𝔖 Scriptorium
✦   LIBER   ✦

πŸ“

Network Models and Optimization: Multiobjective Genetic Algorithm Approach

✍ Scribed by Mitsuo Gen, Runwei Cheng, Lin Lin (auth.)


Publisher
Springer-Verlag London
Year
2008
Tongue
English
Leaves
701
Series
Decision engineering
Edition
1
Category
Library

⬇  Acquire This Volume

No coin nor oath required. For personal study only.

✦ Synopsis


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.

✦ Table of Contents


Front Matter....Pages i-xiv
Multiobjective Genetic Algorithms....Pages 1-47
Basic Network Models....Pages 49-134
Logistics Network Models....Pages 135-228
Communication Network Models....Pages 229-295
Advanced Planning and Scheduling Models....Pages 297-417
Project Scheduling Models....Pages 419-476
Assembly Line Balancing Models....Pages 477-550
Tasks Scheduling Models....Pages 551-606
Advanced Network Models....Pages 607-685
Back Matter....Pages 687-692

✦ Subjects


Engineering Economics, Organization, Logistics, Marketing; Combinatorics; Operations Research/Decision Theory; Algorithm Analysis and Problem Complexity


πŸ“œ SIMILAR VOLUMES


Network models and optimization: multiob
✍ Mitsuo Gen, Runwei Cheng, Lin Lin (auth.) πŸ“‚ Library πŸ“… 2008 πŸ› Springer-Verlag London 🌐 English

<p><P>Network models are critical tools in business, management, science and industry. <EM>Network Models and Optimization: Multiobjective Genetic Algorithm Approach</EM> presents an insightful, comprehensive, and up-to-date treatment of multiple objective genetic algorithms to network optimization

Genetic algorithms and fuzzy multiobject
✍ Masatoshi Sakawa πŸ“‚ Library πŸ“… 2002 πŸ› Kluwer Academic Publishers 🌐 English

Since the introduction of genetic algorithms in the 1970s, an enormous number of articles together with several significant monographs 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

Optimization of computer networks: model
✍ Marino, Pablo Pavon πŸ“‚ Library πŸ“… 2016 πŸ› Wiley 🌐 English

This book covers the design and optimization of computer networks applying a rigorous optimization methodology, applicable to any network technology. It is organized into two parts. In Part 1 the reader will learn how to model network problems appearing in computer networks as optimization programs,

Optimization of Computer Networks: Model
✍ Pablo Pav?n Mari?o πŸ“‚ Library πŸ“… 2016 πŸ› Wiley 🌐 English

<p>This book covers the design and optimization of computer networks applying a rigorous optimization methodology, applicable to any network technology.Β  It is organized into two parts. In Part 1 the reader will learn how to model network problems appearing in computer networks as optimization progr

Nonlinear Multiobjective Optimization: A
✍ Claus Hillermeier (auth.) πŸ“‚ Library πŸ“… 2001 πŸ› BirkhΓ€user Basel 🌐 English

<p><P>Arguably, many industrial optimization problems are of the multiobjective type. The present work, after providing a survey of the state of the art in multiobjective optimization, gives new insight into this important mathematical field by consequently taking up the viewpoint of differential ge