Network Models and Optimization: Multiobjective Genetic Algorithm ApproachNetwork 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
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... function. The methods are applied to a single point in the search space. The point is then improved along the deepest descending direction gradually through iterations as shown in Fig. 1.2. This point-to-point approach embraces the ...
... functions and any kind of constraints, i.e., linear or nonlinear, defined on discrete, continuous or mixed search spaces ... function for evaluating solutions should be given. (i.e., decoding); third, genetic operators such as crossover ...
... function optimization problems is known to have severe drawbacks due to the existence of Hamming cliffs, which ... function optimization problems. It has been widely confirmed that the real number encoding has higher performance than the ...
... function subject to the problem constraints. In general, the objective function provides the mechanism evaluating each individual. However, its range of values varies from problem to problem. To maintain uniformity over various problem ...
... function. The direction can be given randomly as a free direction to avoid the chromosomes jamming into a corner. If the chromosome is near the boundary, the mutation direction given by some criteria might point toward the close ...
Contents
1 | |
49 | |
Logistics Network Models | 135 |
Communication Network Models | 229 |
Advanced Planning and Scheduling Models | 297 |
Project Scheduling Models | 419 |
Assembly Line Balancing Models | 477 |
Tasks Scheduling Models | 551 |
References | 604 |
Index | 687 |
Other editions - View all
Network Models and Optimization: Multiobjective Genetic Algorithm Approach Mitsuo Gen,Runwei Cheng,Lin Lin No preview available - 2008 |
Network Models and Optimization: Multiobjective Genetic Algorithm Approach Mitsuo Gen,Runwei Cheng,Lin Lin No preview available - 2010 |