This book presents a comprehensive guide to Analytic Hierarchy Process with Fuzzy Sets Extensions. Written by prominent researchers, it offers readers a step-by-step approach to decision-making that can be applied to a wide range of real-world problems. With clear explanations and practical examples, this book is an essential resource for researchers, lecturers, and postgraduate students pursuing research on Analytic Hierarchy Process with Fuzzy Sets Extensions.  Each chapter in the book provides a wealth of both basic and advanced concepts, including Interval Type-2 Fuzzy AHP, Intuitionistic Fuzzy AHP, Hesitant Fuzzy AHP, Pythagorean Fuzzy AHP, Picture Fuzzy AHP, q-Rung Orthopair Fuzzy AHP, Spherical Fuzzy AHP, Circular Intuitionistic Fuzzy AHP, and Decomposed Fuzzy AHP. To foster a better understanding, all the chapters include relevant numerical examples or case studies, making it easy to apply the concepts in practice.  Moreover, the book extends all the main aspects of Analytic Hierarchy Process with Fuzzy Sets Extensions, presenting a dynamic snapshot of the field that is expected to stimulate new directions, ideas, and developments.  In summary, this book is an essential reference guide for anyone seeking to gain a deep understanding of Analytic Hierarchy Process with Fuzzy Sets Extensions. It is written in a rigorous and scholarly manner, making it a valuable resource for researchers and academics. Order your copy today and discover the practical insights and strategies for decision-making offered by this authoritative volume.

This book consists of the papers accepted after a careful review process at an international scientific meeting where the latest developments on intelligent and fuzzy systems are presented and discussed. The latest developments in both the theoretical and practical fields of the new fuzzy set extensions have been prepared by expert researchers. Contributed by participants from more than 40 different countries, this book is also a useful resource in terms of showing the levels that fuzzy and intelligent systems have reached in various countries of the world. The intended readers are intelligent and fuzzy systems researchers, lecturers, M.Sc., and Ph.D. students studying fuzzy sets and artificial intelligence. The book covers fuzzy logic theory and applications, heuristics, and metaheuristics from optimization to machine learning, from quality management to risk management, making the book an excellent source for researchers.

This book consists of the papers accepted after a careful review process at an international scientific meeting where the latest developments on intelligent and fuzzy systems are presented and discussed. The latest developments in both the theoretical and practical fields of the new fuzzy set extensions have been prepared by expert researchers. Contributed by participants from more than 40 different countries, this book is also a useful resource in terms of showing the levels that fuzzy and intelligent systems have reached in various countries of the world. The intended readers are intelligent and fuzzy systems researchers, lecturers, M.Sc., and Ph.D. students studying fuzzy sets and artificial intelligence. The book covers fuzzy logic theory and applications, heuristics, and metaheuristics from optimization to machine learning, from quality management to risk management, making the book an excellent source for researchers.

In this present internet age, risk analysis and crisis response based on information will make up a digital world full of possibilities and improvements to people's daily life and capabilities. These services will be supported by more intelligent systems and more effective decisionmaking. This book contains all the papers presented at the 4th International Conference on Risk Analysis and Crisis Response, August 27-29, 2013, Istanbul, Turkey. The theme was intelligent systems and decision making for risk analysis and crisis response. The risk issues in the papers cluster around the following topics: natural disasters, finance risks, food and feed safety, catastrophic accidents, critical infrastructure, global climate change, project management, supply chains, public health, threats to social safety, energy and environment. This volume will be of interest to all professionals and academics in the field of risk analysis, crisis response, intelligent systems and decision-making, as well as related fields of enquiry.

This book offers a comprehensive reference guide for modeling humanoid robots using intelligent and fuzzy systems. It provides readers with the necessary intelligent and fuzzy tools for controlling humanoid robots by incomplete, vague, and imprecise information or insufficient data, where classical modeling approaches cannot be applied. The respective chapters, written by prominent researchers, explain a wealth of both basic and advanced concepts including fuzzy control, metaheuristic-based control, neutrosophic control, etc. To foster reader comprehension, all chapters include relevant numerical examples or case studies. Taken together, they form an excellent reference guide for researchers, lecturers, and postgraduate students pursuing research on humanoid robots. Moreover, by extending all the main aspects of humanoid robots to its intelligent and fuzzy counterparts, the book presents a dynamic snapshot of the field that is expected to stimulate new directions, ideas, and developments.

This book introduces readers to the novel concept of spherical fuzzy sets, showing how these sets can be applied in practice to solve various decision-making problems. It also demonstrates that these sets provide a larger preference volume in 3D space for decision-makers. Written by authoritative researchers, the various chapters cover a large amount of theoretical and practical information, allowing readers to gain an extensive understanding of both the fundamentals and applications of spherical fuzzy sets in intelligent decision-making and mathematical programming.

This book offers a comprehensive reference guide for modeling humanoid robots using intelligent and fuzzy systems. It provides readers with the necessary intelligent and fuzzy tools for controlling humanoid robots by incomplete, vague, and imprecise information or insufficient data, where classical modeling approaches cannot be applied. The respective chapters, written by prominent researchers, explain a wealth of both basic and advanced concepts including fuzzy control, metaheuristic-based control, neutrosophic control, etc. To foster reader comprehension, all chapters include relevant numerical examples or case studies. Taken together, they form an excellent reference guide for researchers, lecturers, and postgraduate students pursuing research on humanoid robots. Moreover, by extending all the main aspects of humanoid robots to its intelligent and fuzzy counterparts, the book presents a dynamic snapshot of the field that is expected to stimulate new directions, ideas, and developments.

This book offers a comprehensive reference guide to customer-oriented product design and intelligence. It provides readers with the necessary intelligent tools for designing customer-oriented products in contexts characterized by incomplete information or insufficient data, where classical product design approaches cannot be applied. The respective chapters, written by prominent researchers, explain a wealth of both basic and advanced concepts including fuzzy QFD, fuzzy FMEA, the fuzzy Kano model, fuzzy axiomatic design, fuzzy heuristics-based design, conjoint analysis-based design, and many others. To foster reader comprehension, all chapters include relevant numerical examples or case studies. Taken together, they form an excellent reference guide for researchers, lecturers, and postgraduate students pursuing research on customer-oriented product design. Moreover, by extending all the main aspects of classical customer-oriented product design to its intelligent and fuzzy counterparts, the book presents a dynamic snapshot of the field that is expected to stimulate new directions, ideas, and developments.

This book introduces readers to the novel concept of spherical fuzzy sets, showing how these sets can be applied in practice to solve various decision-making problems. It also demonstrates that these sets provide a larger preference volume in 3D space for decision-makers. Written by authoritative researchers, the various chapters cover a large amount of theoretical and practical information, allowing readers to gain an extensive understanding of both the fundamentals and applications of spherical fuzzy sets in intelligent decision-making and mathematical programming.

This book offers a comprehensive reference guide to customer-oriented product design and intelligence. It provides readers with the necessary intelligent tools for designing customer-oriented products in contexts characterized by incomplete information or insufficient data, where classical product design approaches cannot be applied. The respective chapters, written by prominent researchers, explain a wealth of both basic and advanced concepts including fuzzy QFD, fuzzy FMEA, the fuzzy Kano model, fuzzy axiomatic design, fuzzy heuristics-based design, conjoint analysis-based design, and many others. To foster reader comprehension, all chapters include relevant numerical examples or case studies. Taken together, they form an excellent reference guide for researchers, lecturers, and postgraduate students pursuing research on customer-oriented product design. Moreover, by extending all the main aspects of classical customer-oriented product design to its intelligent and fuzzy counterparts, the book presents a dynamic snapshot of the field that is expected to stimulate new directions, ideas, and developments.

This book offers a comprehensive reference guide to operations research theory and applications in health care systems. It provides readers with all the necessary tools for solving health care problems. The respective chapters, written by prominent researchers, explain a wealth of both basic and advanced concepts of operations research for the management of operating rooms, intensive care units, supply chain, emergency medical service, human resources, lean health care, and procurement. To foster a better understanding, the chapters include relevant examples or case studies. Taken together, they form an excellent reference guide for researchers, lecturers and postgraduate students pursuing research on health care management problems. The book presents a dynamic snapshot on the field that is expected to stimulate new directions and stimulate new ideas and developments.

This book offers a comprehensive reference guide to intelligence systems in environmental management. It provides readers with all the necessary tools for solving complex environmental problems, where classical techniques cannot be applied. The respective chapters, written by prominent researchers, explain a wealth of both basic and advanced concepts including ant colony, genetic algorithms, evolutionary algorithms, fuzzy multi-criteria decision making tools, particle swarm optimization, agent-based modelling, artificial neural networks, simulated annealing, Tabu search, fuzzy multi-objective optimization, fuzzy rules, support vector machines, fuzzy cognitive maps, cumulative belief degrees, and many others. To foster a better understanding, all the chapters include relevant numerical examples or case studies. Taken together, they form an excellent reference guide for researchers, lecturers and postgraduate students pursuing research on complex environmental problems. Moreover, by extending all the main aspects of classical environmental solution techniques to its intelligent counterpart, the book presents a dynamic snapshot on the field that is expected to stimulate new directions and stimulate new ideas and developments.

This book offers a comprehensive reference guide to fuzzy statistics and fuzzy decision-making techniques. It provides readers with all the necessary tools for making statistical inference in the case of incomplete information or insufficient data, where classical statistics cannot be applied. The respective chapters, written by prominent researchers, explain a wealth of both basic and advanced concepts including: fuzzy probability distributions, fuzzy frequency distributions, fuzzy Bayesian inference, fuzzy mean, mode and median, fuzzy dispersion, fuzzy p-value, and many others. To foster a better understanding, all the chapters include relevant numerical examples or case studies. Taken together, they form an excellent reference guide for researchers, lecturers and postgraduate students pursuing research on fuzzy statistics. Moreover, by extending all the main aspects of classical statistical decision-making to its fuzzy counterpart, the book presents a dynamic snapshot of the field that is expected to stimulate new directions, ideas and developments.

This book offers a multifaceted perspective on fuzzy set theory, discussing its developments over the last 50 years. It reports on all types of fuzzy sets, from ordinary to hesitant fuzzy sets, with each one explained by its own developers, authoritative scientists well known for their previous works. Highlighting recent theorems and proofs, the book also explores how fuzzy set theory has come to be extensively used in almost all branches of science, including the health sciences, decision science, earth science and the social sciences alike. It presents a wealth of real-world sample applications, from routing problem to robotics, and from agriculture to engineering. By offering a comprehensive, timely and detailed portrait of the field, the book represents an excellent reference guide for researchers, lecturers and postgraduate students pursuing research on new fuzzy set extensions.

This book presents a selection of recently developed collective and computational intelligence techniques, which it subsequently applies to energy management problems ranging from performance analysis to economic analysis, and from strategic analysis to operational analysis, with didactic numerical examples. As a form of intelligence emerging from the collaboration and competition of individuals, collective and computational intelligence addresses new methodological, theoretical, and practical aspects of complex energy management problems. The book offers an excellent reference guide for practitioners, researchers, lecturers and postgraduate students pursuing research on intelligence in energy management. The contributing authors are recognized researchers in the energy research field.

This book offers a comprehensive reference guide to operations research theory and applications in health care systems. It provides readers with all the necessary tools for solving health care problems. The respective chapters, written by prominent researchers, explain a wealth of both basic and advanced concepts of operations research for the management of operating rooms, intensive care units, supply chain, emergency medical service, human resources, lean health care, and procurement. To foster a better understanding, the chapters include relevant examples or case studies. Taken together, they form an excellent reference guide for researchers, lecturers and postgraduate students pursuing research on health care management problems. The book presents a dynamic snapshot on the field that is expected to stimulate new directions and stimulate new ideas and developments.

This book presents recently developed intelligent techniques with applications and theory in the area of engineering management. The involved applications of intelligent techniques such as neural networks, fuzzy sets, Tabu search, genetic algorithms, etc. will be useful for engineering managers, postgraduate students, researchers, and lecturers. The book has been written considering the contents of a classical engineering management book but intelligent techniques are used for handling the engineering management problem areas. This comprehensive characteristics of the book makes it an excellent reference for the solution of complex problems of engineering management. The authors of the chapters are well-known researchers with their previous works in the area of engineering management.

This book offers a comprehensive reference guide to intelligence systems in environmental management. It provides readers with all the necessary tools for solving complex environmental problems, where classical techniques cannot be applied. The respective chapters, written by prominent researchers, explain a wealth of both basic and advanced concepts including ant colony, genetic algorithms, evolutionary algorithms, fuzzy multi-criteria decision making tools, particle swarm optimization, agent-based modelling, artificial neural networks, simulated annealing, Tabu search, fuzzy multi-objective optimization, fuzzy rules, support vector machines, fuzzy cognitive maps, cumulative belief degrees, and many others. To foster a better understanding, all the chapters include relevant numerical examples or case studies. Taken together, they form an excellent reference guide for researchers, lecturers and postgraduate students pursuing research on complex environmental problems. Moreover, by extending all the main aspects of classical environmental solution techniques to its intelligent counterpart, the book presents a dynamic snapshot on the field that is expected to stimulate new directions and stimulate new ideas and developments.

Supply Chain Management Under Fuzziness presents recently developed fuzzy models and techniques for supply chain management. These include: fuzzy PROMETHEE, fuzzy AHP, fuzzy ANP, fuzzy VIKOR, fuzzy DEMATEL, fuzzy clustering, fuzzy linear programming, and fuzzy inference systems. The book covers both practical applications and new developments concerning these methods. This book offers an excellent resource for researchers and practitioners in supply chain management and logistics, and will provide them with new suggestions and directions for future research. Moreover, it will support graduate students in their university courses, such as specialized courses on supply chains and logistics, as well as related courses in the fields of industrial engineering, engineering management and business administration.

This book presents recently developed intelligent techniques with applications and theory in the area of quality management. The involved applications of intelligence include techniques such as fuzzy sets, neural networks, genetic algorithms, etc. The book consists of classical quality management topics dealing with intelligent techniques for solving the complex quality management problems. The book will serve as an excellent reference for quality managers, researchers, lecturers and postgraduate students in this area. The authors of the chapters are well-known researchers in the area of quality management.

This book offers a comprehensive reference guide to fuzzy statistics and fuzzy decision-making techniques. It provides readers with all the necessary tools for making statistical inference in the case of incomplete information or insufficient data, where classical statistics cannot be applied. The respective chapters, written by prominent researchers, explain a wealth of both basic and advanced concepts including: fuzzy probability distributions, fuzzy frequency distributions, fuzzy Bayesian inference, fuzzy mean, mode and median, fuzzy dispersion, fuzzy p-value, and many others. To foster a better understanding, all the chapters include relevant numerical examples or case studies. Taken together, they form an excellent reference guide for researchers, lecturers and postgraduate students pursuing research on fuzzy statistics. Moreover, by extending all the main aspects of classical statistical decision-making to its fuzzy counterpart, the book presents a dynamic snapshot of the field that is expected to stimulate new directions, ideas and developments.

This book offers a multifaceted perspective on fuzzy set theory, discussing its developments over the last 50 years. It reports on all types of fuzzy sets, from ordinary to hesitant fuzzy sets, with each one explained by its own developers, authoritative scientists well known for their previous works. Highlighting recent theorems and proofs, the book also explores how fuzzy set theory has come to be extensively used in almost all branches of science, including the health sciences, decision science, earth science and the social sciences alike. It presents a wealth of real-world sample applications, from routing problem to robotics, and from agriculture to engineering. By offering a comprehensive, timely and detailed portrait of the field, the book represents an excellent reference guide for researchers, lecturers and postgraduate students pursuing research on new fuzzy set extensions.

This book presents recently developed intelligent techniques with applications and theory in the area of quality management. The involved applications of intelligence include techniques such as fuzzy sets, neural networks, genetic algorithms, etc. The book consists of classical quality management topics dealing with intelligent techniques for solving the complex quality management problems. The book will serve as an excellent reference for quality managers, researchers, lecturers and postgraduate students in this area. The authors of the chapters are well-known researchers in the area of quality management.

This book presents recently developed intelligent techniques with applications and theory in the area of engineering management. The involved applications of intelligent techniques such as neural networks, fuzzy sets, Tabu search, genetic algorithms, etc. will be useful for engineering managers, postgraduate students, researchers, and lecturers. The book has been written considering the contents of a classical engineering management book but intelligent techniques are used for handling the engineering management problem areas. This comprehensive characteristics of the book makes it an excellent reference for the solution of complex problems of engineering management. The authors of the chapters are well-known researchers with their previous works in the area of engineering management.

Industrial engineering is a branch of engineering dealing with the optimization of complex processes or systems. It is concerned with the development, improvement, implementation and evaluation of production and service systems. Computational Intelligence Systems find a wide application area in industrial engineering: neural networks in forecasting, fuzzy sets in capital budgeting, ant colony optimization in scheduling, Simulated Annealing in optimization, etc. This book will include most of the application areas of industrial engineering through these computational intelligence systems. In the literature, there is no book including many real and practical applications of Computational Intelligence Systems from the point of view of Industrial Engineering. Every chapter will include explanatory and didactic applications. It is aimed that the book will be a main source for MSc and PhD students.