The book contains ten chapters as follows, Prepare Knowledge, Regression and Self-regression Models with Fuzzy Coefficients; Regression and Self-regression Models with Fuzzy Variables, Fuzzy Input/output Model, Fuzzy Cluster Analysis and Fuzzy Recognition, Fuzzy Linear Programming, Fuzzy Geometric Programming, Fuzzy Relative Equation and Its Optimizing, Interval and Fuzzy Differential Equations and Interval and Fuzzy Functional and Their Variation. It can not only be used as teaching materials or reference books for under-graduates in higher education, master graduates and doctor graduates in the courses of applied mathematics, computer science, artificial intelligence, fuzzy information process and automation, operations research, system science and engineering, and the like, but also serves as a reference book for researchers in these fields, particularly, for researchers in soft science.

Monte Carlo Methods in Fuzzy Optimization is a clear and didactic book about Monte Carlo methods using random fuzzy numbers to obtain approximate solutions to fuzzy optimization problems. The book includes various solved problems such as fuzzy linear programming, fuzzy regression, fuzzy inventory control, fuzzy game theory, and fuzzy queuing theory. The book will appeal to engineers, researchers, and students in Fuzziness and applied mathematics.

The book gives readers a thorough understanding of fuzzy geometric programming, a field that was originated by the author. It is organized into two parts: theory and applications. The former aims at development of issues including fuzzy posynomial geometric programming and its dual form, a fuzzy reverse posynomial geometric programming and its dual form and a geometric programming model with fuzzy coefficients and fuzzy variables. The latter is intended to discuss problems in applications, including antinomy in fuzzy geometric programming, as well as practical examples from the power of industry and the administration of postal services. Audience: Researchers, doctoral and post-doctoral students working in fuzzy mathematics, applied mathematics, engineering, operations research, and economics.

Optimization is an extremely important area in science and technology which provides powerful and useful tools and techniques for the formulation and solution of a multitude of problems in which we wish, or need, to to find a best possible option or solution. The volume is divided into a coupe of parts which present various aspects of fuzzy optimization, some related more general issues, and applications.

Fuzzy Multicriteria Decision-Making: Models, Algorithms and Applications addresses theoretical and practical gaps in considering uncertainty and multicriteria factors encountered in the design, planning, and control of complex systems. Including all prerequisite knowledge and augmenting some parts with a step-by-step explanation of more advanced concepts, the authors provide a systematic and comprehensive presentation of the concepts, design methodology, and detailed algorithms. These are supported by many numeric illustrations and a number of application scenarios to motivate the reader and make some abstract concepts more tangible. Fuzzy Multicriteria Decision-Making: Models, Algorithms and Applications will appeal to a wide audience of researchers and practitioners in disciplines where decision-making is paramount, including various branches of engineering, operations research, economics and management; it will also be of interest to graduate students and senior undergraduate students in courses such as decision making, management, risk management, operations research, numerical methods, and knowledge-based systems.

Production engineering and management involve a series of planning and control activities in a production system. A production system can be as small as a shop with only one machine or as big as a global operation including many manufacturing plants, distribution centers, and retail locations in multiple continents. The product of a production system can also vary in complexity based on the material used, technology employed, etc. Every product, whether a pencil or an airplane, is produced in a system which depends on good management to be successful. Production management has been at the center of industrial engineering and management science disciplines since the industrial revolution. The tools and techniques of production management have been so successful that they have been adopted to various service industries, as well. The book is intended to be a valuable resource to undergraduate and graduate students interested in the applications of production management under fuzziness. The chapters represent all areas of production management and are organized to reflect the natural order of production management tasks. In all chapters, special attention is given to applicability and wherever possible, numerical examples are presented. While the reader is expected to have a fairly good understanding of the fuzzy logic, the book provides the necessary notation and preliminary knowledge needed in each chapter.

This volume is a collection of fourteen papers, written by different authors and co-authors (listed in the order of the papers): N. Radwan, M. Badr Senousy, A. E. D. M. Riad, Chunfang Liu, YueSheng Luo, J. M. Jency, I. Arockiarani, P. P. Dey, S. Pramanik, B. C. Giri, N. Shah, A. Hussain, Gaurav, M. Kumar, K. Bhutani S. Aggarwal, V. Pătraşcu, F. Yuhua, S. Broumi, A. Bakali, M. Talea, F. Smarandache, M. Khan, S. Afzal, H. E. Khalid, M. A. Baset ,I. M. Hezam.