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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/22728
Ver los metadatos del registro completo
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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 |
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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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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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