A hybrid method to optimize the Flexible Job Shop Scheduling Problem
This article addresses task scheduling in the Flexible Job Shop Scheduling Problem (FJSSP). In this manufacturing system, it is necessary to intensify the number of jobs to be processed due to the current conditions of the industrial sector where there is an increase in the demand for products, whic...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | México |
| Institución: | UNIVERSIDAD AUTÓNOMA DEL ESTADO DE HIDALGO |
| Repositorio: | PÄDI Boletín Científico de Ciencias Básicas e Ingeniería del ICBI |
| Idioma: | español |
| OAI Identifier: | oai:repository.uaeh.edu.mx:article/8651 |
| Acceso en línea: | https://repository.uaeh.edu.mx/revistas/index.php/icbi/article/view/8651 |
| Access Level: | acceso abierto |
| Palabra clave: | • Flexible Job Shop Scheduling Problem genetic algorithms hill climbing hybrid optimization makespan algoritmos genéticos escalada de colina optimización híbrida |
| Sumario: | This article addresses task scheduling in the Flexible Job Shop Scheduling Problem (FJSSP). In this manufacturing system, it is necessary to intensify the number of jobs to be processed due to the current conditions of the industrial sector where there is an increase in the demand for products, which leads to an increase in production. To find a task schedule close to the optimum. A hybrid optimization method is proposed using a global search based on genetic algorithms (GA) that have good diversification. A restart hill-climbing process (RHC) is used as a local search method in order to improve each solution. These metaheuristics yield the equilibrium necessary to find the best solution that minimizes the makespan as a cost function. The proposed algorithm was implemented in Matlab, and the results were compared with recently published research to review its efficiency. |
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