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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... example of a strategy which exploits the best solution for possible improvement, ignoring the exploration of the search space. Random search is an example of a strategy which explores the search space, ignoring the exploitation of the ...
... example of a random strategy which explores the search space ignoring the exploitation of the promising regions of the search space. The algorithm is modified to (1) include immigration routine, in each generation, (2) generate and (3) ...
... example of an instance that is n bits long that can be solved in n2 steps. In this example we say the problem has a time complexity of n2. Of course, the exact number of steps will depend on exactly what machine or language is being ...
... Example 2.1: A Simple Example of SPP A simple example of shortest path problem is shown in Fig. 2.3, and Table 2.1gives the data set of the example. Table 2.1 The data set of illustrative shortest path problem Fig. 2.3 A simple example ...
... example of the variable-length chromosome and its decoded path are shown in Fig. 2.4, in terms of the directed network in Fig. 2.3. Fig. 2.4 An example of variable-length 16311 path : 58 chromosome and its decoded path Fixed-length ...
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 |