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Evolutionary multiobjective optimization in engineering management: an empirical study on bridge deck rehabilitation
There exist multiple objectives in engineering management such as minimum cost and maximum service capacity. Although solution methods of multiobjective optimization problems have undergone continual development over the past several decades, the methods available to date are not particularly robust, and none of them performs well on the broad classes. Because genetic algorithms work with a population of points, they can capture a number of solutions simultaneously, and easily incorporate the concept of Pareto optimal set in their optimization process. In this paper, a genetic algorithm is modified to deal with the rehabilitation planning of bridge decks at a network level by minimizing the rehabilitation cost and deterioration degree simultaneously.
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Event
International Conference on Parallel and Distributed Computing Applications and Technologies (2005 : Dalian, China)Pagination
773 - 777Publisher
IEEE Computer SocietyLocation
Dalian, ChinaPlace of publication
Los Alamitos, CAStart date
2005-12-05End date
2005-12-08ISBN-13
9780769524054ISBN-10
0769524052Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2005, IEEE Computer SocietyEditor/Contributor(s)
H Shen, K NakanoTitle of proceedings
PDCAT 2005 : Sixth International Conference on Parallel and Distributed Computing Applications and Technologies (PDCAT'05), 2005 proceedingsUsage metrics
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