Network Models and Optimization: Multiobjective Genetic Algorithm Approach

Name: Network Models and Optimization: Multiobjective Genetic Algorithm Approach
Brand: Springer
SKU: 9781849967464
Price: 14553.0 INR
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by Mitsuo Gen

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ISBN13: 9781849967464
Binding: Paperback
Subject: N/A
Publisher: Springer
Publisher Imprint: Springer
Publication Date: October 22, 2010
Pages: 692
Original Price: EUR 199.99
Language: English
Edition: N/A
Item Weight: 1051 grams
BISAC Subject(s): Industrial Engineering, Operations Research, and Computer Science

About The Book

From the Back Cover

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.

About The Author

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.

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