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Population heuristics present native abilities for solving optimization problems with multiple objectives. Using evolution and adaptation mechanisms, a population of individuals evolves in order to describe a good approximation of the efficient solutions. The resolution of the 1 | | (EC,- ,Tmaar) permutation scheduling problems, for which an exact algorithm is available, is investigated using a population heuristic based on genetic algorithms. The aim here is not to put in competition a heuristic method with an exact method but to give an experimental feedback on the resolution abilities of biobjective permutation scheduling problems with a population heuristic. The paper reports the aspects analyzed in this study : first, the pertinence of using genetic information into a population algorithm and second, a detailed multicriteria analysis of efficient solutions for this class of scheduling problems.
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Tom
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23--50
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Bibliogr. 41 poz.
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- Osaka Prefecture University, 1-1 Gakuen-cho, Sakai, Osaka 599-8231 - JAPAN, morita@eco.osakafu-u.ac.jp
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bwmeta1.element.baztech-article-BPP1-0018-0066