This book helps to enhance the application of fuzzy logic optimization in the areas of science and engineering. It includes implementation and models and paradigms, such as path planning and routing design for different wireless networks, organization behavior strategies models, and so forth. It also: Explains inventory control management, uncertainties management, loss minimization, game optimization, data analysis and prediction, different decision-making system and management, and so forth Describes applicability of fuzzy optimization techniques in areas of science and management Resolves several issues based on uncertainty using member function Helps map different problems based on mathematical models Includes issues and problems based on linear and nonlinear optimizations Focuses on management science such as manpower management and inventory planning This book is aimed at researchers and graduate students in signal processing, power systems, systems and industrial engineering, and computer networks.

This book covers research trends of data science and management involving cutting edge technologies and novel research directions from diverse fields of industries, business and government sectors. It involves usage of various advanced tools and techniques for understanding different data collected at the grassroot level to generate actionable insights for making crucial decisions. This book aims to serve as a reference book for researchers in the area of decision science for management. It covers alternative solutions with innovative ideas and issues from different fields of business management.

The aim of the Special Issue "Applications of Fuzzy Optimization and Fuzzy Decision Making" is to expand the applicability of fuzzy optimization and decision making for solving various types of problems in the areas of economics, business, engineering, management, operations research, etc. Any experimental research or empirical study of theoretical developments in fuzzy optimization and decision making is highly welcome. Additionally, research papers presenting solution methods and/or studying their computational complexity, and proposing new algorithms to solve fuzzy optimization and decision making problems, in an effective and efficient manner, are also welcome. We are looking forward to receive innovative approaches that apply, in practical settings, state-of-the art mathematical/algorithmic techniques from fuzzy technology, computational intelligence and soft-computing methodologies, with the aim to offer robust solutions for complex optimization and decision making problems characterized by non-probabilistic uncertainty, vagueness, ambiguity, and hesitation. Such type of papers will address the suitability, validity, and advantages of using fuzzy technologies and the enhancement of them using intelligent methods to treat real-life problems from various disciplines.

This book contains international perspectives that unifies the themes of strategic management, decision theory, and data science. It contains thought-provoking presentations of case studies backed by adequate analysis adding significance to the discussions. Most of the decision-making models in use do take due advantage of collection and processing of relevant data using appropriate analytics oriented to provide inputs into effective decision-making. The book showcases applications in diverse fields including banking and insurance, portfolio management, inventory analysis, performance assessment of comparable economic agents, managing utilities in a health-care facility, reducing traffic snarls on highways, monitoring achievement of some of the sustainable development goals in a country or state, and similar other areas that showcase policy implications. It holds immense value for researchers as well as professionals responsible for organizational decisions.

After developing fuzzy set theory, many contributors focused their research on the extension of fuzzy sets and their computational methodologies, strengthening modern science and technology. In some real-life phenomena, the conventional methods and traditional fuzzy sets cannot be explained, whereas the extension of fuzzy sets and effective new computing methods can explain it adequately. This edited book presents a new view of fuzzy set-measurement methods entitled "Fuzzy Optimization, Decision Making and Operations Research: Theory and Applications", which deals with different perspectives and areas of research. All chapters are divided into three parts: fuzzy optimization, fuzzy decision-making, and fuzzy operation research. The goal of this book is to provide a relevant methodological framework covering the core fields of fuzzy decision-making method, fuzzy optimization method, fuzzy graphics method, fuzzy operations research, fuzzy optimization using graph theory, fuzzy support systems and its real and industrial applications. For many people, fuzzy words' industrial engineering and scientific meanings are still an advanced system for improving modern science and technology. Although fuzzy logic can be applied to many different areas, people do not know how different fuzzy approaches can be applied to various products currently on the market. It is written for professionals who wish to share their expertise, improve their findings, and provide relevant information in the fields of fuzzy methods and their application in decision-making, optimization theory, graph theory and operations research. This book is aimed at experts and practitioners in the fields of fuzzy optimization, fuzzy decision-making, and fuzzy operation research.

When it comes to optimization techniques, in some cases, the available information from real models may not be enough to construct either a probability distribution or a membership function for problem solving. In such cases, there are various theories that can be used to quantify the uncertain aspects. Optimization Techniques for Problem Solving in Uncertainty is a scholarly reference resource that looks at uncertain aspects involved in different disciplines and applications. Featuring coverage on a wide range of topics including uncertain preference, fuzzy multilevel programming, and metaheuristic applications, this book is geared towards engineers, managers, researchers, and post-graduate students seeking emerging research in the field of optimization.

This edition of Operational Research and Management Science Letters (ORMSL) consists of an annotated bibliography of fuzzy greedy search heuristic. The literature contains tutorials, surveys, conference proceedings, theses, and enhancements to the basic concept and its component of a hybrid metaheuristic, including industrial applications.

Most of the existing portfolio selection models are based on the probability theory. Though they often deal with the uncertainty via probabilistic - proaches, we have to mention that the probabilistic approaches only partly capture the reality. Some other techniques have also been applied to handle the uncertainty of the ?nancial markets, for instance, the fuzzy set theory [Zadeh (1965)]. In reality, many events with fuzziness are characterized by probabilistic approaches, although they are not random events. The fuzzy set theory has been widely used to solve many practical problems, including ?nancial risk management. By using fuzzy mathematical approaches, quan- tative analysis, qualitative analysis, the experts’ knowledge and the investors’ subjective opinions can be better integrated into a portfolio selection model. The contents of this book mainly comprise of the authors’ research results for fuzzy portfolio selection problems in recent years. In addition, in the book, the authors will also introduce some other important progress in the ?eld of fuzzy portfolio optimization. Some fundamental issues and problems of po- folioselectionhavebeenstudiedsystematicallyandextensivelybytheauthors to apply fuzzy systems theory and optimization methods. A new framework for investment analysis is presented in this book. A series of portfolio sel- tion models are given and some of them might be more e?cient for practical applications. Some application examples are given to illustrate these models by using real data from the Chinese securities markets.

This new volume presents a range of techniques that aim to enhance the operation of human resource management by applying state-of-the-art artificial intelligence technology. With illustrative case studies, the volume uses examples from several real-life problems and includes their possible solutions using advanced AI technology. The book explores the confluence of smart computing and traditional businesses to foster productivity, profitability, and prosperity and goes on to apply AI techniques in the recruitment process, with enterprise resource planning management software, for manpower optimization systems in colleges, for creating uniformity in HRM across organizations, for creating conflicting strategy management techniques, and more. One pandemicrelated chapter discusses the use of radio frequency-based technology for monitoring social distancing.

Integrated Disaster Science and Management: Global Case Studies in Mitigation and Recovery bridges the gap between scientific research on natural disasters and the practice of disaster management. It examines natural hazards, including earthquakes, landslides and tsunamis, and uses integrated disaster management techniques, quantitative methods and big data analytics to create early warning models to mitigate impacts of these hazards and reduce the risk of disaster. It also looks at mitigation as part of the recovery process after a disaster, as in the case of the Nepal earthquake. Edited by global experts in disaster management and engineering, the book offers case studies that focus on the critical phases of disaster management. - Identifies advanced techniques and models based on natural disaster science for forecasting disasters and analyzing risk - Offers a holistic approach to the problem of disaster management, including preparation, recovery, and resilience - Includes coverage of social, economic, and environmental impacts on disasters