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 72
... procedure. For many combinatorial optimization problems, it is relatively easy to create a repairing procedure. Liepins and his collaborators have shown, through empirical test of GA performance on a divers set of constrained ...
... procedure of rwGA is shown in Fig. 1.21 □ 2 2 z 1 z □ 1 □ 1 □ 1 △ △ 2 1 □ 1 □ 1 □ 1 △ 2 Fixed search ... procedure : the objective () of eachchromosome , 1,2,...,, ki i f v v k q i popSize input : fitness value (), i evalv i ...
... procedure showed in Fig. 1.25, the nsGA II adopt a special selection process. That is, between two individuals with nondomination rank ri and rj, and crowding distance di and dj, we prefer the solution with the lower (better) rank ...
... procedure of priority-based GA (priGA) for solving SPP models is outlined as follows: 2.2.2.1 Genetic Representation How to encode a solution of the. procedure: priGA for SPP models input: network data (N, A, c, d), GA parameters ...
You have reached your viewing limit for this book.
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 |