This book shows how the application of fuzzy logic can benefit management, group decision making, strategic planning, supply chain management and other business imperatives. The theoretical analysis is fully supported by real-life case studies. The book develops themes that businesses can use to master effectiveness and quality, work with flexibility, and support continuous learning in the organization and the individual.

This is truly an interdisciplinary book for knowledge workers in business, finance, management and socio-economic sciences based on fuzzy logic. It serves as a guide to and techniques for forecasting, decision making and evaluations in an environment involving uncertainty, vagueness, impression and subjectivity. Traditional modeling techniques, contrary to fuzzy logic, do not capture the nature of complex systems especially when humans are involved. Fuzzy logic uses human experience and judgement to facilitate plausible reasoning in order to reach a conclusion. Emphasis is on applications presented in the 27 case studies including Time Forecasting for Project Management, New Product Pricing, and Control of a Parasit-Pest System.

Fuzzy Logic for the Management of Uncertainty covers many important topics, including:" "Developments in mathematics that have paved the road for fuzzy logic;" "Deep, and of a broad perspective, exposition of virtually all approaches used in contemporary science for the representation and handling of imperfect (uncertain, imprecise, vague, ambiguous, etc.) information;" "Coverage of practically all relevant and promising directions and approaches in fuzzy logic research including LT--fuzzy logic, model theoretic approaches, intuitionistic fuzzy logic, nonmonotonic fuzzy logic, modifier fuzzy logic;" "VLSI fuzzy logic-based chips that have triggered the implementation of fuzzy logic in so many fields of science and technology;" "A broad coverage of fuzzy logic in approximate reasoning, including basic issues related to the role of fuzzy logic for approximate reasoning, analyses of various definitions of fuzzy implication that is a crucial element in fuzzy logic-based reasoning schemes,

"This book explores the possibilities and advantages created by fuzzy methods through the presentation of thorough research and case studies"--Provided by publisher.

In today’s increasingly complex and uncertain business environment, financial analysis is yet more critical to business managers who tackle problems of an economic or business nature. Knowledge based on formal logic and even experience becomes less sufficient. This volume systematically sets out the basic elements on which to base financial analysis for business in the new century. It incorporates a previous work that can serve as the basis and foundation for the new contributions that are now being made in the field of financial economy and intend to provide business with instruments and models suitable for dealing with the new economic context. In dealing with rapid and unpredictable changes in technological and business conditions, it postulates a growing reliance on the opinions of experts instead of past data or probabilistic forecasts, which is a radical change but may yield fruitful results. For this reason, much emphasis is devoted to the problem of aggregation of the opinion of experts in the financial field, with the object of limiting, wherever possible, the subjective component of the opinions and making sure that the decisions have the best guarantee of reaching the desired objectives.

The rapid changes that have taken place globally on the economic, social and business fronts characterized the 20th century. The magnitude of these changes has formed an extremely complex and unpredictable decision-making framework, which is difficult to model through traditional approaches. The main purpose of this book is to present the most recent advances in the development of innovative techniques for managing the uncertainty that prevails in the global economic and management environments. These techniques originate mainly from fuzzy sets theory. However, the book also explores the integration of fuzzy sets with other decision support and modeling disciplines, such as multicriteria decision aid, neural networks, genetic algorithms, machine learning, chaos theory, etc. The presentation of the advances in these fields and their real world applications adds a new perspective to the broad fields of management science and economics. Contents: Decision Making, Management and Marketing: Algorithms for Orderly Structuring of Financial OC ObjectsOCO (J Gil-Aluja); A Fuzzy Goal Programming Model for Evaluating a Hospital Service Performance (M Arenas et al.); A Group Decision Making Method Using Fuzzy Triangular Numbers (J L Garc a-Lapresta et al.); Developing Sorting Models Using Preference Disaggregation Analysis: An Experimental Investigation (M Doumpos & C Zopounidis); Stock Markets and Portfolio Management: The Causality Between Interest Rate, Exchange Rate and Stock Price in Emerging Markets: The Case of the Jakarta Stock Exchange (J Gupta et al.); Fuzzy Cognitive Maps in Stock Market (D Koulouriotis et al.); Neural Network vs Linear Models of Stock Returns: An Application to the UK and German Stock Market Indices (A Kanas); Corporate Finance and Banking Management: Expertons and Behaviour of Companies with Regard to the Adequacy Between Business Decisions and Objectives (A Couturier & B Fioleau); Multiple Fuzzy IRR in the Financial Decision Environment (S F Gonzilez et al.); An Automated Knowledge Generation Approach for Managing Credit Scoring Problems (M Michalopoulos et al.); and other papers. Readership: Financial managers, economists, management scientists and computer scientists."

In this hands-on, practical guide, you'll walk through powerful fuzzy logic business applications for business, including risk assessment, forecasting, supplier evaluation, customer targeting, and scheduling. You'll watch fuzzy logic at work analyzing credit risk, evaluating leases, making stock market decisions, and uncovering fraud.

An Introduction to Fuzzy Logic Applications in Intelligent Systems consists of a collection of chapters written by leading experts in the field of fuzzy sets. Each chapter addresses an area where fuzzy sets have been applied to situations broadly related to intelligent systems. The volume provides an introduction to and an overview of recent applications of fuzzy sets to various areas of intelligent systems. Its purpose is to provide information and easy access for people new to the field. The book also serves as an excellent reference for researchers in the field and those working in the specifics of systems development. People in computer science, especially those in artificial intelligence, knowledge-based systems, and intelligent systems will find this to be a valuable sourcebook. Engineers, particularly control engineers, will also have a strong interest in this book. Finally, the book will be of interest to researchers working in decision support systems, operations research, decision theory, management science and applied mathematics. An Introduction to Fuzzy Logic Applications in Intelligent Systems may also be used as an introductory text and, as such, it is tutorial in nature.

Artificial intelligence (AI) describes machines/computers that mimic cognitive functions that humans associate with other human minds, such as learning and problem solving. As businesses have evolved to include more automation of processes, it has become more vital to understand AI and its various applications. Additionally, it is important for workers in the marketing industry to understand how to coincide with and utilize these techniques to enhance and make their work more efficient. The Handbook of Research on Applied AI for International Business and Marketing Applications is a critical scholarly publication that provides comprehensive research on artificial intelligence applications within the context of international business. Highlighting a wide range of topics such as diversification, risk management, and artificial intelligence, this book is ideal for marketers, business professionals, academicians, practitioners, researchers, and students.

This book offers a timely overview of fuzzy and rough set theories and methods. Based on selected contributions presented at the International Symposium on Fuzzy and Rough Sets, ISFUROS 2017, held in Varadero, Cuba, on October 24-26, 2017, the book also covers related approaches, such as hybrid rough-fuzzy sets and hybrid fuzzy-rough sets and granular computing, as well as a number of applications, from big data analytics, to business intelligence, security, robotics, logistics, wireless sensor networks and many more. It is intended as a source of inspiration for PhD students and researchers in the field, fostering not only new ideas but also collaboration between young researchers and institutions and established ones.