Real-time Rescheduling of Production Systems using Relational Reinforcement Learning

Most scheduling methodologies developed until now have laid down good theoretical foundations, but there is still the need for real-time rescheduling methods that can work effectively in disruption management. In this work, a novel approach for automatic generation of rescheduling knowledge using Re...

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Autores principales: Palombarini, Jorge, Martínez, Ernesto
Formato: Objeto de conferencia
Lenguaje:Inglés
Publicado: 2011
Materias:
Ciencias Informáticas
Learning
Rescheduling
Relational modeling
Agile manufacturing
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/126372
https://40jaiio.sadio.org.ar/sites/default/files/T2011/JII/694.pdf
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-126372
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Learning
Rescheduling
Relational modeling
Agile manufacturing
spellingShingle Ciencias Informáticas
Learning
Rescheduling
Relational modeling
Agile manufacturing
Palombarini, Jorge
Martínez, Ernesto
Real-time Rescheduling of Production Systems using Relational Reinforcement Learning
topic_facet Ciencias Informáticas
Learning
Rescheduling
Relational modeling
Agile manufacturing
description Most scheduling methodologies developed until now have laid down good theoretical foundations, but there is still the need for real-time rescheduling methods that can work effectively in disruption management. In this work, a novel approach for automatic generation of rescheduling knowledge using Relational Reinforcement Learning (RRL) is presented. Relational representations of schedule states and repair operators enable to encode in a compact way and use in real-time rescheduling knowledge learned through intensive simulations of state transitions. An industrial example where a current schedule must be repaired following the arrival of a new order is discussed using a prototype application –SmartGantt®- for interactive rescheduling in a reactive way. SmartGantt® demonstrates the advantages of resorting to RRL and abstract states for real-time rescheduling. A small number of training episodes are required to define a repair policy which can handle on the fly events such as order insertion, resource break-down, raw material delay or shortage and rush order arrivals using a sequence of operators to achieve a selected goal.
format Objeto de conferencia
Objeto de conferencia
author Palombarini, Jorge
Martínez, Ernesto
author_facet Palombarini, Jorge
Martínez, Ernesto
author_sort Palombarini, Jorge
title Real-time Rescheduling of Production Systems using Relational Reinforcement Learning
title_short Real-time Rescheduling of Production Systems using Relational Reinforcement Learning
title_full Real-time Rescheduling of Production Systems using Relational Reinforcement Learning
title_fullStr Real-time Rescheduling of Production Systems using Relational Reinforcement Learning
title_full_unstemmed Real-time Rescheduling of Production Systems using Relational Reinforcement Learning
title_sort real-time rescheduling of production systems using relational reinforcement learning
publishDate 2011
url http://sedici.unlp.edu.ar/handle/10915/126372
https://40jaiio.sadio.org.ar/sites/default/files/T2011/JII/694.pdf
work_keys_str_mv AT palombarinijorge realtimereschedulingofproductionsystemsusingrelationalreinforcementlearning
AT martinezernesto realtimereschedulingofproductionsystemsusingrelationalreinforcementlearning
bdutipo_str Repositorios
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