Programação da produção em sistemas flow shop utilizando um método heurístico híbrido algoritmo genético-simulated annealing
This paper deals with the Permutation Flow Shop Scheduling problem. Many heuristic methods have been proposed for this scheduling problem. A class of such heuristics finds a good solution by improving initial sequences for the jobs through search procedures on the solution space as Genetic Algorithm...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2000 |
| País: | Brasil |
| Institución: | Universidade Federal do Ceará (UFC) |
| Repositorio: | Repositório Institucional da Universidade Federal do Ceará (UFC) |
| Idioma: | portugués |
| OAI Identifier: | oai:repositorio.ufc.br:riufc/67115 |
| Acceso en línea: | http://www.repositorio.ufc.br/handle/riufc/67115 |
| Access Level: | acceso abierto |
| Palabra clave: | Programação da produção Flow shop permutacional Metaheurísticas híbridas |
| Sumario: | This paper deals with the Permutation Flow Shop Scheduling problem. Many heuristic methods have been proposed for this scheduling problem. A class of such heuristics finds a good solution by improving initial sequences for the jobs through search procedures on the solution space as Genetic Algorithm (GA) and Simulated Annealing (SA). A promising approach for the problem is the formulation of hybrid metaheuristics by combining GA and SA techniques so that the consequent procedure is more effective than either pure GA or SA methods. In this paper we present a hybrid Genetic Algorithm-Simulated Annealing heuristic for the minimal makespan flow shop sequencing problem. In order to evaluate the effectiveness of the hybridization we compare the hybrid heuristic with both pure GA and SA heuristics. Results from computational experience are presented. |
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