Sistema de soporte de decisiones de producción en un entorno flexible job shop basado en un modelo predictivo-reactivo sujeto a perturbaciones
Production scheduling under real-time events has high importance for the successful performance of real-world scheduling systems. Most manufacturing systems operate in dynamic environments vulnerable to various non-programmed real-time events which continuously forces revision and reconsideration of...
- Autores:
-
Meza Villalba, Sebastian Mateo
- Tipo de recurso:
- Fecha de publicación:
- 2020
- Institución:
- Escuela Colombiana de Ingeniería Julio Garavito
- Repositorio:
- Repositorio Institucional ECI
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.escuelaing.edu.co:001/1205
- Acceso en línea:
- https://catalogo.escuelaing.edu.co/cgi-bin/koha/opac-detail.pl?biblionumber=22397
https://repositorio.escuelaing.edu.co/handle/001/1205
- Palabra clave:
- Sistema de soporte- Decisiones
Sistemas de manufactura -Flexible
Programación de operaciones
Estrategia predictiva-reactiva
Algoritmo genético
Metaheuristicas
Decision support system
Flexible manufacturing system
Scheduling
Predictive-reactive strategy
Genetic algorithm
Metaheuristics
- Rights
- openAccess
- License
- Derechos Reservados - Escuela Colombiana de Ingeniería Julio Garavito
| Summary: | Production scheduling under real-time events has high importance for the successful performance of real-world scheduling systems. Most manufacturing systems operate in dynamic environments vulnerable to various non-programmed real-time events which continuously forces revision and reconsideration of pre-established schedules. In an uncertain environment, efficient ways to adapt current solutions to unexpected events, are preferable to solutions that soon become obsolete. This situation motivated the development of a decision support system that attempts to fill the gap between scheduling theory and practice. The developed prototype uses metaheuristics to generate a predictive schedule for an initial solution before execution. Then, whenever disruptions happen, like arrival of new tasks or cancelation of others, the decision support system starts updating the schedule through a reactive module that uses heuristics based on dispatching rules principles. The proposed system was tested in a simulated scenario of a real flexible manufacturing system located in Valenciennes (France), called AIP-PRIMECA Valenciennes. A disruption was carried out during the execution in the manufacturing system in order to demonstrate the effectiveness of the proposed model. |
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