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
Results 1-5 of 82
... computation and its ap- plication fields such as C&IE (International Conference on Computers and Industrial Engineering) in Japan 1994 and in Korea 2004, IMS (International Conference on Information and Management Science) in 2001 with ...
... computational steps which asymptotically converge to optimalsolution. Most classical optimization methods generate a deterministic sequence of computation based on the gradient or higher order derivatives of objective function. The ...
... computational effort than other conventional heuristics. 3. Flexibility: GA provides us great flexibility to hybridize with domain-dependent heuristics to make an efficient implementation for a specific problem. 1.2. Implementation. of.
... computation t←t+1 crossover mutation CC(t) CM(t) hill-climbing CL(t) decoding decoding Kennedy gave an explanation of hGA with Lamarckian evolution theory. Fig. 1.5 The general structure of hybrid genetic algorithms Fig. 1.6 Applying a ...
... computational complexity, nonelitism approach, and the need for specifying a sharing parameter [59]. The nsGA II was advanced from its origin, nsGA. In nsGA II as shown in Fig. 1.26, a nondominated sorting approach is used for each ...
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 |