Learning-based scheduling in a job shop

Abstract A common way of dynamically scheduling jobs in a manufacturing system is by means of dispatching rules. The problem of this method is that the performance of these rules depends on the state the system is in at each moment, and no rule exists that overrules the rest in all the possible stat...
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Gespeichert in:

Autor*in:	Priore, P. [verfasserIn] de la Fuente, D.

Format:	Artikel
Sprache:	Englisch

Erschienen:	1999

Schlagwörter:	learning-based scheduling job shop systems machine learning

Anmerkung:	© Springer 1999

Übergeordnetes Werk:	Enthalten in: Elektrotechnik und Informationstechnik - Springer-Verlag, 1988, 116(1999), 6 vom: Juni, Seite 370-375
Übergeordnetes Werk:	volume:116 ; year:1999 ; number:6 ; month:06 ; pages:370-375

Links:	Volltext

DOI / URN:	10.1007/BF03159198

Katalog-ID:	OLC2065314567

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520			\|a Abstract A common way of dynamically scheduling jobs in a manufacturing system is by means of dispatching rules. The problem of this method is that the performance of these rules depends on the state the system is in at each moment, and no rule exists that overrules the rest in all the possible states that the system may be in. The system’s state is defined by a set of control attributes. It would therefore be interesting to use the most appropriate dispatching rule at each moment. To achieve this goal, a scheduling approach which uses machine learning is presented in this paper. By means of this technique, by analysing the previous performance of the system (training examples), a set of heuristic rules are generated that can be used to decide which is the most appropriate dispatching rule at each moment in time. This approach is applied to a job shop configuration. The results demonstrate that this approach produces an improvement in the performance of the system when compared to the traditional method of using dispatching rules.
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allfields	10.1007/BF03159198 doi (DE-627)OLC2065314567 (DE-He213)BF03159198-p DE-627 ger DE-627 rakwb eng 620 004 070 VZ Priore, P. verfasserin aut Learning-based scheduling in a job shop 1999 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer 1999 Abstract A common way of dynamically scheduling jobs in a manufacturing system is by means of dispatching rules. The problem of this method is that the performance of these rules depends on the state the system is in at each moment, and no rule exists that overrules the rest in all the possible states that the system may be in. The system’s state is defined by a set of control attributes. It would therefore be interesting to use the most appropriate dispatching rule at each moment. To achieve this goal, a scheduling approach which uses machine learning is presented in this paper. By means of this technique, by analysing the previous performance of the system (training examples), a set of heuristic rules are generated that can be used to decide which is the most appropriate dispatching rule at each moment in time. This approach is applied to a job shop configuration. The results demonstrate that this approach produces an improvement in the performance of the system when compared to the traditional method of using dispatching rules. learning-based scheduling job shop systems machine learning de la Fuente, D. aut Enthalten in Elektrotechnik und Informationstechnik Springer-Verlag, 1988 116(1999), 6 vom: Juni, Seite 370-375 30 cm (DE-627)129622184 (DE-600)246725-2 (DE-576)015132323 0932-383X nnns volume:116 year:1999 number:6 month:06 pages:370-375 https://doi.org/10.1007/BF03159198 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_11 GBV_ILN_20 GBV_ILN_21 GBV_ILN_32 GBV_ILN_34 GBV_ILN_62 GBV_ILN_70 GBV_ILN_100 GBV_ILN_105 GBV_ILN_267 GBV_ILN_2006 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2016 GBV_ILN_2020 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_4036 GBV_ILN_4046 GBV_ILN_4266 GBV_ILN_4307 GBV_ILN_4314 GBV_ILN_4316 GBV_ILN_4317 GBV_ILN_4319 GBV_ILN_4328 AR 116 1999 6 06 370-375
spelling	10.1007/BF03159198 doi (DE-627)OLC2065314567 (DE-He213)BF03159198-p DE-627 ger DE-627 rakwb eng 620 004 070 VZ Priore, P. verfasserin aut Learning-based scheduling in a job shop 1999 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer 1999 Abstract A common way of dynamically scheduling jobs in a manufacturing system is by means of dispatching rules. The problem of this method is that the performance of these rules depends on the state the system is in at each moment, and no rule exists that overrules the rest in all the possible states that the system may be in. The system’s state is defined by a set of control attributes. It would therefore be interesting to use the most appropriate dispatching rule at each moment. To achieve this goal, a scheduling approach which uses machine learning is presented in this paper. By means of this technique, by analysing the previous performance of the system (training examples), a set of heuristic rules are generated that can be used to decide which is the most appropriate dispatching rule at each moment in time. This approach is applied to a job shop configuration. The results demonstrate that this approach produces an improvement in the performance of the system when compared to the traditional method of using dispatching rules. learning-based scheduling job shop systems machine learning de la Fuente, D. aut Enthalten in Elektrotechnik und Informationstechnik Springer-Verlag, 1988 116(1999), 6 vom: Juni, Seite 370-375 30 cm (DE-627)129622184 (DE-600)246725-2 (DE-576)015132323 0932-383X nnns volume:116 year:1999 number:6 month:06 pages:370-375 https://doi.org/10.1007/BF03159198 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_11 GBV_ILN_20 GBV_ILN_21 GBV_ILN_32 GBV_ILN_34 GBV_ILN_62 GBV_ILN_70 GBV_ILN_100 GBV_ILN_105 GBV_ILN_267 GBV_ILN_2006 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2016 GBV_ILN_2020 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_4036 GBV_ILN_4046 GBV_ILN_4266 GBV_ILN_4307 GBV_ILN_4314 GBV_ILN_4316 GBV_ILN_4317 GBV_ILN_4319 GBV_ILN_4328 AR 116 1999 6 06 370-375
allfields_unstemmed	10.1007/BF03159198 doi (DE-627)OLC2065314567 (DE-He213)BF03159198-p DE-627 ger DE-627 rakwb eng 620 004 070 VZ Priore, P. verfasserin aut Learning-based scheduling in a job shop 1999 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer 1999 Abstract A common way of dynamically scheduling jobs in a manufacturing system is by means of dispatching rules. The problem of this method is that the performance of these rules depends on the state the system is in at each moment, and no rule exists that overrules the rest in all the possible states that the system may be in. The system’s state is defined by a set of control attributes. It would therefore be interesting to use the most appropriate dispatching rule at each moment. To achieve this goal, a scheduling approach which uses machine learning is presented in this paper. By means of this technique, by analysing the previous performance of the system (training examples), a set of heuristic rules are generated that can be used to decide which is the most appropriate dispatching rule at each moment in time. This approach is applied to a job shop configuration. The results demonstrate that this approach produces an improvement in the performance of the system when compared to the traditional method of using dispatching rules. learning-based scheduling job shop systems machine learning de la Fuente, D. aut Enthalten in Elektrotechnik und Informationstechnik Springer-Verlag, 1988 116(1999), 6 vom: Juni, Seite 370-375 30 cm (DE-627)129622184 (DE-600)246725-2 (DE-576)015132323 0932-383X nnns volume:116 year:1999 number:6 month:06 pages:370-375 https://doi.org/10.1007/BF03159198 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_11 GBV_ILN_20 GBV_ILN_21 GBV_ILN_32 GBV_ILN_34 GBV_ILN_62 GBV_ILN_70 GBV_ILN_100 GBV_ILN_105 GBV_ILN_267 GBV_ILN_2006 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2016 GBV_ILN_2020 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_4036 GBV_ILN_4046 GBV_ILN_4266 GBV_ILN_4307 GBV_ILN_4314 GBV_ILN_4316 GBV_ILN_4317 GBV_ILN_4319 GBV_ILN_4328 AR 116 1999 6 06 370-375
allfieldsGer	10.1007/BF03159198 doi (DE-627)OLC2065314567 (DE-He213)BF03159198-p DE-627 ger DE-627 rakwb eng 620 004 070 VZ Priore, P. verfasserin aut Learning-based scheduling in a job shop 1999 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer 1999 Abstract A common way of dynamically scheduling jobs in a manufacturing system is by means of dispatching rules. The problem of this method is that the performance of these rules depends on the state the system is in at each moment, and no rule exists that overrules the rest in all the possible states that the system may be in. The system’s state is defined by a set of control attributes. It would therefore be interesting to use the most appropriate dispatching rule at each moment. To achieve this goal, a scheduling approach which uses machine learning is presented in this paper. By means of this technique, by analysing the previous performance of the system (training examples), a set of heuristic rules are generated that can be used to decide which is the most appropriate dispatching rule at each moment in time. This approach is applied to a job shop configuration. The results demonstrate that this approach produces an improvement in the performance of the system when compared to the traditional method of using dispatching rules. learning-based scheduling job shop systems machine learning de la Fuente, D. aut Enthalten in Elektrotechnik und Informationstechnik Springer-Verlag, 1988 116(1999), 6 vom: Juni, Seite 370-375 30 cm (DE-627)129622184 (DE-600)246725-2 (DE-576)015132323 0932-383X nnns volume:116 year:1999 number:6 month:06 pages:370-375 https://doi.org/10.1007/BF03159198 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_11 GBV_ILN_20 GBV_ILN_21 GBV_ILN_32 GBV_ILN_34 GBV_ILN_62 GBV_ILN_70 GBV_ILN_100 GBV_ILN_105 GBV_ILN_267 GBV_ILN_2006 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2016 GBV_ILN_2020 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_4036 GBV_ILN_4046 GBV_ILN_4266 GBV_ILN_4307 GBV_ILN_4314 GBV_ILN_4316 GBV_ILN_4317 GBV_ILN_4319 GBV_ILN_4328 AR 116 1999 6 06 370-375
allfieldsSound	10.1007/BF03159198 doi (DE-627)OLC2065314567 (DE-He213)BF03159198-p DE-627 ger DE-627 rakwb eng 620 004 070 VZ Priore, P. verfasserin aut Learning-based scheduling in a job shop 1999 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer 1999 Abstract A common way of dynamically scheduling jobs in a manufacturing system is by means of dispatching rules. The problem of this method is that the performance of these rules depends on the state the system is in at each moment, and no rule exists that overrules the rest in all the possible states that the system may be in. The system’s state is defined by a set of control attributes. It would therefore be interesting to use the most appropriate dispatching rule at each moment. To achieve this goal, a scheduling approach which uses machine learning is presented in this paper. By means of this technique, by analysing the previous performance of the system (training examples), a set of heuristic rules are generated that can be used to decide which is the most appropriate dispatching rule at each moment in time. This approach is applied to a job shop configuration. The results demonstrate that this approach produces an improvement in the performance of the system when compared to the traditional method of using dispatching rules. learning-based scheduling job shop systems machine learning de la Fuente, D. aut Enthalten in Elektrotechnik und Informationstechnik Springer-Verlag, 1988 116(1999), 6 vom: Juni, Seite 370-375 30 cm (DE-627)129622184 (DE-600)246725-2 (DE-576)015132323 0932-383X nnns volume:116 year:1999 number:6 month:06 pages:370-375 https://doi.org/10.1007/BF03159198 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-MAT GBV_ILN_11 GBV_ILN_20 GBV_ILN_21 GBV_ILN_32 GBV_ILN_34 GBV_ILN_62 GBV_ILN_70 GBV_ILN_100 GBV_ILN_105 GBV_ILN_267 GBV_ILN_2006 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2016 GBV_ILN_2020 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_4036 GBV_ILN_4046 GBV_ILN_4266 GBV_ILN_4307 GBV_ILN_4314 GBV_ILN_4316 GBV_ILN_4317 GBV_ILN_4319 GBV_ILN_4328 AR 116 1999 6 06 370-375
language	English
source	Enthalten in Elektrotechnik und Informationstechnik 116(1999), 6 vom: Juni, Seite 370-375 volume:116 year:1999 number:6 month:06 pages:370-375
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topic_title	620 004 070 VZ Learning-based scheduling in a job shop learning-based scheduling job shop systems machine learning
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title	Learning-based scheduling in a job shop
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title_full	Learning-based scheduling in a job shop
author_sort	Priore, P.
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author_browse	Priore, P. de la Fuente, D.
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title_sort	learning-based scheduling in a job shop
title_auth	Learning-based scheduling in a job shop
abstract	Abstract A common way of dynamically scheduling jobs in a manufacturing system is by means of dispatching rules. The problem of this method is that the performance of these rules depends on the state the system is in at each moment, and no rule exists that overrules the rest in all the possible states that the system may be in. The system’s state is defined by a set of control attributes. It would therefore be interesting to use the most appropriate dispatching rule at each moment. To achieve this goal, a scheduling approach which uses machine learning is presented in this paper. By means of this technique, by analysing the previous performance of the system (training examples), a set of heuristic rules are generated that can be used to decide which is the most appropriate dispatching rule at each moment in time. This approach is applied to a job shop configuration. The results demonstrate that this approach produces an improvement in the performance of the system when compared to the traditional method of using dispatching rules. © Springer 1999
abstractGer	Abstract A common way of dynamically scheduling jobs in a manufacturing system is by means of dispatching rules. The problem of this method is that the performance of these rules depends on the state the system is in at each moment, and no rule exists that overrules the rest in all the possible states that the system may be in. The system’s state is defined by a set of control attributes. It would therefore be interesting to use the most appropriate dispatching rule at each moment. To achieve this goal, a scheduling approach which uses machine learning is presented in this paper. By means of this technique, by analysing the previous performance of the system (training examples), a set of heuristic rules are generated that can be used to decide which is the most appropriate dispatching rule at each moment in time. This approach is applied to a job shop configuration. The results demonstrate that this approach produces an improvement in the performance of the system when compared to the traditional method of using dispatching rules. © Springer 1999
abstract_unstemmed	Abstract A common way of dynamically scheduling jobs in a manufacturing system is by means of dispatching rules. The problem of this method is that the performance of these rules depends on the state the system is in at each moment, and no rule exists that overrules the rest in all the possible states that the system may be in. The system’s state is defined by a set of control attributes. It would therefore be interesting to use the most appropriate dispatching rule at each moment. To achieve this goal, a scheduling approach which uses machine learning is presented in this paper. By means of this technique, by analysing the previous performance of the system (training examples), a set of heuristic rules are generated that can be used to decide which is the most appropriate dispatching rule at each moment in time. This approach is applied to a job shop configuration. The results demonstrate that this approach produces an improvement in the performance of the system when compared to the traditional method of using dispatching rules. © Springer 1999
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container_issue	6
title_short	Learning-based scheduling in a job shop
url	https://doi.org/10.1007/BF03159198
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