A hybrid genetic algorithm for ROADEF'05-like complex production problems
In this work, we present a hybrid technique that combines a Genetic Algorithm with meta-heuristics to solve a problem in RENAULT France’s production plants. The method starts with an initial solution obtained by means of a GRASP (Greedy Randomized Adaptive Search Procedure) used as an input for a Ge...
- Autores:
-
Frutos, Mariano
Olivera, Ana Carolina
Tohmé, Fernando
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2015
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/60738
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/60738
http://bdigital.unal.edu.co/59070/
- Palabra clave:
- 62 Ingeniería y operaciones afines / Engineering
Multi-objective Optimization
Hybrid Algorithms
Car Sequencing
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
| Summary: | In this work, we present a hybrid technique that combines a Genetic Algorithm with meta-heuristics to solve a problem in RENAULT France’s production plants. The method starts with an initial solution obtained by means of a GRASP (Greedy Randomized Adaptive Search Procedure) used as an input for a Genetic Algorithm complemented by a Simulated Annealing procedure of population improvement. We establish a comparison point among the different techniques used in the method. Their performances are evaluated as well as that of the entire method. The conclusion is that hybrid methods have clear advantages for the treatment of production planning problems. |
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