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Artificial intelligence effectiveness in job shop environments

CASTRILLON, OMAR and SARACHE, WILLIAM and GIRALDO, JAIME (2011) Artificial intelligence effectiveness in job shop environments. Dyna; Vol. 78, núm. 168 (2011); 149-157 DYNA; Vol. 78, núm. 168 (2011); 149-157 2346-2183 0012-7353 .

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URL oficial: http://revistas.unal.edu.co/index.php/dyna/article...

Resumen

The aim of this paper is to define a new methodology that allows the comparison of the effectiveness among some of the major artificial intelligence techniques (random technique, taboo search, data mining, evolutionary algorithms). This methodology is applied in the sequencing production process in job shop environments, in a problem with N orders, and M machines, where each of the orders must pass through every machine regardless of its turn. These techniques are measured by the variables of total makespan time, total idle time, and machine utilization percentage. Initially, a theoretical review was conducted and showed the usefulness and effectiveness of artificial intelligence in the sequencing production processes. Subsequently and based on the experiments presented, the obtained results showed that these techniques have an effectiveness higher than 95%, with a confidence interval of 99.5% measured by the variables under study.

Tipo de documento:Artículo - Article
Información adicional:Derechos de autor reservados
Palabras clave:Makespan time, idle time, evolutionary algorithms, taboo search, data mining, random techniques
Unidad administrativa:Revistas electrónicas UN > Dyna
Código ID:28088
Enviado por : Dirección Nacional de Bibliotecas STECNICO
Enviado el día :27 Junio 2014 19:16
Ultima modificación:27 Noviembre 2017 22:20
Ultima modificación:27 Noviembre 2017 22:20
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