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© 2008

Network Models and Optimization

Multiobjective Genetic Algorithm Approach

Book

Part of the Decision Engineering book series (DECENGIN)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Pages 49-134
  3. Pages 135-228
  4. Pages 551-606
  5. Pages 607-685
  6. Back Matter
    Pages 687-692

About this book

Introduction

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.

Keywords

Genetic Algorithms Logistics Models Manufacturing Network Models Optimization Scheduling Models algorithms logistics operations research scheduling

Authors and affiliations

  1. 1.Graduate School of Information, Production and Systems (IPS)Waseda UniversityFukukuokaJapan
  2. 2.JANA Solutions, Inc.TokyoJapan

About the authors

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.

Bibliographic information

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