Job Shop problem solution with an intelligent agent

This paper defines a new and effective methodology, based on intelligent agents, for the sequencing of production in Job Shop environments; especially, for small and medium enterprises in the metalworking sector, where these techniques have not been widely used; Due to the high resistance to change. This work is carried out in two phases. In the first, the different techniques used are defined. In the second, statistical tests are carried out in order to determine the percentage of approximation of these solutions to the optimal or sub-optimal solution. The result of this work shows that intelligent agent-based techniques do not always produce an optimal result; but in a few seconds, these techniques can find a suboptimal solution with an approximation of 97.81% and 90.43% to the optimal or suboptimal solution, in the variables total process time and total dead time, respectively. This contrasts with the little effectiveness found in traditional techniques.

• Autores: Danilo Castrillón, Omar; Alberto Giraldo, Jaime; Sarache, William Ariel
• Fecha de publicación: 2009
• Institución: Universidad EAFIT
• Repositorio: Repositorio EAFIT
• Idioma: spa
• OAI Identifier: oai:repository.eafit.edu.co:10784/14502
• Acceso en línea: http://hdl.handle.net/10784/14502
• Palabra clave: Process Time; Downtime; Intelligent Agents; Production Sequencing; Job Shop; Evolutionary Algorithms; Multi-Objective Optimization; Tiempo De Proceso; Tiempo Muerto; Agentes Inteligentes; Secuenciación De La Producción; Job Shop; Algoritmos Evolutivos; Optimización Multiobjetivo
• License: Copyright (c) 2009 Omar Danilo Castrillón, Jaime Alberto Giraldo, William Ariel Sarache