Network Models and Optimization: Multiobjective Genetic Algorithm Approach

Front Cover
Springer Science & Business Media, Jul 10, 2008 - Technology & Engineering - 692 pages

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.

From inside the book

What people are saying - Write a review

We haven't found any reviews in the usual places.

Selected pages


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

Other editions - View all

Common terms and phrases

Popular passages

Page 179 - A supply chain is a network of facilities and distribution options that performs the functions of procurement of materials, transformation of these materials into intermediate and finished products, and the distribution of these finished products to customers.
Page 14 - The method may work reasonably well when the feasible search space is convex and it constitutes a reasonable part of the whole search space.
Page 20 - This rule states that the ratio of successful mutations to all mutations should be 1/5, hence if the ratio is greater than 1/5 then increase the step size, and if the ratio is less than 1/5 then decrease the step size.
Page 94 - ... replacing the genes lost from the population during the selection process so that they can be tried in a new context or (b) providing the genes that were not present in the initial population.
Page 15 - GA have proved to be a versatile and effective approach for solving optimization problems. Nevertheless, there are many situations in which the simple GA does not perform particularly well, and various methods of hybridization have been proposed. One of most common forms of hybrid genetic algorithm (hGA) is to incorporate local optimization as an add-on extra to the canonical GA loop of recombination and selection.
Page 155 - Logistics management is that part of supply chain management that plans, implements, and controls the efficient, effective forward and reverse flow and storage of goods, Services, and related Information between the point of origin and the point of consumption in order to meet customers
Page 332 - Baker, 1974, p. 185), a non-delay schedule is a schedule where "no machine is kept idle at a time when it could begin processing some operation".
Page 1 - ... rejecting others so as to keep the population size constant. Fitter chromosomes have higher probabilities of being selected. After several generations, the algorithms converge to the best chromosome, which hopefully represents the optimum or suboptimal solution to the problem.
Page 11 - The genetic operators and their significance can now be explained. Crossover. Crossover is the main genetic operator. It operates on two individuals at a time and generates an offspring by combining schemata from both parents. A simple way to achieve crossover would be to choose a random cut point and generate the offspring by combining the segment of one parent to the left of the cut point with the segment of the other parent to the right of the cut point. This method works well with the bit string...
Page 12 - If it is too low, many genes that would have been useful are never tried out; but if it is too high, there will be much random perturbation, the offspring will start losing their resemblance to the parents, and the algorithm will lose the ability to learn from the history of the search.

About the author (2008)

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