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Non-Convex Multi-Objective Optimization: 123 (Springer Optimization and Its Applications, 123) - Tapa dura

Pardalos, Panos M.; Žilinskas, Antanas; Žilinskas, Julius

 
9783319610054: Non-Convex Multi-Objective Optimization: 123 (Springer Optimization and Its Applications, 123)
Tapa dura
ISBN 10: 3319610058 ISBN 13: 9783319610054
Editorial: Springer, 2017

Sinopsis

Recent results on non-convex multi-objective optimization problems and methods are presented in this book, with particular attention to expensive black-box objective functions. Multi-objective optimization methods facilitate designers, engineers, and researchers to make decisions on appropriate trade-offs between various conflicting goals. A variety of deterministic and stochastic multi-objective optimization methods are developed in this book. Beginning with basic concepts and a review of non-convex single-objective optimization problems; this book moves on to cover multi-objective branch and bound algorithms, worst-case optimal algorithms (for Lipschitz functions and bi-objective problems), statistical models based algorithms, and probabilistic branch and bound approach. Detailed descriptions of new algorithms for non-convex multi-objective optimization, their theoretical substantiation, and examples for practical applications to the cell formation problem in manufacturing engineering, the process design in chemical engineering, and business process management are included to aide researchers and graduate students in mathematics, computer science, engineering, economics, and business management.  

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Acerca del autor

Carlos Oliveira obtained a PhD in Operations Research from the University of Florida, a Masters in Computer Science from Universidade Federal do Ceará, Brazil, and a B.Sc. in Computer Science from Universidade Estadual do Ceará, Brazil. Carlos has spent more than ten years working on combinatorial optimization problems in several areas, including telecommunications, computational biology, and logistics. He has written more than 20 papers on optimization aspects of these areas. He is an associate editor for J. of Global Optimization and Optimization Letters. Carlos was assistant professor at Oklahoma State University from 2004 to 2006. Since then he has worked as a consultant in the areas of optimization and software engineering. He works in New York City and lives in New Jersey with his wife and son. Carlos Oliveira can be contacted at his web site http://coliveira.net. Panos Pardalos is Distinguished Professor of Industrial and Systems Engineering at the University of Florida. He is also affiliated faculty member of the Computer Science Department, the Hellenic Studies Center, and the Biomedical Engineering Program. He is also the director of the Center for Applied Optimization.Dr. Pardalos obtained a PhD degree from the University of Minnesota in Computer and Information Sciences. Dr. Pardalos is a world leading expert in global and combinatorial optimization. He is the editor-in-chief of the Journal of Global Optimization, Journal of Optimization Letters, and Computational Management Science. In addition, he is the managing editor of several book series, and a member of the editorial board of several international journals. He is the author of 8 books and the editor of several books. He has written numerous articles and developed several well known software packages. His research is supported by National Science Foundation and other government organizations. His recent research interests include network design problems, optimization intelecommunications, e-commerce, data mining, biomedical applications, and massive computing.

De la contraportada

Recent results on non-convex multi-objective optimization problems and methods are presented in this book, with particular attention to expensive black-box objective functions. Multi-objective optimization methods facilitate designers, engineers, and researchers to make decisions on appropriate trade-offs between various conflicting goals. A variety of deterministic and stochastic multi-objective optimization methods are developed in this book. Beginning with basic concepts and a review of non-convex single-objective optimization problems; this book moves on to cover multi-objective branch and bound algorithms, worst-case optimal algorithms (for Lipschitz functions and bi-objective problems), statistical models based algorithms, and probabilistic branch and bound approach. Detailed descriptions of new algorithms for non-convex multi-objective optimization, their theoretical substantiation, and examples for practical applications to the cell formation problem in manufacturing engineering, the process design in chemical engineering, and business process management are included to aide researchers and graduate students in mathematics, computer science, engineering, economics, and business management. 

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Editorial
Springer
Año de publicación
2017
Idioma
Inglés
ISBN 10 
3319610058
ISBN 13 
9783319610054
Encuadernación
Tapa dura
Número de edición
1
Número de páginas
192
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