# Multiple Criteria Decision Analysis: An Integrated Approach

Multiple Criteria Decision Analysis: An Integrated Approach

By Valerie Belton and Theodor J. Stewart

The field of multiple criteria decision analysis has developed rapidly over the past quarter century, and in the process a number of divergent schools of thought have emerged. "Multiple Criteria Decision Analysis: An Integrated Approach" provides a comprehensive overview of the main streams of thought within MCDA.

The book's two principal aims are: 1. to provide sufficient awareness of the underlying philosophies and theories, understanding of the practical detail of the methods, and insight into practice to enable researchers, students and industry practitioners to implement MCDA methods in an informed manner; and 2. to develop an integrated view of MCDA, incorporating both integration of different schools of thought within MCDA and integration of MCDA with broader management theory, science and practice, thereby informing the development of theory and practice across these areas.

The book should be of value to practicing decision analysts or graduate students in MCDA; operations researchers or graduate students in OR/MS; and managers or management students who need to understand MCDA.

Genetic Algorithms and Fuzzy Multiobjective Optimization

By Masatoshi Sakawa
Since the introduction of genetic algorithms in the 1970s, an enormous number of articles and books have been published on this methodology. As a result, genetic algorithms have made a major contribution to optimization, adaptation and learning in a wide variety of unexpected fields. Over the years, many excellent books in genetic algorithm optimization have been published; however, they focus mainly on single-objective discrete or other hard optimization problems under certainty.

"Genetic Algorithms And Fuzzy Multiobjective Optimization" introduces the latest advances in the field of genetic algorithm optimization for 0-1 programming, integer programming, nonconvex programming, and job-shop scheduling problems under multiobjectiveness and fuzziness. In addition, the book treats a wide range of actual real-world applications. The theoretical material and applications place special stress on interactive decision-making aspects of fuzzy multiobjective optimization for human-centered systems in most realistic situations when dealing with fuzziness.

The intended readers of this book are senior undergraduate students, graduate students, researchers and practitioners in disciplines that deal with the subjects of multiobjective programming for discrete or other hard optimization problems under fuzziness. Real-world research applications are used throughout the book to illustrate the presentation. Examples include flexible scheduling in a machine center, operation planning of district heating and cooling plants, and coal purchase planning in an actual electric power plant.

Object-Oriented Discrete-Event Simulation with Java: A Practical Introduction

By Jose M. Garrido
This book introduces the application of the Java programming language in discrete-event simulation. In addition, the fundamental concepts and practical simulation techniques for modeling different types of systems to study their general behavior and their performance are introduced. The approaches applied are the process interaction approach to discrete-event simulation and object-oriented modeling. Java is used as the implementation language and UML as the modeling language.

The book concentrates on object-oriented modeling and implementation aspects of simulation models using Java and practical simulation techniques. In addition, the book illustrates the dynamic behavior of systems using the various simulation models as case studies.

Missing Data (Sage University Press Series. Quatitative Applications in the Social Sciences, No. 136)

By Paul D. Allison
Sooner or later anyone who does statistical analysis runs into problems with missing data in which information for some variables is missing for some cases. Why is this a problem? Because most statistical methods presume that every case has information on all the variables to be included in the analysis.

Using numerous examples and practical tips, this book offers a non-technical explanation of the standard methods for missing data (such as list-wise or case-wise deletion) as well as two newer methods: maximum likelihood and multiple imputation.

Anyone who has been relying on ad-hoc methods that are statistically inefficient or biased will find this book a welcome and accessible solution to their problems with handling missing data.

Monographs on Statistics and Applied Probability: Statistics in the 21st Century

Edited by Adrian E. Raftery, Martin A. Tanner and Martin T. Wells
Originally published in the Journal of the American Statistical Association, this collection of vignettes examines our statistical past, comments on our present and speculates on our future. Although the coverage is broad and the topics diverse, it reveals the essential intellectual unity of the field as we see the same themes recurring in different contexts. We see how the development of statistics has been driven by the unprecedented and still growing range of applications, by the explosion in computer technology, and by the new types of data that continue to emerge and advance the discipline.

Extensive author and subject indices and an introductory overview by the editors make "Statistics in the 21st Century" an important statistics reference not just for this discipline but for the many other fields that rely on its methods and results. Organized around major areas of application that lead up to vignettes on theory and methods, it forms a landmark record of the progress and perceived future of the discipline.

In-Depth Analysis of Linear Programming

By F.P. Vasilyev and A.Y. Ivanitskiy
Along with the traditional material concerning linear programming (the simplex method, the theory of duality, the dual simplex method), "In-Depth Analysis of Linear Programming" contains new results of research carried out by the authors. For the first time, the criteria of stability (in the geometrical and algebraic forms) of the general linear programming problem are formulated and proved.

New regularization methods based on the idea of extension of an admissible set are proposed for solving unstable (ill-posed) linear programming problems. In contrast to the well-known regularization methods, in the methods proposed in this book the initial unstable problem is replaced by a new, stable auxiliary problem. This is also a linear programming problem, which can be solved by standard finite methods.

In addition, the authors indicate the conditions imposed on the parameters of the auxiliary problem which guarantee its stability, and this circumstance advantageously distinguishes the regularization methods proposed in this book from the existing methods. In these existing methods, the stability of the auxiliary problem is usually only presupposed but is not explicitly investigated.

Efficiency in the Public Sector

Edited by Kevin J. Fox
Regardless of where we live, the management of the public sector impacts on our lives. Hence, we all have an interest, one way or another, in the achievement of efficiency and productivity improvements in the activities of the public sector. For a government agency that provides a public service, striving for unreasonable benchmark targets for efficiency may lead to a deterioration of service quality, along with an increase in stress and job dissatisfaction for public sector employees. Slack performance targets may lead to gross inefficiency, poor quality of service and low self-esteem for employees. In the case of regulation, inappropriate policies can lead to unprecedented disasters.

In all of these cases, efficient management is required, although it is often unclear how to assess this efficiency. In this volume, several authors consider various aspects and contexts of performance measurement. Hence, this volume represents a unique collection of advances in efficiency assessment for the public sector by leading researchers in the field.

Essays and Surveys in Metaheuristics

Edited by Celso C. Ribeiro and Pierre Hansen
The field of metaheuristics has been fast evolving in recent years. Techniques such as simulated annealing, tabu search, genetic algorithms, scatter search, greedy randomized adaptive search, variable neighborhood search, ant systems and their hybrids are currently among the most efficient and robust optimization strategies to find high-quality solutions to many real-life optimization problems.

A very large number of successful applications of metaheuristics are reported in the literature and spread throughout many books, journals and conference proceedings. A series of international conferences entirely devoted to the theory, applications and computational developments in metaheuristics has been attracting an increasing number of participants from universities and industry.

"Essays and Surveys in Metaheuristics" goes beyond the recent conference-oriented volumes in Metaheuristics, with its focus on surveys of recent developments of the main metaheuristics. Well-known specialists have written surveys on the following subjects: simulated annealing, noising methods, strategies for the parallel implementation of metaheuristics, greedy randomized adaptive search procedures, tabu search, variable neighborhood search, ant colonies and evolutionary algorithms. Several further essays address issues or variants of metaheuristics, as well as innovative or successful applications of metaheuristics to classical or new combinatorial optimization problems.

Analysis of Financial Time Series

By Ruey S. Tsay
This comprehensive book introduces the theory and applications of time series methods with an emphasis on statistical content and applications. It provides professionals with state-of-the-art methods for applying time series analysis to their work with real-life examples from financial markets. Provides broad, up-to-date coverage of material with real examples of financial applications.