Resumen

MEISEL, José David  y  PRADO, Liliana Katherine. A HYBRID GENETIC ALGORITHM AND A SIMULATED ANNEALING FOR SOLVING THE JOB SHOP SCHEDULING PROBLEM. Rev.EIA.Esc.Ing.Antioq [online]. 2010, n.13, pp.39-51. ISSN 1794-1237.

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.

Palabras clave : Job Shop; genetic algorithm; simulated annealing; operations management; combinatorial optimization.

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