suite of downloadable code. The book is of value to researchers, graduate students, and practitioners in the areas of operations research, theoretical computer science, optimization, and computational intelligence. The reader should have elementary knowledge of sets, matrices, and enumerative combinatorics.