Hybrid simulation and ga for a flexible flow shop problem with variable processors and re-entrant flow
The problem of FFSP (Flexible Flow Shop Problem) has been sufficiently investigated due to its importance for production programming and control, although many of the solution methods have been based on GA (Genetic Algorithm) and simulation, these techniques have been used in deterministic environme...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2018
- Institución:
- Universidad del Rosario
- Repositorio:
- Repositorio EdocUR - U. Rosario
- Idioma:
- eng
- OAI Identifier:
- oai:repository.urosario.edu.co:10336/23750
- Acceso en línea:
- https://doi.org/10.1007/978-3-030-00350-0_21
https://repository.urosario.edu.co/handle/10336/23750
- Palabra clave:
- Efficiency
Genetic algorithms
Heuristic methods
Machine shop practice
Flexible flow-shop problems
GA (genetic algorithm)
Hybrid simulation
Manufacturing environments
Parallel machine
Recirculation process
Simulation
Simulation process
Problem solving
Flexible workshop programming
Genetic algorithm
Parallel machines
Process re-entry
Simulation
- Rights
- License
- http://purl.org/coar/access_right/c_abf2
Summary: | The problem of FFSP (Flexible Flow Shop Problem) has been sufficiently investigated due to its importance for production programming and control, although many of the solution methods have been based on GA (Genetic Algorithm) and simulation, these techniques have been used in deterministic environments and under specific conditions of the problem, that is, complying with restrictions given in the Graham notation. In this paper we describe an application of these techniques to solve a very particular case where manual work stations and equipment with different degrees of efficiency, technological restrictions, recirculation process are used. The nesting of the GA is used within a simulation process. It is showed that the method proposed in adjustment and efficiency is better compared with other heuristics, in addition to the benefits of using different techniques in series to solve problems of real manufacturing environments. © 2018, Springer Nature Switzerland AG. |
---|