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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... Edge Sets: An Effective Evolutionary Coding of Spanning Trees, IEEE Transactions on Evolutionary Computation, 7(3), 225–239. 26. Kim, J. H. & Myung, H. (1996). A two-phase evolutionary programming for general constrained optimization ...
... Edge Lists A directed graph (or digraph) G on the nodes set N = {1,2,···,n} is specified by: 1. Its number of nodes n; 2. The list of its arcs, given as a sequence of ordered m pairs A={(i,j), (k,l), ···, (s,t)}. The digraph G of Fig ...
... edge-cost flow, Integral flow with multipliers, Path constrained network flow, Integral flow with homologous arcs, Integral flow with bundles, Undirected flow with lower bounds, Directed two-commodity integral flow, Undirected ...
... edge weights may be negative). Dijkstra's algorithm accomplishes the same problem with a lower running time, but requires edge weights to be nonnegative. Thus, Bellman-Ford is usually used only when there are negative edge weights ...
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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 |