Examinando por Materia "JOB SHOP"

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  • Miniatura
    PublicaciónAcceso abierto
    Un algoritmo genético híbrido y un enfriamiento simulado para solucionar el problema de programación de pedidos Job Shop
    (2013-12-17) Meisel-Donoso, José David; Prado, L. K. (Liliana Katherine)
    Job Shop Scheduling Problem (JSP), classified as NP-Hard, has been a challenge for the scientific community because achieving an optimal solution to this problem is complicated as it grows in number of machines and jobs. Numerous techniques, including metaheuristics, have been used for its solution; however, the efficiency of the techniques, in terms of computational time, has not been very satisfactory. Because of this and for contributing to the solution of this problem, a simulated annealing (SA) and an improved genetic algorithm (IGA) have been proposed. The latter, by implementing a strategy of simulated annealing in the mutation phase, allows the algorithm to enhance and diversify the solutions at the same time, in order not to converge prematurely to a local optimum. The results showed that the proposed algorithms yield good results with deviations around the best values found not exceeding 5 % for more complex problems.
  • Miniatura
    PublicaciónAcceso abierto
    Metodología basada en los algoritmos Vega y Moga para solucionar un problema multiobjetivo en un sistema de producción job shop
    (2013-10-29) Coca-Ortegón, G. A. (Germán Augusto); Castrillón, Omar Danilo; Ruiz-Herrera, S. (Santiago)
    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%.