Using recurrent neural networks for the performance analysis and optimization of stochastic milkrun-supplied flow lines

Long-term throughput, as a key performance indicator of a stochastic flow line, is affected by numerous parameters describing the features of the flow line, such as processing time and buffer size. Fast and accurate evaluation methods for a given set of values for those parameters are a prerequisite...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Südbeck, Insa [verfasserIn] Mindlina, Julia [verfasserIn] Schnabel, André - 1987- [verfasserIn] Helber, Stefan - 1963- [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Rechteinformationen:	Open Access Namensnennung 4.0 International ; CC BY 4.0

Schlagwörter:	Buffer allocation Flow line Milk run Neural network Performance Evaluation

Übergeordnetes Werk:	Enthalten in: Schmalenbach journal of business research - Wiesbaden : Springer Fachmedien, 1997, 76(2024), 2 vom: Juni, Seite 267-291
Übergeordnetes Werk:	volume:76 ; year:2024 ; number:2 ; month:06 ; pages:267-291

Links:	Link aufrufen Link aufrufen Link aufrufen

DOI / URN:	10.1007/s41471-024-00183-5

Katalog-ID:	1891480065

Internformat


LEADER	01000caa a2200265 4500
001	1891480065
003	DE-627
005	20240827132549.0
007	cr uuu---uuuuu
008	240618s2024 xx \|\|\|\|\|o 00\| \|\|eng c
024	7		\|a 10.1007/s41471-024-00183-5 \|2 doi
024	7		\|a 10419/298800 \|2 hdl
035			\|a (DE-627)1891480065
035			\|a (DE-599)KXP1891480065
040			\|a DE-627 \|b ger \|c DE-627 \|e rda
041			\|a eng
084			\|a C45 \|a C61 \|a M11 \|2 jelc
100	1		\|a Südbeck, Insa \|e verfasserin \|4 aut
245	1	0	\|a Using recurrent neural networks for the performance analysis and optimization of stochastic milkrun-supplied flow lines \|c Insa Südbeck, Julia Mindlina, André Schnabel, Stefan Helber
264		1	\|c 2024
336			\|a Text \|b txt \|2 rdacontent
337			\|a Computermedien \|b c \|2 rdamedia
338			\|a Online-Ressource \|b cr \|2 rdacarrier
506	0		\|q DE-206 \|a Open Access \|e Controlled Vocabulary for Access Rights \|u http://purl.org/coar/access_right/c_abf2
520			\|a Long-term throughput, as a key performance indicator of a stochastic flow line, is affected by numerous parameters describing the features of the flow line, such as processing time and buffer size. Fast and accurate evaluation methods for a given set of values for those parameters are a prerequisite to systematically optimize such a flow line. In this paper, we consider the case of a flow line with random processing times, limited buffer capacities and so-called milkruns that supply the machines with material parts that are required to perform, e.g., assembly operations on workpieces. In such a system, shortages in the supply of material parts can limit the performance of the flow line. Up to now, there are no accurate analytical approaches to quantify the complex interactions in such milkrun-supplied flow lines for realistic problem sizes. We propose to use recurrent neural networks to determine the long-term throughput of such flow lines enabling us to evaluate production systems of flexible size. Our results show that the throughput can be determined accurately and quickly via recurrent neural networks. Furthermore, we use this new evaluation procedure as a building block to optimize this type of flow line using gradient and local search techniques.
540			\|q DE-206 \|a Namensnennung 4.0 International \|f CC BY 4.0 \|2 cc \|u https://creativecommons.org/licenses/by/4.0/
650		4	\|a Buffer allocation \|7 (dpeaa)DE-206
650		4	\|a Flow line \|7 (dpeaa)DE-206
650		4	\|a Milk run \|7 (dpeaa)DE-206
650		4	\|a Neural network \|7 (dpeaa)DE-206
650		4	\|a Performance Evaluation \|7 (dpeaa)DE-206
700	1		\|a Mindlina, Julia \|e verfasserin \|0 (DE-588)1183025556 \|0 (DE-627)1663000514 \|4 aut
700	1		\|a Schnabel, André \|d 1987- \|e verfasserin \|0 (DE-588)1217072446 \|0 (DE-627)1728880173 \|4 aut
700	1		\|a Helber, Stefan \|d 1963- \|e verfasserin \|0 (DE-588)121396584 \|0 (DE-627)081280858 \|0 (DE-576)292688954 \|4 aut
773	0	8	\|i Enthalten in \|t Schmalenbach journal of business research \|d Wiesbaden : Springer Fachmedien, 1997 \|g 76(2024), 2 vom: Juni, Seite 267-291 \|h Online-Ressource \|w (DE-627)34585828X \|w (DE-600)2076397-9 \|w (DE-576)114818126 \|x 2366-6153 \|7 nnns
773	1	8	\|g volume:76 \|g year:2024 \|g number:2 \|g month:06 \|g pages:267-291
856	4	0	\|u https://link.springer.com/content/pdf/10.1007/s41471-024-00183-5.pdf \|x Verlag \|z kostenfrei
856	4	0	\|u https://doi.org/10.1007/s41471-024-00183-5 \|x Resolving-System \|z kostenfrei
856	4	0	\|u https://hdl.handle.net/10419/298800 \|x Resolving-System \|z kostenfrei
912			\|a GBV_USEFLAG_U
912			\|a GBV_ILN_26
912			\|a ISIL_DE-206
912			\|a SYSFLAG_1
912			\|a GBV_KXP
912			\|a GBV_ILN_20
912			\|a GBV_ILN_22
912			\|a GBV_ILN_23
912			\|a GBV_ILN_24
912			\|a GBV_ILN_31
912			\|a GBV_ILN_39
912			\|a GBV_ILN_40
912			\|a GBV_ILN_60
912			\|a GBV_ILN_62
912			\|a GBV_ILN_63
912			\|a GBV_ILN_65
912			\|a GBV_ILN_69
912			\|a GBV_ILN_70
912			\|a GBV_ILN_73
912			\|a GBV_ILN_95
912			\|a GBV_ILN_105
912			\|a GBV_ILN_110
912			\|a GBV_ILN_120
912			\|a GBV_ILN_151
912			\|a GBV_ILN_152
912			\|a GBV_ILN_161
912			\|a GBV_ILN_170
912			\|a GBV_ILN_184
912			\|a GBV_ILN_187
912			\|a GBV_ILN_206
912			\|a GBV_ILN_213
912			\|a GBV_ILN_230
912			\|a GBV_ILN_285
912			\|a GBV_ILN_293
912			\|a GBV_ILN_370
912			\|a GBV_ILN_602
912			\|a GBV_ILN_2006
912			\|a GBV_ILN_2009
912			\|a GBV_ILN_2010
912			\|a GBV_ILN_2014
912			\|a GBV_ILN_2020
912			\|a GBV_ILN_2021
912			\|a GBV_ILN_2027
912			\|a GBV_ILN_2034
912			\|a GBV_ILN_2050
912			\|a GBV_ILN_2055
912			\|a GBV_ILN_2111
912			\|a GBV_ILN_4012
912			\|a GBV_ILN_4037
912			\|a GBV_ILN_4046
912			\|a GBV_ILN_4112
912			\|a GBV_ILN_4125
912			\|a GBV_ILN_4126
912			\|a GBV_ILN_4249
912			\|a GBV_ILN_4305
912			\|a GBV_ILN_4306
912			\|a GBV_ILN_4307
912			\|a GBV_ILN_4313
912			\|a GBV_ILN_4322
912			\|a GBV_ILN_4323
912			\|a GBV_ILN_4324
912			\|a GBV_ILN_4325
912			\|a GBV_ILN_4326
912			\|a GBV_ILN_4328
912			\|a GBV_ILN_4335
912			\|a GBV_ILN_4338
912			\|a GBV_ILN_4367
912			\|a GBV_ILN_4700
912			\|a GBV_ILN_4753
912			\|a GBV_ILN_2403
912			\|a GBV_ILN_2403
912			\|a ISIL_DE-LFER
951			\|a AR
952			\|d 76 \|j 2024 \|e 2 \|c 6 \|h 267-291
980			\|2 26 \|1 01 \|x 0206 \|b 4539824678 \|y x1z \|z 18-06-24
980			\|2 2403 \|1 01 \|x DE-LFER \|b 4547789029 \|c 00 \|f --%%-- \|d --%%-- \|e n \|j --%%-- \|y l01 \|z 08-07-24
981			\|2 2403 \|1 01 \|x DE-LFER \|r https://doi.org/10.1007/s41471-024-00183-5
981			\|2 2403 \|1 01 \|x DE-LFER \|r https://link.springer.com/content/pdf/10.1007/s41471-024-00183-5.pdf

Indexfelder

author_variant	i s is j m jm a s as s h sh
matchkey_str	article:23666153:2024----::snrcreterlewrsoteefracaayiadpiiainftcat
hierarchy_sort_str	2024
publishDate	2024
allfields	10.1007/s41471-024-00183-5 doi 10419/298800 hdl (DE-627)1891480065 (DE-599)KXP1891480065 DE-627 ger DE-627 rda eng C45 C61 M11 jelc Südbeck, Insa verfasserin aut Using recurrent neural networks for the performance analysis and optimization of stochastic milkrun-supplied flow lines Insa Südbeck, Julia Mindlina, André Schnabel, Stefan Helber 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Long-term throughput, as a key performance indicator of a stochastic flow line, is affected by numerous parameters describing the features of the flow line, such as processing time and buffer size. Fast and accurate evaluation methods for a given set of values for those parameters are a prerequisite to systematically optimize such a flow line. In this paper, we consider the case of a flow line with random processing times, limited buffer capacities and so-called milkruns that supply the machines with material parts that are required to perform, e.g., assembly operations on workpieces. In such a system, shortages in the supply of material parts can limit the performance of the flow line. Up to now, there are no accurate analytical approaches to quantify the complex interactions in such milkrun-supplied flow lines for realistic problem sizes. We propose to use recurrent neural networks to determine the long-term throughput of such flow lines enabling us to evaluate production systems of flexible size. Our results show that the throughput can be determined accurately and quickly via recurrent neural networks. Furthermore, we use this new evaluation procedure as a building block to optimize this type of flow line using gradient and local search techniques. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Buffer allocation (dpeaa)DE-206 Flow line (dpeaa)DE-206 Milk run (dpeaa)DE-206 Neural network (dpeaa)DE-206 Performance Evaluation (dpeaa)DE-206 Mindlina, Julia verfasserin (DE-588)1183025556 (DE-627)1663000514 aut Schnabel, André 1987- verfasserin (DE-588)1217072446 (DE-627)1728880173 aut Helber, Stefan 1963- verfasserin (DE-588)121396584 (DE-627)081280858 (DE-576)292688954 aut Enthalten in Schmalenbach journal of business research Wiesbaden : Springer Fachmedien, 1997 76(2024), 2 vom: Juni, Seite 267-291 Online-Ressource (DE-627)34585828X (DE-600)2076397-9 (DE-576)114818126 2366-6153 nnns volume:76 year:2024 number:2 month:06 pages:267-291 https://link.springer.com/content/pdf/10.1007/s41471-024-00183-5.pdf Verlag kostenfrei https://doi.org/10.1007/s41471-024-00183-5 Resolving-System kostenfrei https://hdl.handle.net/10419/298800 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_184 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_4753 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 76 2024 2 6 267-291 26 01 0206 4539824678 x1z 18-06-24 2403 01 DE-LFER 4547789029 00 --%%-- --%%-- n --%%-- l01 08-07-24 2403 01 DE-LFER https://doi.org/10.1007/s41471-024-00183-5 2403 01 DE-LFER https://link.springer.com/content/pdf/10.1007/s41471-024-00183-5.pdf
spelling	10.1007/s41471-024-00183-5 doi 10419/298800 hdl (DE-627)1891480065 (DE-599)KXP1891480065 DE-627 ger DE-627 rda eng C45 C61 M11 jelc Südbeck, Insa verfasserin aut Using recurrent neural networks for the performance analysis and optimization of stochastic milkrun-supplied flow lines Insa Südbeck, Julia Mindlina, André Schnabel, Stefan Helber 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Long-term throughput, as a key performance indicator of a stochastic flow line, is affected by numerous parameters describing the features of the flow line, such as processing time and buffer size. Fast and accurate evaluation methods for a given set of values for those parameters are a prerequisite to systematically optimize such a flow line. In this paper, we consider the case of a flow line with random processing times, limited buffer capacities and so-called milkruns that supply the machines with material parts that are required to perform, e.g., assembly operations on workpieces. In such a system, shortages in the supply of material parts can limit the performance of the flow line. Up to now, there are no accurate analytical approaches to quantify the complex interactions in such milkrun-supplied flow lines for realistic problem sizes. We propose to use recurrent neural networks to determine the long-term throughput of such flow lines enabling us to evaluate production systems of flexible size. Our results show that the throughput can be determined accurately and quickly via recurrent neural networks. Furthermore, we use this new evaluation procedure as a building block to optimize this type of flow line using gradient and local search techniques. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Buffer allocation (dpeaa)DE-206 Flow line (dpeaa)DE-206 Milk run (dpeaa)DE-206 Neural network (dpeaa)DE-206 Performance Evaluation (dpeaa)DE-206 Mindlina, Julia verfasserin (DE-588)1183025556 (DE-627)1663000514 aut Schnabel, André 1987- verfasserin (DE-588)1217072446 (DE-627)1728880173 aut Helber, Stefan 1963- verfasserin (DE-588)121396584 (DE-627)081280858 (DE-576)292688954 aut Enthalten in Schmalenbach journal of business research Wiesbaden : Springer Fachmedien, 1997 76(2024), 2 vom: Juni, Seite 267-291 Online-Ressource (DE-627)34585828X (DE-600)2076397-9 (DE-576)114818126 2366-6153 nnns volume:76 year:2024 number:2 month:06 pages:267-291 https://link.springer.com/content/pdf/10.1007/s41471-024-00183-5.pdf Verlag kostenfrei https://doi.org/10.1007/s41471-024-00183-5 Resolving-System kostenfrei https://hdl.handle.net/10419/298800 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_184 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_4753 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 76 2024 2 6 267-291 26 01 0206 4539824678 x1z 18-06-24 2403 01 DE-LFER 4547789029 00 --%%-- --%%-- n --%%-- l01 08-07-24 2403 01 DE-LFER https://doi.org/10.1007/s41471-024-00183-5 2403 01 DE-LFER https://link.springer.com/content/pdf/10.1007/s41471-024-00183-5.pdf
allfields_unstemmed	10.1007/s41471-024-00183-5 doi 10419/298800 hdl (DE-627)1891480065 (DE-599)KXP1891480065 DE-627 ger DE-627 rda eng C45 C61 M11 jelc Südbeck, Insa verfasserin aut Using recurrent neural networks for the performance analysis and optimization of stochastic milkrun-supplied flow lines Insa Südbeck, Julia Mindlina, André Schnabel, Stefan Helber 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Long-term throughput, as a key performance indicator of a stochastic flow line, is affected by numerous parameters describing the features of the flow line, such as processing time and buffer size. Fast and accurate evaluation methods for a given set of values for those parameters are a prerequisite to systematically optimize such a flow line. In this paper, we consider the case of a flow line with random processing times, limited buffer capacities and so-called milkruns that supply the machines with material parts that are required to perform, e.g., assembly operations on workpieces. In such a system, shortages in the supply of material parts can limit the performance of the flow line. Up to now, there are no accurate analytical approaches to quantify the complex interactions in such milkrun-supplied flow lines for realistic problem sizes. We propose to use recurrent neural networks to determine the long-term throughput of such flow lines enabling us to evaluate production systems of flexible size. Our results show that the throughput can be determined accurately and quickly via recurrent neural networks. Furthermore, we use this new evaluation procedure as a building block to optimize this type of flow line using gradient and local search techniques. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Buffer allocation (dpeaa)DE-206 Flow line (dpeaa)DE-206 Milk run (dpeaa)DE-206 Neural network (dpeaa)DE-206 Performance Evaluation (dpeaa)DE-206 Mindlina, Julia verfasserin (DE-588)1183025556 (DE-627)1663000514 aut Schnabel, André 1987- verfasserin (DE-588)1217072446 (DE-627)1728880173 aut Helber, Stefan 1963- verfasserin (DE-588)121396584 (DE-627)081280858 (DE-576)292688954 aut Enthalten in Schmalenbach journal of business research Wiesbaden : Springer Fachmedien, 1997 76(2024), 2 vom: Juni, Seite 267-291 Online-Ressource (DE-627)34585828X (DE-600)2076397-9 (DE-576)114818126 2366-6153 nnns volume:76 year:2024 number:2 month:06 pages:267-291 https://link.springer.com/content/pdf/10.1007/s41471-024-00183-5.pdf Verlag kostenfrei https://doi.org/10.1007/s41471-024-00183-5 Resolving-System kostenfrei https://hdl.handle.net/10419/298800 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_184 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_4753 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 76 2024 2 6 267-291 26 01 0206 4539824678 x1z 18-06-24 2403 01 DE-LFER 4547789029 00 --%%-- --%%-- n --%%-- l01 08-07-24 2403 01 DE-LFER https://doi.org/10.1007/s41471-024-00183-5 2403 01 DE-LFER https://link.springer.com/content/pdf/10.1007/s41471-024-00183-5.pdf
allfieldsGer	10.1007/s41471-024-00183-5 doi 10419/298800 hdl (DE-627)1891480065 (DE-599)KXP1891480065 DE-627 ger DE-627 rda eng C45 C61 M11 jelc Südbeck, Insa verfasserin aut Using recurrent neural networks for the performance analysis and optimization of stochastic milkrun-supplied flow lines Insa Südbeck, Julia Mindlina, André Schnabel, Stefan Helber 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Long-term throughput, as a key performance indicator of a stochastic flow line, is affected by numerous parameters describing the features of the flow line, such as processing time and buffer size. Fast and accurate evaluation methods for a given set of values for those parameters are a prerequisite to systematically optimize such a flow line. In this paper, we consider the case of a flow line with random processing times, limited buffer capacities and so-called milkruns that supply the machines with material parts that are required to perform, e.g., assembly operations on workpieces. In such a system, shortages in the supply of material parts can limit the performance of the flow line. Up to now, there are no accurate analytical approaches to quantify the complex interactions in such milkrun-supplied flow lines for realistic problem sizes. We propose to use recurrent neural networks to determine the long-term throughput of such flow lines enabling us to evaluate production systems of flexible size. Our results show that the throughput can be determined accurately and quickly via recurrent neural networks. Furthermore, we use this new evaluation procedure as a building block to optimize this type of flow line using gradient and local search techniques. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Buffer allocation (dpeaa)DE-206 Flow line (dpeaa)DE-206 Milk run (dpeaa)DE-206 Neural network (dpeaa)DE-206 Performance Evaluation (dpeaa)DE-206 Mindlina, Julia verfasserin (DE-588)1183025556 (DE-627)1663000514 aut Schnabel, André 1987- verfasserin (DE-588)1217072446 (DE-627)1728880173 aut Helber, Stefan 1963- verfasserin (DE-588)121396584 (DE-627)081280858 (DE-576)292688954 aut Enthalten in Schmalenbach journal of business research Wiesbaden : Springer Fachmedien, 1997 76(2024), 2 vom: Juni, Seite 267-291 Online-Ressource (DE-627)34585828X (DE-600)2076397-9 (DE-576)114818126 2366-6153 nnns volume:76 year:2024 number:2 month:06 pages:267-291 https://link.springer.com/content/pdf/10.1007/s41471-024-00183-5.pdf Verlag kostenfrei https://doi.org/10.1007/s41471-024-00183-5 Resolving-System kostenfrei https://hdl.handle.net/10419/298800 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_184 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_4753 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 76 2024 2 6 267-291 26 01 0206 4539824678 x1z 18-06-24 2403 01 DE-LFER 4547789029 00 --%%-- --%%-- n --%%-- l01 08-07-24 2403 01 DE-LFER https://doi.org/10.1007/s41471-024-00183-5 2403 01 DE-LFER https://link.springer.com/content/pdf/10.1007/s41471-024-00183-5.pdf
allfieldsSound	10.1007/s41471-024-00183-5 doi 10419/298800 hdl (DE-627)1891480065 (DE-599)KXP1891480065 DE-627 ger DE-627 rda eng C45 C61 M11 jelc Südbeck, Insa verfasserin aut Using recurrent neural networks for the performance analysis and optimization of stochastic milkrun-supplied flow lines Insa Südbeck, Julia Mindlina, André Schnabel, Stefan Helber 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Long-term throughput, as a key performance indicator of a stochastic flow line, is affected by numerous parameters describing the features of the flow line, such as processing time and buffer size. Fast and accurate evaluation methods for a given set of values for those parameters are a prerequisite to systematically optimize such a flow line. In this paper, we consider the case of a flow line with random processing times, limited buffer capacities and so-called milkruns that supply the machines with material parts that are required to perform, e.g., assembly operations on workpieces. In such a system, shortages in the supply of material parts can limit the performance of the flow line. Up to now, there are no accurate analytical approaches to quantify the complex interactions in such milkrun-supplied flow lines for realistic problem sizes. We propose to use recurrent neural networks to determine the long-term throughput of such flow lines enabling us to evaluate production systems of flexible size. Our results show that the throughput can be determined accurately and quickly via recurrent neural networks. Furthermore, we use this new evaluation procedure as a building block to optimize this type of flow line using gradient and local search techniques. DE-206 Namensnennung 4.0 International CC BY 4.0 cc https://creativecommons.org/licenses/by/4.0/ Buffer allocation (dpeaa)DE-206 Flow line (dpeaa)DE-206 Milk run (dpeaa)DE-206 Neural network (dpeaa)DE-206 Performance Evaluation (dpeaa)DE-206 Mindlina, Julia verfasserin (DE-588)1183025556 (DE-627)1663000514 aut Schnabel, André 1987- verfasserin (DE-588)1217072446 (DE-627)1728880173 aut Helber, Stefan 1963- verfasserin (DE-588)121396584 (DE-627)081280858 (DE-576)292688954 aut Enthalten in Schmalenbach journal of business research Wiesbaden : Springer Fachmedien, 1997 76(2024), 2 vom: Juni, Seite 267-291 Online-Ressource (DE-627)34585828X (DE-600)2076397-9 (DE-576)114818126 2366-6153 nnns volume:76 year:2024 number:2 month:06 pages:267-291 https://link.springer.com/content/pdf/10.1007/s41471-024-00183-5.pdf Verlag kostenfrei https://doi.org/10.1007/s41471-024-00183-5 Resolving-System kostenfrei https://hdl.handle.net/10419/298800 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_184 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_4753 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 76 2024 2 6 267-291 26 01 0206 4539824678 x1z 18-06-24 2403 01 DE-LFER 4547789029 00 --%%-- --%%-- n --%%-- l01 08-07-24 2403 01 DE-LFER https://doi.org/10.1007/s41471-024-00183-5 2403 01 DE-LFER https://link.springer.com/content/pdf/10.1007/s41471-024-00183-5.pdf
language	English
source	Enthalten in Schmalenbach journal of business research 76(2024), 2 vom: Juni, Seite 267-291 volume:76 year:2024 number:2 month:06 pages:267-291
sourceStr	Enthalten in Schmalenbach journal of business research 76(2024), 2 vom: Juni, Seite 267-291 volume:76 year:2024 number:2 month:06 pages:267-291
format_phy_str_mv	Article
building	26:1 2403:0
institution	findex.gbv.de
selectbib_iln_str_mv	26@1z 2403@01
topic_facet	Buffer allocation Flow line Milk run Neural network Performance Evaluation
isfreeaccess_bool	true
container_title	Schmalenbach journal of business research
authorswithroles_txt_mv	Südbeck, Insa @@aut@@ Mindlina, Julia @@aut@@ Schnabel, André @@aut@@ Helber, Stefan @@aut@@
publishDateDaySort_date	2024-06-01T00:00:00Z
hierarchy_top_id	34585828X
id	1891480065
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">1891480065</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240827132549.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240618s2024 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s41471-024-00183-5</subfield><subfield code="2">doi</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10419/298800</subfield><subfield code="2">hdl</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)1891480065</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KXP1891480065</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">C45</subfield><subfield code="a">C61</subfield><subfield code="a">M11</subfield><subfield code="2">jelc</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Südbeck, Insa</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Using recurrent neural networks for the performance analysis and optimization of stochastic milkrun-supplied flow lines</subfield><subfield code="c">Insa Südbeck, Julia Mindlina, André Schnabel, Stefan Helber</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2024</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="506" ind1="0" ind2=" "><subfield code="q">DE-206</subfield><subfield code="a">Open Access</subfield><subfield code="e">Controlled Vocabulary for Access Rights</subfield><subfield code="u">http://purl.org/coar/access_right/c_abf2</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Long-term throughput, as a key performance indicator of a stochastic flow line, is affected by numerous parameters describing the features of the flow line, such as processing time and buffer size. Fast and accurate evaluation methods for a given set of values for those parameters are a prerequisite to systematically optimize such a flow line. In this paper, we consider the case of a flow line with random processing times, limited buffer capacities and so-called milkruns that supply the machines with material parts that are required to perform, e.g., assembly operations on workpieces. In such a system, shortages in the supply of material parts can limit the performance of the flow line. Up to now, there are no accurate analytical approaches to quantify the complex interactions in such milkrun-supplied flow lines for realistic problem sizes. We propose to use recurrent neural networks to determine the long-term throughput of such flow lines enabling us to evaluate production systems of flexible size. Our results show that the throughput can be determined accurately and quickly via recurrent neural networks. Furthermore, we use this new evaluation procedure as a building block to optimize this type of flow line using gradient and local search techniques.</subfield></datafield><datafield tag="540" ind1=" " ind2=" "><subfield code="q">DE-206</subfield><subfield code="a">Namensnennung 4.0 International</subfield><subfield code="f">CC BY 4.0</subfield><subfield code="2">cc</subfield><subfield code="u">https://creativecommons.org/licenses/by/4.0/</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Buffer allocation</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Flow line</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Milk run</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Neural network</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Performance Evaluation</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mindlina, Julia</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1183025556</subfield><subfield code="0">(DE-627)1663000514</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Schnabel, André</subfield><subfield code="d">1987-</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1217072446</subfield><subfield code="0">(DE-627)1728880173</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Helber, Stefan</subfield><subfield code="d">1963-</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)121396584</subfield><subfield code="0">(DE-627)081280858</subfield><subfield code="0">(DE-576)292688954</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Schmalenbach journal of business research</subfield><subfield code="d">Wiesbaden : Springer Fachmedien, 1997</subfield><subfield code="g">76(2024), 2 vom: Juni, Seite 267-291</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)34585828X</subfield><subfield code="w">(DE-600)2076397-9</subfield><subfield code="w">(DE-576)114818126</subfield><subfield code="x">2366-6153</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:76</subfield><subfield code="g">year:2024</subfield><subfield code="g">number:2</subfield><subfield code="g">month:06</subfield><subfield code="g">pages:267-291</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://link.springer.com/content/pdf/10.1007/s41471-024-00183-5.pdf</subfield><subfield code="x">Verlag</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1007/s41471-024-00183-5</subfield><subfield code="x">Resolving-System</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://hdl.handle.net/10419/298800</subfield><subfield code="x">Resolving-System</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_26</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_1</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_KXP</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_120</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_184</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_187</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4328</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4753</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2403</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2403</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-LFER</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">76</subfield><subfield code="j">2024</subfield><subfield code="e">2</subfield><subfield code="c">6</subfield><subfield code="h">267-291</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">01</subfield><subfield code="x">0206</subfield><subfield code="b">4539824678</subfield><subfield code="y">x1z</subfield><subfield code="z">18-06-24</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2403</subfield><subfield code="1">01</subfield><subfield code="x">DE-LFER</subfield><subfield code="b">4547789029</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">n</subfield><subfield code="j">--%%--</subfield><subfield code="y">l01</subfield><subfield code="z">08-07-24</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">2403</subfield><subfield code="1">01</subfield><subfield code="x">DE-LFER</subfield><subfield code="r">https://doi.org/10.1007/s41471-024-00183-5</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">2403</subfield><subfield code="1">01</subfield><subfield code="x">DE-LFER</subfield><subfield code="r">https://link.springer.com/content/pdf/10.1007/s41471-024-00183-5.pdf</subfield></datafield></record></collection>
standort_str_mv	--%%--
standort_iln_str_mv	2403:--%%-- DE-LFER:--%%--
author	Südbeck, Insa
spellingShingle	Südbeck, Insa jelc C45 misc Buffer allocation misc Flow line misc Milk run misc Neural network misc Performance Evaluation Using recurrent neural networks for the performance analysis and optimization of stochastic milkrun-supplied flow lines
authorStr	Südbeck, Insa
ppnlink_with_tag_str_mv	@@773@@(DE-627)34585828X
format	electronic Article
delete_txt_mv	keep
author_role	aut aut aut aut
typewithnormlink_str_mv	DifferentiatedPerson@(DE-588)1183025556 Person@(DE-588)1183025556 Person@(DE-588)1217072446 DifferentiatedPerson@(DE-588)1217072446 Person@(DE-588)121396584 DifferentiatedPerson@(DE-588)121396584
collection	KXP GVK SWB
remote_str	true
last_changed_iln_str_mv	26@18-06-24 2403@08-07-24
illustrated	Not Illustrated
issn	2366-6153
topic_title	C45 C61 M11 jelc Using recurrent neural networks for the performance analysis and optimization of stochastic milkrun-supplied flow lines Insa Südbeck, Julia Mindlina, André Schnabel, Stefan Helber Buffer allocation (dpeaa)DE-206 Flow line (dpeaa)DE-206 Milk run (dpeaa)DE-206 Neural network (dpeaa)DE-206 Performance Evaluation (dpeaa)DE-206
topic	jelc C45 misc Buffer allocation misc Flow line misc Milk run misc Neural network misc Performance Evaluation
topic_unstemmed	jelc C45 misc Buffer allocation misc Flow line misc Milk run misc Neural network misc Performance Evaluation
topic_browse	jelc C45 misc Buffer allocation misc Flow line misc Milk run misc Neural network misc Performance Evaluation
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
standort_txtP_mv	--%%--
format_main_str_mv	Text Zeitschrift/Artikel
carriertype_str_mv	cr
hierarchy_parent_title	Schmalenbach journal of business research
normlinkwithtype_str_mv	(DE-588)1183025556@DifferentiatedPerson (DE-588)1183025556@Person (DE-588)1217072446@Person (DE-588)1217072446@DifferentiatedPerson (DE-588)121396584@Person (DE-588)121396584@DifferentiatedPerson
hierarchy_parent_id	34585828X
signature	--%%--
signature_str_mv	--%%--
hierarchy_top_title	Schmalenbach journal of business research
isfreeaccess_txt	true
familylinks_str_mv	(DE-627)34585828X (DE-600)2076397-9 (DE-576)114818126
normlinkwithrole_str_mv	(DE-588)1183025556@@aut@@ (DE-588)1217072446@@aut@@ (DE-588)121396584@@aut@@
title	Using recurrent neural networks for the performance analysis and optimization of stochastic milkrun-supplied flow lines
ctrlnum	(DE-627)1891480065 (DE-599)KXP1891480065
title_full	Using recurrent neural networks for the performance analysis and optimization of stochastic milkrun-supplied flow lines Insa Südbeck, Julia Mindlina, André Schnabel, Stefan Helber
author_sort	Südbeck, Insa
journal	Schmalenbach journal of business research
journalStr	Schmalenbach journal of business research
callnumber-first-code	-
lang_code	eng
isOA_bool	true
recordtype	marc
publishDateSort	2024
contenttype_str_mv	txt
container_start_page	267
author_browse	Südbeck, Insa Mindlina, Julia Schnabel, André Helber, Stefan
selectkey	26:x 2403:l
container_volume	76
class	C45 C61 M11 jelc
format_se	Elektronische Aufsätze
author-letter	Südbeck, Insa
doi_str_mv	10.1007/s41471-024-00183-5
normlink	1183025556 1663000514 1217072446 1728880173 121396584 081280858 292688954
normlink_prefix_str_mv	(DE-588)1183025556 (DE-627)1663000514 (DE-588)1217072446 (DE-627)1728880173 (DE-588)121396584 (DE-627)081280858 (DE-576)292688954
author2-role	verfasserin
title_sort	using recurrent neural networks for the performance analysis and optimization of stochastic milkrun-supplied flow lines
title_auth	Using recurrent neural networks for the performance analysis and optimization of stochastic milkrun-supplied flow lines
abstract	Long-term throughput, as a key performance indicator of a stochastic flow line, is affected by numerous parameters describing the features of the flow line, such as processing time and buffer size. Fast and accurate evaluation methods for a given set of values for those parameters are a prerequisite to systematically optimize such a flow line. In this paper, we consider the case of a flow line with random processing times, limited buffer capacities and so-called milkruns that supply the machines with material parts that are required to perform, e.g., assembly operations on workpieces. In such a system, shortages in the supply of material parts can limit the performance of the flow line. Up to now, there are no accurate analytical approaches to quantify the complex interactions in such milkrun-supplied flow lines for realistic problem sizes. We propose to use recurrent neural networks to determine the long-term throughput of such flow lines enabling us to evaluate production systems of flexible size. Our results show that the throughput can be determined accurately and quickly via recurrent neural networks. Furthermore, we use this new evaluation procedure as a building block to optimize this type of flow line using gradient and local search techniques.
abstractGer	Long-term throughput, as a key performance indicator of a stochastic flow line, is affected by numerous parameters describing the features of the flow line, such as processing time and buffer size. Fast and accurate evaluation methods for a given set of values for those parameters are a prerequisite to systematically optimize such a flow line. In this paper, we consider the case of a flow line with random processing times, limited buffer capacities and so-called milkruns that supply the machines with material parts that are required to perform, e.g., assembly operations on workpieces. In such a system, shortages in the supply of material parts can limit the performance of the flow line. Up to now, there are no accurate analytical approaches to quantify the complex interactions in such milkrun-supplied flow lines for realistic problem sizes. We propose to use recurrent neural networks to determine the long-term throughput of such flow lines enabling us to evaluate production systems of flexible size. Our results show that the throughput can be determined accurately and quickly via recurrent neural networks. Furthermore, we use this new evaluation procedure as a building block to optimize this type of flow line using gradient and local search techniques.
abstract_unstemmed	Long-term throughput, as a key performance indicator of a stochastic flow line, is affected by numerous parameters describing the features of the flow line, such as processing time and buffer size. Fast and accurate evaluation methods for a given set of values for those parameters are a prerequisite to systematically optimize such a flow line. In this paper, we consider the case of a flow line with random processing times, limited buffer capacities and so-called milkruns that supply the machines with material parts that are required to perform, e.g., assembly operations on workpieces. In such a system, shortages in the supply of material parts can limit the performance of the flow line. Up to now, there are no accurate analytical approaches to quantify the complex interactions in such milkrun-supplied flow lines for realistic problem sizes. We propose to use recurrent neural networks to determine the long-term throughput of such flow lines enabling us to evaluate production systems of flexible size. Our results show that the throughput can be determined accurately and quickly via recurrent neural networks. Furthermore, we use this new evaluation procedure as a building block to optimize this type of flow line using gradient and local search techniques.
collection_details	GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_184 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2006 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2014 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 GBV_ILN_4753 GBV_ILN_2403 ISIL_DE-LFER
container_issue	2
title_short	Using recurrent neural networks for the performance analysis and optimization of stochastic milkrun-supplied flow lines
url	https://link.springer.com/content/pdf/10.1007/s41471-024-00183-5.pdf https://doi.org/10.1007/s41471-024-00183-5 https://hdl.handle.net/10419/298800
ausleihindikator_str_mv	26 2403:n
rolewithnormlink_str_mv	@@aut@@(DE-588)1183025556 @@aut@@(DE-588)1217072446 @@aut@@(DE-588)121396584
remote_bool	true
author2	Mindlina, Julia Schnabel, André 1987- Helber, Stefan 1963-
author2Str	Mindlina, Julia Schnabel, André 1987- Helber, Stefan 1963-
ppnlink	34585828X
GND_str_mv	Mindlina, Julia Schnabel, André Helber, Stefan
GND_txt_mv	Mindlina, Julia Schnabel, André Helber, Stefan
GND_txtF_mv	Mindlina, Julia Schnabel, André Helber, Stefan
mediatype_str_mv	c
isOA_txt	true
hochschulschrift_bool	false
doi_str	10.1007/s41471-024-00183-5
callnumber-a	--%%--
up_date	2024-08-28T04:31:11.872Z
_version_	1808604302538702848
fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">1891480065</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240827132549.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">240618s2024 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s41471-024-00183-5</subfield><subfield code="2">doi</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10419/298800</subfield><subfield code="2">hdl</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)1891480065</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)KXP1891480065</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">C45</subfield><subfield code="a">C61</subfield><subfield code="a">M11</subfield><subfield code="2">jelc</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Südbeck, Insa</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Using recurrent neural networks for the performance analysis and optimization of stochastic milkrun-supplied flow lines</subfield><subfield code="c">Insa Südbeck, Julia Mindlina, André Schnabel, Stefan Helber</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2024</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="506" ind1="0" ind2=" "><subfield code="q">DE-206</subfield><subfield code="a">Open Access</subfield><subfield code="e">Controlled Vocabulary for Access Rights</subfield><subfield code="u">http://purl.org/coar/access_right/c_abf2</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Long-term throughput, as a key performance indicator of a stochastic flow line, is affected by numerous parameters describing the features of the flow line, such as processing time and buffer size. Fast and accurate evaluation methods for a given set of values for those parameters are a prerequisite to systematically optimize such a flow line. In this paper, we consider the case of a flow line with random processing times, limited buffer capacities and so-called milkruns that supply the machines with material parts that are required to perform, e.g., assembly operations on workpieces. In such a system, shortages in the supply of material parts can limit the performance of the flow line. Up to now, there are no accurate analytical approaches to quantify the complex interactions in such milkrun-supplied flow lines for realistic problem sizes. We propose to use recurrent neural networks to determine the long-term throughput of such flow lines enabling us to evaluate production systems of flexible size. Our results show that the throughput can be determined accurately and quickly via recurrent neural networks. Furthermore, we use this new evaluation procedure as a building block to optimize this type of flow line using gradient and local search techniques.</subfield></datafield><datafield tag="540" ind1=" " ind2=" "><subfield code="q">DE-206</subfield><subfield code="a">Namensnennung 4.0 International</subfield><subfield code="f">CC BY 4.0</subfield><subfield code="2">cc</subfield><subfield code="u">https://creativecommons.org/licenses/by/4.0/</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Buffer allocation</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Flow line</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Milk run</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Neural network</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Performance Evaluation</subfield><subfield code="7">(dpeaa)DE-206</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Mindlina, Julia</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1183025556</subfield><subfield code="0">(DE-627)1663000514</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Schnabel, André</subfield><subfield code="d">1987-</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)1217072446</subfield><subfield code="0">(DE-627)1728880173</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Helber, Stefan</subfield><subfield code="d">1963-</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(DE-588)121396584</subfield><subfield code="0">(DE-627)081280858</subfield><subfield code="0">(DE-576)292688954</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Schmalenbach journal of business research</subfield><subfield code="d">Wiesbaden : Springer Fachmedien, 1997</subfield><subfield code="g">76(2024), 2 vom: Juni, Seite 267-291</subfield><subfield code="h">Online-Ressource</subfield><subfield code="w">(DE-627)34585828X</subfield><subfield code="w">(DE-600)2076397-9</subfield><subfield code="w">(DE-576)114818126</subfield><subfield code="x">2366-6153</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:76</subfield><subfield code="g">year:2024</subfield><subfield code="g">number:2</subfield><subfield code="g">month:06</subfield><subfield code="g">pages:267-291</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://link.springer.com/content/pdf/10.1007/s41471-024-00183-5.pdf</subfield><subfield code="x">Verlag</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1007/s41471-024-00183-5</subfield><subfield code="x">Resolving-System</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://hdl.handle.net/10419/298800</subfield><subfield code="x">Resolving-System</subfield><subfield code="z">kostenfrei</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_U</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_26</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_1</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_KXP</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_20</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_22</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_23</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_24</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_31</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_39</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_40</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_60</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_62</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_63</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_65</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_69</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_73</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_95</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_105</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_110</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_120</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_151</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_152</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_161</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_170</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_184</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_187</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_206</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_213</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_230</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_285</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_293</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_370</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_602</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2006</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2009</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2010</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2014</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2021</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2027</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2034</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2050</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2055</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2111</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4012</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4037</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4046</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4112</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4125</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4126</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4249</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4305</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4306</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4307</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4313</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4322</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4323</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4324</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4325</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4326</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4328</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4335</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4338</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4367</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4753</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2403</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2403</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ISIL_DE-LFER</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">76</subfield><subfield code="j">2024</subfield><subfield code="e">2</subfield><subfield code="c">6</subfield><subfield code="h">267-291</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">26</subfield><subfield code="1">01</subfield><subfield code="x">0206</subfield><subfield code="b">4539824678</subfield><subfield code="y">x1z</subfield><subfield code="z">18-06-24</subfield></datafield><datafield tag="980" ind1=" " ind2=" "><subfield code="2">2403</subfield><subfield code="1">01</subfield><subfield code="x">DE-LFER</subfield><subfield code="b">4547789029</subfield><subfield code="c">00</subfield><subfield code="f">--%%--</subfield><subfield code="d">--%%--</subfield><subfield code="e">n</subfield><subfield code="j">--%%--</subfield><subfield code="y">l01</subfield><subfield code="z">08-07-24</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">2403</subfield><subfield code="1">01</subfield><subfield code="x">DE-LFER</subfield><subfield code="r">https://doi.org/10.1007/s41471-024-00183-5</subfield></datafield><datafield tag="981" ind1=" " ind2=" "><subfield code="2">2403</subfield><subfield code="1">01</subfield><subfield code="x">DE-LFER</subfield><subfield code="r">https://link.springer.com/content/pdf/10.1007/s41471-024-00183-5.pdf</subfield></datafield></record></collection>
score	6.902795

Nicht das Richtige dabei?

Schreiben Sie uns!

Using recurrent neural networks for the performance analysis and optimization of stochastic milkrun-supplied flow lines

Nicht das Richtige dabei?

Zugang & Verfügbarkeit

Vorhandene Bände

Nicht das Richtige dabei?