Solutions to the dynamic average tardiness problem in single machine environments

Autores
San Pedro, María Eugenia de; Lasso, Marta Graciela; Villagra, Andrea; Pandolfi, Daniel; Gallard, Raúl Hector
Año de publicación
2003
Idioma
español castellano
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
In dynamic scheduling arrival times as well, as some or all job attributes are unknown in advance. Dynamism can be classified as partial or total. In simplest partially dynamic problems the only unknown attribute of a job is its arrival time rj. A job arrival can be given at any instant in the time interval between zero and a limit established by its processing time, in order to ensure finishing it before the due date deadline. In the cases where the arrivals are near to zero the problem becomes closer to the static problem, otherwise the problem becomes more restrictive. In totally dynamics problems, other job attributes such as processing time pj, due date dj, and tardiness penalty wj, are also unknown. This paper proposes different approaches for resolution of (partial and total) Dynamic Average Tardiness problems in a single machine environment. The first approach uses, as a list of dispatching priorities a final (total) schedule, found as the best by another method for a similar static problem: same job features, processing time, and due dates. The second approach uses as a dispatching priority the order imposed by a partial schedule created by another heuristic, at each decision point. The details of implementation of the proposed algorithms and results for a group of selected instances are discussed in this work.
Eje: Agentes y Sistemas Inteligentes (ASI)
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
Evolutionary Scheduling
Average Tardiness
Intelligent agents
Scheduling
Dynamic scheduling
conventional heuristics
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:10915/22728

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spelling Solutions to the dynamic average tardiness problem in single machine environmentsSan Pedro, María Eugenia deLasso, Marta GracielaVillagra, AndreaPandolfi, DanielGallard, Raúl HectorCiencias InformáticasARTIFICIAL INTELLIGENCEEvolutionary SchedulingAverage TardinessIntelligent agentsSchedulingDynamic schedulingconventional heuristicsIn dynamic scheduling arrival times as well, as some or all job attributes are unknown in advance. Dynamism can be classified as partial or total. In simplest partially dynamic problems the only unknown attribute of a job is its arrival time rj. A job arrival can be given at any instant in the time interval between zero and a limit established by its processing time, in order to ensure finishing it before the due date deadline. In the cases where the arrivals are near to zero the problem becomes closer to the static problem, otherwise the problem becomes more restrictive. In totally dynamics problems, other job attributes such as processing time pj, due date dj, and tardiness penalty wj, are also unknown. This paper proposes different approaches for resolution of (partial and total) Dynamic Average Tardiness problems in a single machine environment. The first approach uses, as a list of dispatching priorities a final (total) schedule, found as the best by another method for a similar static problem: same job features, processing time, and due dates. The second approach uses as a dispatching priority the order imposed by a partial schedule created by another heuristic, at each decision point. The details of implementation of the proposed algorithms and results for a group of selected instances are discussed in this work.Eje: Agentes y Sistemas Inteligentes (ASI)Red de Universidades con Carreras en Informática (RedUNCI)2003-10info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf729-739http://sedici.unlp.edu.ar/handle/10915/22728spainfo: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:UNLP2025-10-15T10:47:47Zoai:sedici.unlp.edu.ar:10915/22728Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-10-15 10:47:47.709SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Solutions to the dynamic average tardiness problem in single machine environments
title Solutions to the dynamic average tardiness problem in single machine environments
spellingShingle Solutions to the dynamic average tardiness problem in single machine environments
San Pedro, María Eugenia de
Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
Evolutionary Scheduling
Average Tardiness
Intelligent agents
Scheduling
Dynamic scheduling
conventional heuristics
title_short Solutions to the dynamic average tardiness problem in single machine environments
title_full Solutions to the dynamic average tardiness problem in single machine environments
title_fullStr Solutions to the dynamic average tardiness problem in single machine environments
title_full_unstemmed Solutions to the dynamic average tardiness problem in single machine environments
title_sort Solutions to the dynamic average tardiness problem in single machine environments
dc.creator.none.fl_str_mv San Pedro, María Eugenia de
Lasso, Marta Graciela
Villagra, Andrea
Pandolfi, Daniel
Gallard, Raúl Hector
author San Pedro, María Eugenia de
author_facet San Pedro, María Eugenia de
Lasso, Marta Graciela
Villagra, Andrea
Pandolfi, Daniel
Gallard, Raúl Hector
author_role author
author2 Lasso, Marta Graciela
Villagra, Andrea
Pandolfi, Daniel
Gallard, Raúl Hector
author2_role author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
Evolutionary Scheduling
Average Tardiness
Intelligent agents
Scheduling
Dynamic scheduling
conventional heuristics
topic Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
Evolutionary Scheduling
Average Tardiness
Intelligent agents
Scheduling
Dynamic scheduling
conventional heuristics
dc.description.none.fl_txt_mv In dynamic scheduling arrival times as well, as some or all job attributes are unknown in advance. Dynamism can be classified as partial or total. In simplest partially dynamic problems the only unknown attribute of a job is its arrival time rj. A job arrival can be given at any instant in the time interval between zero and a limit established by its processing time, in order to ensure finishing it before the due date deadline. In the cases where the arrivals are near to zero the problem becomes closer to the static problem, otherwise the problem becomes more restrictive. In totally dynamics problems, other job attributes such as processing time pj, due date dj, and tardiness penalty wj, are also unknown. This paper proposes different approaches for resolution of (partial and total) Dynamic Average Tardiness problems in a single machine environment. The first approach uses, as a list of dispatching priorities a final (total) schedule, found as the best by another method for a similar static problem: same job features, processing time, and due dates. The second approach uses as a dispatching priority the order imposed by a partial schedule created by another heuristic, at each decision point. The details of implementation of the proposed algorithms and results for a group of selected instances are discussed in this work.
Eje: Agentes y Sistemas Inteligentes (ASI)
Red de Universidades con Carreras en Informática (RedUNCI)
description In dynamic scheduling arrival times as well, as some or all job attributes are unknown in advance. Dynamism can be classified as partial or total. In simplest partially dynamic problems the only unknown attribute of a job is its arrival time rj. A job arrival can be given at any instant in the time interval between zero and a limit established by its processing time, in order to ensure finishing it before the due date deadline. In the cases where the arrivals are near to zero the problem becomes closer to the static problem, otherwise the problem becomes more restrictive. In totally dynamics problems, other job attributes such as processing time pj, due date dj, and tardiness penalty wj, are also unknown. This paper proposes different approaches for resolution of (partial and total) Dynamic Average Tardiness problems in a single machine environment. The first approach uses, as a list of dispatching priorities a final (total) schedule, found as the best by another method for a similar static problem: same job features, processing time, and due dates. The second approach uses as a dispatching priority the order imposed by a partial schedule created by another heuristic, at each decision point. The details of implementation of the proposed algorithms and results for a group of selected instances are discussed in this work.
publishDate 2003
dc.date.none.fl_str_mv 2003-10
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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format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/22728
url http://sedici.unlp.edu.ar/handle/10915/22728
dc.language.none.fl_str_mv spa
language spa
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
729-739
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instname:Universidad Nacional de La Plata
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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