Evolutionary multi-objective scheduling procedures in non-standardized production processes
Scheduling problems can be seen as multi-objective optimization problems (MOPs), involving the simultaneous satisfaction of several goals related to the optimal design, coordination and management of tasks. The complexity of the goal functions and of the combinatorial methods used to find analytical...
- Autores:
-
Frutos, Mariano
Tohmé, Fernando
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2012
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/31163
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/31163
http://bdigital.unal.edu.co/21241/
- Palabra clave:
- Job-Shop scheduling
multi-objective optimization
Pareto frontier
memetic algorithm
local search
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
| Summary: | Scheduling problems can be seen as multi-objective optimization problems (MOPs), involving the simultaneous satisfaction of several goals related to the optimal design, coordination and management of tasks. The complexity of the goal functions and of the combinatorial methods used to find analytical solutions to them is quite high. The search of solutions (Pareto-optima) is better served by the use of genetic algorithms (GAs). In this work we analyze the performance of NSGAII (Non-dominated Sorting Genetic Algorithm II), SPEAII (Strength Pareto Evolutionary algorithm II) and their predecessors, NSGA and SPEA, when devoted to scheduling tasks in non-standardized production activities. |
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