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. |
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... shows a general structure of GA. Let P(t) andC(t) be parents and offspring in current generation t, respectively and the general implementation structure of GA is described as follows: procedure:basicGA input: problem data, GA ...
... shows an illustration of spEA for a maximization problem with two objectives. The procedure of spEA is shown in Fig. 1.23 2 z (a) 2 z Fig. 1.22 Illustration of spEA (maximization case) (b) Adaptive-weight Genetic Algorithm (awGA: Gen ...
... show in Fig. +z 1 z 2 z min 2 z max 1 z max 2 z min 1 z subspace corresponding to current solutions adaptive moving line minimal rectangle containing all current solutions maximum extreme point minimum extreme point −z ) , ( max 2 min ...
... show that an already known NP-complete problem reduces to A. A consequence of this definition is that if we had a polynomial time algorithm for C, we could solve all problems in NP in polynomial time. This definition was given by Cook ...
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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 |