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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Multiobjective Genetic Algorithm Approach Mitsuo Gen, Runwei Cheng, Lin Lin. Chapter. 1. Multiobjective. Genetic. Algorithms. Many real-world problems from operations research (OR) / management science (MS) are very complex in nature and ...
Multiobjective Genetic Algorithm Approach Mitsuo Gen, Runwei Cheng, Lin Lin. others so as to keep the population size constant. Fitter chromosomes have higher probabilities of being selected. After several generations, the algorithms ...
... genetic algorithm Fig. 1.2 Comparison of conventional and genetic approaches. 1.1.4. Major. Advantages. GA have received considerable attention regarding their potential as a novel optimization technique. There are three major advantages ...
Multiobjective Genetic Algorithm Approach Mitsuo Gen, Runwei Cheng, Lin Lin. 1. Adaptability: GA does not have much mathematical requirement regarding about the optimization problems. Due to the evolutionary nature, GA will search for ...
Multiobjective Genetic Algorithm Approach Mitsuo Gen, Runwei Cheng, Lin Lin. Table 1.1 Explanation of GA terms Genetic algorithms Explanation Chromosome (string, individual) Solution (coding) Genes (bits) Part of solution Locus Position ...
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 |