METODOLOGÍA BASADA EN LOS ALGORITMOS VEGA Y MOGA PARA SOLUCIONAR UN PROBLEMA MULTIOBJETIVO EN UN SISTEMA DE PRODUCCIÓN JOB SHOP (METHODOLOGY BASED ON THE ALGORITHMS VEGA AND MOGA TO SOLVE A MULTIOBJECTIVE PROBLEM IN A SYSTEM OF PRODUCTION JOB SHOP)
METODOLOGÍA BASADA EN LOS ALGORITMOS VEGA Y MOGA PARA SOLUCIONAR UN PROBLEMA MULTIOBJETIVO EN UN SISTEMA DE PRODUCCIÓN JOB SHOP (METHODOLOGY BASED ON THE ALGORITHMS VEGA AND MOGA TO SOLVE A MULTIOBJECTIVE PROBLEM IN A SYSTEM OF PRODUCTION JOB SHOP)
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En este artículo se presenta una metodología que pretende minimizar de forma simultánea, en un ambiente de producción tipo “job shop” correspondiente a una empresa metalmecánica, las siguientes variables: tiempo de proceso, costo de mano de obra directa y, asimismo la fracción defectuosa generada por la fatiga del operario. Con este propósito se fusionan elementos de los algoritmos genéticos Vega y Moga, desarrollando para el efecto las siguientes etapas: generar la población inicial, conformar la nueva población, realizar análisis de varianza y por último, comparar con un método híbrido entre sumas ponderadas y algoritmos genéticos.
De acuerdo con lo anterior, al evaluar el individuo de menor tiempo de proceso proveniente de la metodología basada en los algoritmos Vega y Moga, respecto al individuo de menor tiempo de desarrollo proveniente del método híbrido entre sumas ponderadas y algoritmos genéticos, se encuentra que el primero supera en desempeño al segundo así: en cuanto a la variable tiempo de proceso (en horas) en 27,86%; en cuanto a la variable tiempo de proceso (en semanas) en 1,25%; en cuanto a la variable costo de mano de obra directa (MOD) en 6,73% y, en cuanto a la variable fracción defectuosa en 25,85%.
Abstract: This paper presents a methodology that aims to minimize simultaneously, in a “Jo b Shop” production system the following variables: process time (makespan time), cost of direct labor and also the fraction defective generated by operator fatigue. For this purpose, are taken and fused elements of genetic algorithms Vega and Moga, through the following steps: generating the initial population, form the new population, obtaining the appropriate analysis of variance and finally compared with a hybrid method of weighted sums and genetic algorithms.
According to the above, when evaluating the solution faster processing time corresponding to the method based on algorithms Vega and Moga, respect to the solution faster processing time calculated from the method based on weighted sums and genetic algorithms, states that the first one exceeds the second performance as: for process time variable (in hours) at 27.86%, for variable in process time (in weeks) at 1.25%, in terms of the variable cost of direct labor in 6.73% and, as to the variable defective fraction in 25.85%.
Sumário:Neste artigo apresentamos uma metodologia que visa minimizar ao mesmo tempo, em um ambiente de produção tipo “job shop” para uma empresa de engenharia, as seguintes variáveis: tempo de processo, custo de mão de obra direta e também a fração defeituosa gerada pela fadiga do operador. Para este efeito, os elementos de fusível e algoritmos genéticos Moga Vega, desenvolvido para efectuar os seguintes passos: geração de uma população inicial, formam a nova população, a análise de variância e, finalmente, em comparação com um método híbrido e somas ponderadas algoritmos genéticos. De acordo com o exposto, o menor tempo individual processo de avaliação da metodologia baseada em algoritmos e Moga Vega, em comparação com o tempo de processamento menor do indivíduo a partir da soma ponderada método híbrido e de algoritmos genéticos, a primeira supera a segunda maneira: como a variável de tempo do processo (em horas) 27,86%, em termos de tempo variável de processo (em semanas) a 1,25%, em termos de custo variável mão de obra direta (MOD) em 6,73% e, como a fração defeituosa variável 25,85%.
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