Artificial intelligence effectiveness in job shop environments

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.

• Autores: Castrillón Gómez, Ómar Danilo; Sarache Castro, William Ariel; Giraldo García, Jaime Alberto
• Tipo de recurso: Article of journal
• Fecha de publicación: 2011
• Institución: Universidad Nacional de Colombia
• Repositorio: Universidad Nacional de Colombia
• Idioma: spa
• OAI Identifier: oai:repositorio.unal.edu.co:unal/38003
• Acceso en línea: https://repositorio.unal.edu.co/handle/unal/38003; http://bdigital.unal.edu.co/28088/
• Palabra clave: Makespan time; idle time; evolutionary algorithms; taboo search; data mining; random techniques
• Rights: openAccess
• License: Atribución-NoComercial 4.0 Internacional