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This paper starts by studying the performance of two interrelated genetic algorithms (GA) for the static Single Machine Scheduling Problem (SMSP). One is a single start GA, the other, called MetaGA, is a multi-start version GA. The performance is evaluated for total weighted tardiness, on the basis of the quality of scheduling solutions obtained for a limit on computation time. Then, a scheduling system, based on Genetic Algorithms is proposed, for the resolution of the dynamic version of the same problem. The approach used adapts the resolution of the static problem to the dynamic one in which changes may occur continually. This takes into account dynamic occurrences in a system and adapts the current population to a new regenerated population
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Contexto Educativo
Citação
Madureira, A., Ramos, C., do Carmo Silva, S. (2000). A Genetic Algorithm for the Dynamic Single Machine Scheduling Problem. In: Camarinha-Matos, L.M., Afsarmanesh, H., Erbe, HH. (eds) Advances in Networked Enterprises. BASYS 2000. IFIP — The International Federation for Information Processing, vol 53. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-35529-0_30
Editora
Springer
Licença CC
Sem licença CC