Network Models and Optimization: Multiobjective Genetic Algorithm Approach: Decision Engineering
Autor Mitsuo Gen, Runwei Cheng, Lin Linen Limba Engleză Paperback – 22 oct 2010
| Toate formatele și edițiile | Preț | Express |
|---|---|---|
| Paperback (1) | 1188.09 lei 43-57 zile | |
| SPRINGER LONDON – 22 oct 2010 | 1188.09 lei 43-57 zile | |
| Hardback (1) | 1199.79 lei 43-57 zile | |
| SPRINGER LONDON – 31 iul 2008 | 1199.79 lei 43-57 zile |
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Specificații
ISBN-13: 9781849967464
ISBN-10: 1849967466
Pagini: 708
Ilustrații: XIV, 692 p.
Dimensiuni: 155 x 235 x 37 mm
Greutate: 0.98 kg
Ediția:Softcover reprint of hardcover 1st ed. 2008
Editura: SPRINGER LONDON
Colecția Springer
Seria Decision Engineering
Locul publicării:London, United Kingdom
ISBN-10: 1849967466
Pagini: 708
Ilustrații: XIV, 692 p.
Dimensiuni: 155 x 235 x 37 mm
Greutate: 0.98 kg
Ediția:Softcover reprint of hardcover 1st ed. 2008
Editura: SPRINGER LONDON
Colecția Springer
Seria Decision Engineering
Locul publicării:London, United Kingdom
Public țintă
ResearchCuprins
Multiobjective Genetic Algorithms.- Basic Network Models.- Logistics Network Models.- Communication Network Models.- Advanced Planning and Scheduling Models.- Project Scheduling Models.- Assembly Line Balancing Models.- Tasks Scheduling Models.- Advanced Network Models.
Notă biografică
Professor Mitsuo Gen is currently a professor of the Graduate School of Information, Production and Systems at Waseda University. He previously worked as a lecturer and professor at Ashikaga Institute of Technology. His research interests include genetic and evolutionary computation; fuzzy logic and neural networks; supply chain network design; optimization for information networks; and advanced planning and scheduling (APS).
Runwei Cheng is a Doctor of Engineering and currently works for JANA Solutions, Inc.
Lin Lin is currently a PhD candidate and research assistant at Waseda University, where he gained his MSc from the Graduate School of Information, Production and Systems. His research interests include hybrid genetic algorthims; neural networks; engineering optimization; multiobjective optimization; applications of evolutionary techniques; production and logistics; communication networks; image processing and pattern recognition; and parallel and distributed systems.
Runwei Cheng is a Doctor of Engineering and currently works for JANA Solutions, Inc.
Lin Lin is currently a PhD candidate and research assistant at Waseda University, where he gained his MSc from the Graduate School of Information, Production and Systems. His research interests include hybrid genetic algorthims; neural networks; engineering optimization; multiobjective optimization; applications of evolutionary techniques; production and logistics; communication networks; image processing and pattern recognition; and parallel and distributed systems.
Textul de pe ultima copertă
Network models are critical tools in business, management, science and industry. Network Models and Optimization: Multiobjective Genetic Algorithm Approach presents an insightful, comprehensive, and up-to-date treatment of multiple objective genetic algorithms to network optimization problems in many disciplines, such as engineering, computer science, operations research, transportation, telecommunication, and manufacturing.
Network Models and Optimization: Multiobjective Genetic Algorithm Approach extensively covers algorithms and applications, including shortest path problems, minimum cost flow problems, maximum flow problems, minimum spanning tree problems, travelling salesman and postman problems, location-allocation problems, project scheduling problems, multistage-based scheduling problems, logistics network problems, communication network problem, and network models in assembly line balancing problems, and airline fleet assignment problems.
Network Models and Optimization: Multiobjective Genetic Algorithm Approach can be used both as a student textbook and as a professional reference for practitioners in many disciplines who use network optimization methods to model and solve problems.
Network Models and Optimization: Multiobjective Genetic Algorithm Approach extensively covers algorithms and applications, including shortest path problems, minimum cost flow problems, maximum flow problems, minimum spanning tree problems, travelling salesman and postman problems, location-allocation problems, project scheduling problems, multistage-based scheduling problems, logistics network problems, communication network problem, and network models in assembly line balancing problems, and airline fleet assignment problems.
Network Models and Optimization: Multiobjective Genetic Algorithm Approach can be used both as a student textbook and as a professional reference for practitioners in many disciplines who use network optimization methods to model and solve problems.
Caracteristici
Presents an insightful, comprehensive, and up-to-date treatment of multiple objective genetic algorithms to network optimization problems Includes supplementary material: sn.pub/extras