Optimum routing and sequencing in a flexible multiobjective job shop using genetic algorithms
The paper proposes a genetic algorithm to solve optimally the problem of scheduling in a multi-objective production system Flexible Job Shop (FJS), currently of interest for many researchers, because it is a combinatorial optimization problem of complexity NP-hard, and because an optimal solution re...
| Autor: | |
|---|---|
| Formato: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2016 |
| País: | Perú |
| Recursos: | Universidad Nacional Mayor de San Marcos |
| Repositorio: | Revistas - Universidad Nacional Mayor de San Marcos |
| Idioma: | español |
| OAI Identifier: | oai:revistasinvestigacion.unmsm.edu.pe:article/12846 |
| Acesso em linha: | https://revistasinvestigacion.unmsm.edu.pe/index.php/idata/article/view/12846 |
| Access Level: | acceso abierto |
| Palavra-chave: | Flexible Job Shop Scheduling Problem Genetic Algorithms Makespan Maximum Workload Total Workload. Algoritmos Genéticos Máximo Workload |
| Resumo: | The paper proposes a genetic algorithm to solve optimally the problem of scheduling in a multi-objective production system Flexible Job Shop (FJS), currently of interest for many researchers, because it is a combinatorial optimization problem of complexity NP-hard, and because an optimal solution results in an increase in production. the problem is divided, in the subproblem routing, where it is assigned to each operation of Jobs, one of the most optimum machines (from a set available) minimizing Maximum Workload, and Total Workload and subproblem sequencing, where it is found the optimal order of execution of operations (distributed on each machine) minimizing the Makespan. The algorithm is coded in Matlab M language, their performance is tested, solving complex problems, and the results are compared with those obtained by other researchers. |
|---|