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
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/21428

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network_name_str SEDICI (UNLP)
spelling 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
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http://purl.org/coar/resource_type/c_5794
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dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/21428
url http://sedici.unlp.edu.ar/handle/10915/21428
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv 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)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
236-240
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instname:Universidad Nacional de La Plata
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