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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Multiobjective Genetic Algorithm Approach Mitsuo Gen, Runwei Cheng, Lin Lin. commodity integral flow, Disjoint connecting paths, Maximum length-bounded disjoint paths, Maximum fixed-length disjoint paths, Unsplittable multicommodity flow ...
... flow assignment (CFA) problem [16], and the dynamic routing problem [17]. It is noted that all these problems can be formulated as some sort of a combinatorial optimization problem. 2.2.1. Mathematical. Formulation. of. the. SPP. Models.
... is formulated as follows, in which the objectives are minimizing cost function z1 and minimizing delay function z2 from source node 1 to sink node n: with constraints at Eqs. 2.2and2.6, a flow conservation law is 2.2 Shortest Path Model 59.
... flow conservation law is observed at each of the nodes other than s or t. That is, what goes out of node i, ∑nj=1 xij must be equal to what comes in, ∑nk=1 xki. 2.2.2 Priority-based GA for SPP Models Let P(t) and C(t) be parents and ...
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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 |