Behavior of the main parameters of the Genetic Algorithm for Flow Shop Scheduling Problems
There are different suggested values to adapt the basic parameters of a Genetic Algorithm, however, these values may not be the optimal for all kinds of applications. The following research presents a metaheuristic based on a Genetic Algorithm to solve problems of type Flow Shop Scheduling wi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | Spanish |
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Universidad de Ciencias Informáticas
2014-01-01
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| Series: | Revista Cubana de Ciencias Informáticas |
| Subjects: | |
| Online Access: | http://rcci.uci.cu/index.php?journal=rcci&page=article&op=view&path[]=590&path[]=257 |
| _version_ | 1828808931311353856 |
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| author | Yunior César Fonseca Reyna Yailen Martínez Jiménez Ángel Enrique Figueredo León Luis Alberto Pernía Nieves |
| author_facet | Yunior César Fonseca Reyna Yailen Martínez Jiménez Ángel Enrique Figueredo León Luis Alberto Pernía Nieves |
| author_sort | Yunior César Fonseca Reyna |
| collection | DOAJ |
| description | There are different suggested values to adapt the basic parameters of a Genetic Algorithm, however, these values may
not be the optimal for all kinds of applications. The following research presents a metaheuristic based on a Genetic
Algorithm to solve problems of type Flow Shop Scheduling with the objective of minimizing the completion time of all
jobs, known in literature as makespan or Cmax. This problem is typical of combinatorial optimization and can be
found in manufacturing environment, where there are conventional machines-tools and different types of pieces which
share the same route. A set of crossover and selection operators are implemented methods for the pro posed Genetic
Algorithm once its main factors are calibrated, the size of the population, number of generations, mutation and
crossover factors, statistical study is performed in order to determine the combinations of these parameters that has
a greater influence. Finally, the combination of parameters whit the best performance is tested with problems of
different levels of complexity in order to obtain satisfactory results in terms of solutions quality. |
| first_indexed | 2024-12-12T08:48:18Z |
| format | Article |
| id | doaj.art-d69ec848715646539ed199803bc6aa8e |
| institution | Directory Open Access Journal |
| issn | 1994-1536 2227-1899 |
| language | Spanish |
| last_indexed | 2024-12-12T08:48:18Z |
| publishDate | 2014-01-01 |
| publisher | Universidad de Ciencias Informáticas |
| record_format | Article |
| series | Revista Cubana de Ciencias Informáticas |
| spelling | doaj.art-d69ec848715646539ed199803bc6aa8e2022-12-22T00:30:21ZspaUniversidad de Ciencias InformáticasRevista Cubana de Ciencias Informáticas1994-15362227-18992014-01-018199111Behavior of the main parameters of the Genetic Algorithm for Flow Shop Scheduling ProblemsYunior César Fonseca Reyna0Yailen Martínez Jiménez1Ángel Enrique Figueredo León2Luis Alberto Pernía Nieves3Departamento de Informática. Universidad de Granma, km 18 ½, Carretera a Manzanillo, Bayamo, Granma, CubaDepartamento de Ciencia de la Computación. Universidad Central “Marta Abreu” de las Villas, Carretera a Camajuaní, km 5 ½, Santa Clara, Villa Clara, CubaDepartamento de Informática. Universidad de Granma, km 18 ½, Carretera a Manzanillo, Bayamo, Granma, CubaDepartamento de Informática. Universidad de Granma, km 18 ½, Carretera a Manzanillo, Bayamo, Granma, CubaThere are different suggested values to adapt the basic parameters of a Genetic Algorithm, however, these values may not be the optimal for all kinds of applications. The following research presents a metaheuristic based on a Genetic Algorithm to solve problems of type Flow Shop Scheduling with the objective of minimizing the completion time of all jobs, known in literature as makespan or Cmax. This problem is typical of combinatorial optimization and can be found in manufacturing environment, where there are conventional machines-tools and different types of pieces which share the same route. A set of crossover and selection operators are implemented methods for the pro posed Genetic Algorithm once its main factors are calibrated, the size of the population, number of generations, mutation and crossover factors, statistical study is performed in order to determine the combinations of these parameters that has a greater influence. Finally, the combination of parameters whit the best performance is tested with problems of different levels of complexity in order to obtain satisfactory results in terms of solutions quality.http://rcci.uci.cu/index.php?journal=rcci&page=article&op=view&path[]=590&path[]=257Genetic algorithmsflow shopmakespanoptimizationscheduling |
| spellingShingle | Yunior César Fonseca Reyna Yailen Martínez Jiménez Ángel Enrique Figueredo León Luis Alberto Pernía Nieves Behavior of the main parameters of the Genetic Algorithm for Flow Shop Scheduling Problems Revista Cubana de Ciencias Informáticas Genetic algorithms flow shop makespan optimization scheduling |
| title | Behavior of the main parameters of the Genetic Algorithm for Flow Shop Scheduling Problems |
| title_full | Behavior of the main parameters of the Genetic Algorithm for Flow Shop Scheduling Problems |
| title_fullStr | Behavior of the main parameters of the Genetic Algorithm for Flow Shop Scheduling Problems |
| title_full_unstemmed | Behavior of the main parameters of the Genetic Algorithm for Flow Shop Scheduling Problems |
| title_short | Behavior of the main parameters of the Genetic Algorithm for Flow Shop Scheduling Problems |
| title_sort | behavior of the main parameters of the genetic algorithm for flow shop scheduling problems |
| topic | Genetic algorithms flow shop makespan optimization scheduling |
| url | http://rcci.uci.cu/index.php?journal=rcci&page=article&op=view&path[]=590&path[]=257 |
| work_keys_str_mv | AT yuniorcesarfonsecareyna behaviorofthemainparametersofthegeneticalgorithmforflowshopschedulingproblems AT yailenmartinezjimenez behaviorofthemainparametersofthegeneticalgorithmforflowshopschedulingproblems AT angelenriquefigueredoleon behaviorofthemainparametersofthegeneticalgorithmforflowshopschedulingproblems AT luisalbertopernianieves behaviorofthemainparametersofthegeneticalgorithmforflowshopschedulingproblems |