Solving unrestricted parallel machine scheduling problems via evolutionary algorithms
- Autores
- Gatica, Claudia Ruth; Ferretti, Edgardo; Gallard, Raúl Hector
- Año de publicación
- 2003
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- Parallel machine scheduling, also known as parallel task scheduling, involves the assignment of multiple tasks onto the system architecture’s processing components (a bank of machines in parallel). A basic model involving m machines and n independent jobs is the foundation of more complex models. Here, the jobs are allocated according to resource availability following some allocation rule. The completion time of the last job to leave the system, known as the makespan (Cmax), is one of the most important objective functions to be minimized, because it usually implies high utilization of resources, but other important objectives must be also considered. These problems are known in the literature [9, 11] as unrestricted parallel machine scheduling problems. Many of these problems are NP-hard for 2≤ m ≤ n, and conventional heuristics and evolutionary algorithms (EAs) have been developed to provide acceptable schedules as solutions. This presentation shows the problem of allocating a number of non-identical independent tasks in a production system. The model assumes that the system consists of a number of identical machines and only one task may execute on a machine at a time. All schedules and tasks are non-preemptive. A set of well-known conventional heuristics will be contrasted with evolutionary approaches using multiple recombination and indirect representations.
Eje: Informática de Gestión
Red de Universidades con Carreras en Informática (RedUNCI) - Materia
-
Ciencias Informáticas
gestión
Algorithms
informática
Scheduling
Parallel
solving unrestricted
parallel machine
scheduling problems
evolutionary algorithms - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/21428
Ver los metadatos del registro completo
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Solving unrestricted parallel machine scheduling problems via evolutionary algorithmsGatica, Claudia RuthFerretti, EdgardoGallard, Raúl HectorCiencias InformáticasgestiónAlgorithmsinformáticaSchedulingParallelsolving unrestrictedparallel machinescheduling problemsevolutionary algorithmsParallel machine scheduling, also known as parallel task scheduling, involves the assignment of multiple tasks onto the system architecture’s processing components (a bank of machines in parallel). A basic model involving m machines and n independent jobs is the foundation of more complex models. Here, the jobs are allocated according to resource availability following some allocation rule. The completion time of the last job to leave the system, known as the makespan (Cmax), is one of the most important objective functions to be minimized, because it usually implies high utilization of resources, but other important objectives must be also considered. These problems are known in the literature [9, 11] as unrestricted parallel machine scheduling problems. Many of these problems are NP-hard for 2≤ m ≤ n, and conventional heuristics and evolutionary algorithms (EAs) have been developed to provide acceptable schedules as solutions. This presentation shows the problem of allocating a number of non-identical independent tasks in a production system. The model assumes that the system consists of a number of identical machines and only one task may execute on a machine at a time. All schedules and tasks are non-preemptive. A set of well-known conventional heuristics will be contrasted with evolutionary approaches using multiple recombination and indirect representations.Eje: Informática de GestiónRed de Universidades con Carreras en Informática (RedUNCI)2003-05info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf236-240http://sedici.unlp.edu.ar/handle/10915/21428enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T10:27:26Zoai:sedici.unlp.edu.ar:10915/21428Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:27:26.936SEDICI (UNLP) - Universidad Nacional de La Platafalse |
dc.title.none.fl_str_mv |
Solving unrestricted parallel machine scheduling problems via evolutionary algorithms |
title |
Solving unrestricted parallel machine scheduling problems via evolutionary algorithms |
spellingShingle |
Solving unrestricted parallel machine scheduling problems via evolutionary algorithms Gatica, Claudia Ruth Ciencias Informáticas gestión Algorithms informática Scheduling Parallel solving unrestricted parallel machine scheduling problems evolutionary algorithms |
title_short |
Solving unrestricted parallel machine scheduling problems via evolutionary algorithms |
title_full |
Solving unrestricted parallel machine scheduling problems via evolutionary algorithms |
title_fullStr |
Solving unrestricted parallel machine scheduling problems via evolutionary algorithms |
title_full_unstemmed |
Solving unrestricted parallel machine scheduling problems via evolutionary algorithms |
title_sort |
Solving unrestricted parallel machine scheduling problems via evolutionary algorithms |
dc.creator.none.fl_str_mv |
Gatica, Claudia Ruth Ferretti, Edgardo Gallard, Raúl Hector |
author |
Gatica, Claudia Ruth |
author_facet |
Gatica, Claudia Ruth Ferretti, Edgardo Gallard, Raúl Hector |
author_role |
author |
author2 |
Ferretti, Edgardo Gallard, Raúl Hector |
author2_role |
author author |
dc.subject.none.fl_str_mv |
Ciencias Informáticas gestión Algorithms informática Scheduling Parallel solving unrestricted parallel machine scheduling problems evolutionary algorithms |
topic |
Ciencias Informáticas gestión Algorithms informática Scheduling Parallel solving unrestricted parallel machine scheduling problems evolutionary algorithms |
dc.description.none.fl_txt_mv |
Parallel machine scheduling, also known as parallel task scheduling, involves the assignment of multiple tasks onto the system architecture’s processing components (a bank of machines in parallel). A basic model involving m machines and n independent jobs is the foundation of more complex models. Here, the jobs are allocated according to resource availability following some allocation rule. The completion time of the last job to leave the system, known as the makespan (Cmax), is one of the most important objective functions to be minimized, because it usually implies high utilization of resources, but other important objectives must be also considered. These problems are known in the literature [9, 11] as unrestricted parallel machine scheduling problems. Many of these problems are NP-hard for 2≤ m ≤ n, and conventional heuristics and evolutionary algorithms (EAs) have been developed to provide acceptable schedules as solutions. This presentation shows the problem of allocating a number of non-identical independent tasks in a production system. The model assumes that the system consists of a number of identical machines and only one task may execute on a machine at a time. All schedules and tasks are non-preemptive. A set of well-known conventional heuristics will be contrasted with evolutionary approaches using multiple recombination and indirect representations. Eje: Informática de Gestión Red de Universidades con Carreras en Informática (RedUNCI) |
description |
Parallel machine scheduling, also known as parallel task scheduling, involves the assignment of multiple tasks onto the system architecture’s processing components (a bank of machines in parallel). A basic model involving m machines and n independent jobs is the foundation of more complex models. Here, the jobs are allocated according to resource availability following some allocation rule. The completion time of the last job to leave the system, known as the makespan (Cmax), is one of the most important objective functions to be minimized, because it usually implies high utilization of resources, but other important objectives must be also considered. These problems are known in the literature [9, 11] as unrestricted parallel machine scheduling problems. Many of these problems are NP-hard for 2≤ m ≤ n, and conventional heuristics and evolutionary algorithms (EAs) have been developed to provide acceptable schedules as solutions. This presentation shows the problem of allocating a number of non-identical independent tasks in a production system. The model assumes that the system consists of a number of identical machines and only one task may execute on a machine at a time. All schedules and tasks are non-preemptive. A set of well-known conventional heuristics will be contrasted with evolutionary approaches using multiple recombination and indirect representations. |
publishDate |
2003 |
dc.date.none.fl_str_mv |
2003-05 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion Objeto de conferencia http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
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conferenceObject |
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dc.identifier.none.fl_str_mv |
http://sedici.unlp.edu.ar/handle/10915/21428 |
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http://sedici.unlp.edu.ar/handle/10915/21428 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
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info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/2.5/ar/ Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) |
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openAccess |
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http://creativecommons.org/licenses/by-nc-sa/2.5/ar/ Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) |
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