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 86
... Selection and Sequencing. . . . . . 50 2.1.2 SpanningTreeModel:ArcSelection.................... 51 2.1.3 Maximum Flow Model: Arc Selection and Flow Assignment 52 2.1.4 RepresentingNetworks ............................... 53 2.1.5 ...
... Selection Model . . . . . 355 5.4.1 MathematicalFormulationofiOS/RS ...................358 5.4.2 Multistage Operation-based GA for iOS/RS . . . ...........363 5.4.3 ExperimentandDiscussions...........................372 5.5 Integrated ...
... selection and natural genetics. GA, differing from conventional search techniques, start with an initial set of random solutions called population satisfying boundary and/or system constraints to the problem. Each individual in the ...
... selection, etc.). 5. Parameter values that genetic algorithm uses (population size, probabilities of applying genetic operators, etc.). Initial solutions 1100101010 1011101110 . . . 0011011001 1100110001 t←0 encoding 1100101010 ...
... selection work on the phenotype space. Natural selection is the link between chromosomes and the performance of the decoded solutions. The mapping from the genotype space to the phenotype space has a considerable influence on the ...
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 |