Mass Customization: A Supply Chain Approach is a text on the emerging topic of mass customization in manufacturing. The contributed chapters in this book provide a unified treatment to the topic by offering coverage in four main categories - concepts and current state of research; problem solving frameworks, models, and methodologies; supportive techniques and technologies for enabling mass customization; and future research agenda. The book blends theory and practice and includes prototypical applications to illustrate this complex, yet emerging field of inquiry.

This volume is an interdisciplinary book which introduces, in a very readable way, state-of-the-art research in the fundamental topics of mathematical modelling of Biosystems. In short, the book offers an overview of mathematical and computational modelling of biosystems including biological phenomena in general. There is also a special introduction to Protein Physics which aims to explain the all-or-none first order phase transitions from native to denatured states.

Interest in constrained optimization originated with the simple linear pro­ gramming model since it was practical and perhaps the only computationally tractable model at the time. Constrained linear optimization models were soon adopted in numerous application areas and are perhaps the most widely used mathematical models in operations research and management science at the time of this writing. Modelers have, however, found the assumption of linearity to be overly restrictive in expressing the real-world phenomena and problems in economics, finance, business, communication, engineering design, computational biology, and other areas that frequently demand the use of nonlinear expressions and discrete variables in optimization models.

Motivation This book is based on the view-tx)int that both public and private decision making, in practice, can often be ilrproved upon by means of fonnal (nonnative) decision nodels and methods. To sane extent, the validity of this statement can be measured by the irrpressive number of su=esses of disciplines as operations research and management science.

Assignment and matching problems belang to those cornbinatorial opti- mization problems which are weIl understood in theory and have many applications in practice. Since a research group qf the Mathematical Institute in Cologne has worked in this field for several years, we feIt that a publication of the developed procedures and algorithms would be useful, both for further research and academic tu': tion as weIl as for applications in practice.

Integer Prograw~ing is one of the most fascinating and difficult areas in the field of Mathematical Optimization. Due to this fact notable research contributions to Integer Programming have been made in very different branches of mathematics and its applications. Since these publications are scattered over many journals, proceedings volumes, monographs, and working papers, a comprehensive bibliography of all these sources is a helpful tool even for specialists in this field. I initiated this compilation of literature in 1970 at the Institut fur ~konometrie und Operations Research, University of Bonn. Since then many collaborators have contributed to and worked on it. Among them Dipl.-Math.

This book is intended to provide an introductory text of Nonlinear and Dynamic Programming for students of managerial economics and operations research. The author also hopes that engineers, business executives, managers, and others responsible for planning of industrial operations may find it useful as a guide to the problems and methods treated, with a view to practical applications. The book may be considered as a sequel to the author's Linear Programming in Industry (1960, 4th revised and enlarged edition 1974), but it can be used independently by readers familiar with the elements of linear programming models and techniques. The two volumes con­ stitute an introduction to the methods of mathematical programming and their application to industrial optimization problems. The author feels that the vast and ever-increasing literature on mathematical programming has not rendered an introductory exposition super­ fluous. The general student often tends to feel somewhat lost if he goes straight to the special literature; he will be better equipped for tackling real problems and using computer systems if he has acquired some previous training in constructing small-scale programming models and applying standard algorithms for solving them by hand. The book is intended to provide this kind of training, keeping the mathematics at the necessary minimum. The text contains numerous exercises. The reader should work out these problems for himself and check with the answers given at the end of the book. The text is based on lectures given at the University of Copenhagen.

This book discusses machine learning and artificial intelligence (AI) for agricultural economics. It is written with a view towards bringing the benefits of advanced analytics and prognostics capabilities to small scale farmers worldwide. This volume provides data science and software engineering teams with the skills and tools to fully utilize economic models to develop the software capabilities necessary for creating lifesaving applications.

This book serves as an advanced text for a graduate course on stochastic algorithms for the students of probability and statistics, engineering, economics and machine learning. This second edition gives a comprehensive treatment of stochastic approximation algorithms based on the ordinary differential equation (ODE) approach which analyses the algorithm in terms of a limiting ODE. It has a streamlined treatment of the classical convergence analysis and includes several recent developments such as concentration bounds, avoidance of traps, stability tests, distributed and asynchronous schemes, multiple time scales, general noise models, etc., and a category-wise exposition of many important applications. It is also a useful reference for researchers and practitioners in the field.

This book moves beyond a general introduction to Product Lifecycle Management (PLM) and its basic elements to provide a more in-depth analysis of the subjects introduced in Volume 1 (Product Lifecycle Management: 21st Century Paradigm for Product Realisation).

Optimal Control and Dynamic Games has been edited to honor the outstanding contributions of Professor Suresh Sethi in the fields of Applied Optimal Control. Professor Sethi is internationally one of the foremost experts in this field. He is, among others, co-author of the popular textbook "Sethi and Thompson: Optimal Control Theory: Applications to Management Science and Economics".

Optimal control and differential games continue to attract strong interest from researchers interested in dynamical problems and models in management science. This volume explores the application of these methodologies to new as well as to classical decision problems in management sciences and economics. In Part I, optimal control and dynamical systems approaches are used to analyze problems in areas such as monetary policy, pollution control, relationship marketing, drug control, debt financing, and ethical behavior. In Part II differential games are applied to problems such as oligopolistic competition, common resource management, spillovers in foreign direct investments, marketing channels, incentive strategies, and the computation of Markov perfect Nash equilibria.

Decision & Control in Management Science analyzes emerging decision problems in the management and engineering sciences. It is divided into five parts. The first part explores methodological issues involved in the optimization of deterministic and stochastic dynamical systems. The second part describes approaches to the model energy and environmental systems and draws policy implications related to the mitigation of pollutants. The third part applies quantitative techniques to problems in finance and economics, such as hedging of options, inflation targeting, and equilibrium asset pricing. The fourth part considers a series of problems in production systems. Optimization methods are put forward to provide optimal policies in areas such as inventory management, transfer-line, flow-shop and other industrial problems. The last part covers game theory. Chapters range from theoretical issues to applications in politics and interactions in franchising systems.

Since its inception 20 years ago the theory of fuzzy sets has advanced in a variety of ways and in many disciplines. Applications of this theory can be found in artificial intelligence, computer science, control engineering, decision theory, expert systems, logic, management science, operations research, pattern recognition, robotics and others. Theoretical advances have been made in many directions. In fact it seems extremely difficult for a newcomer to the field or for somebody who wants to apply fuzzy set theory to his problems to recognize properly the present "state of the art." Therefore, many applications use fuzzy set theory on a much more elementary level than appropriate and necessary. On the other hand, theoretical publications are already so specialized and assume such a background in fuzzy set theory that they are hard to understand.