Eintrag weiter verarbeiten
Verfügbar über Online-Ressource

Augmented Go & See: An approach for improved bottleneck identification in production lines

Gespeichert in:

Bibliographische Detailangaben
Zeitschriftentitel: Procedia Manufacturing
Personen und Körperschaften: Hofmann, Constantin, Staehr, Tom, Cohen, Samuel, Stricker, Nicole, Haefner, Benjamin, Lanza, Gisela
In: Procedia Manufacturing, 31, 2019, S. 148-154
Format: E-Article
Sprache: Englisch
veröffentlicht:
Elsevier BV
Schlagwörter:
Artificial Intelligence
Industrial and Manufacturing Engineering
author_facet Hofmann, Constantin
Staehr, Tom
Cohen, Samuel
Stricker, Nicole
Haefner, Benjamin
Lanza, Gisela
Hofmann, Constantin
Staehr, Tom
Cohen, Samuel
Stricker, Nicole
Haefner, Benjamin
Lanza, Gisela
author Hofmann, Constantin
Staehr, Tom
Cohen, Samuel
Stricker, Nicole
Haefner, Benjamin
Lanza, Gisela
spellingShingle Hofmann, Constantin
Staehr, Tom
Cohen, Samuel
Stricker, Nicole
Haefner, Benjamin
Lanza, Gisela
Procedia Manufacturing
Augmented Go & See: An approach for improved bottleneck identification in production lines
Artificial Intelligence
Industrial and Manufacturing Engineering
author_sort hofmann, constantin
spelling Hofmann, Constantin Staehr, Tom Cohen, Samuel Stricker, Nicole Haefner, Benjamin Lanza, Gisela 2351-9789 Elsevier BV Artificial Intelligence Industrial and Manufacturing Engineering http://dx.doi.org/10.1016/j.promfg.2019.03.023 Augmented Go & See: An approach for improved bottleneck identification in production lines Procedia Manufacturing
doi_str_mv 10.1016/j.promfg.2019.03.023
facet_avail Online
Free
finc_class_facet Informatik
Technik
format ElectronicArticle
fullrecord blob:ai-49-aHR0cDovL2R4LmRvaS5vcmcvMTAuMTAxNi9qLnByb21mZy4yMDE5LjAzLjAyMw
id ai-49-aHR0cDovL2R4LmRvaS5vcmcvMTAuMTAxNi9qLnByb21mZy4yMDE5LjAzLjAyMw
institution DE-D275
DE-Bn3
DE-Brt1
DE-Zwi2
DE-D161
DE-Gla1
DE-Zi4
DE-15
DE-Pl11
DE-Rs1
DE-105
DE-14
DE-Ch1
DE-L229
imprint Elsevier BV, 2019
imprint_str_mv Elsevier BV, 2019
issn 2351-9789
issn_str_mv 2351-9789
language English
mega_collection Elsevier BV (CrossRef)
match_str hofmann2019augmentedgoseeanapproachforimprovedbottleneckidentificationinproductionlines
publishDateSort 2019
publisher Elsevier BV
recordtype ai
record_format ai
series Procedia Manufacturing
source_id 49
title Augmented Go & See: An approach for improved bottleneck identification in production lines
title_unstemmed Augmented Go & See: An approach for improved bottleneck identification in production lines
title_full Augmented Go & See: An approach for improved bottleneck identification in production lines
title_fullStr Augmented Go & See: An approach for improved bottleneck identification in production lines
title_full_unstemmed Augmented Go & See: An approach for improved bottleneck identification in production lines
title_short Augmented Go & See: An approach for improved bottleneck identification in production lines
title_sort augmented go & see: an approach for improved bottleneck identification in production lines
topic Artificial Intelligence
Industrial and Manufacturing Engineering
url http://dx.doi.org/10.1016/j.promfg.2019.03.023
publishDate 2019
physical 148-154
description
container_start_page 148
container_title Procedia Manufacturing
container_volume 31
format_de105 Article, E-Article
format_de14 Article, E-Article
format_de15 Article, E-Article
format_de520 Article, E-Article
format_de540 Article, E-Article
format_dech1 Article, E-Article
format_ded117 Article, E-Article
format_degla1 E-Article
format_del152 Buch
format_del189 Article, E-Article
format_dezi4 Article
format_dezwi2 Article, E-Article
format_finc Article, E-Article
format_nrw Article, E-Article
_version_ 1792344845218480129
geogr_code not assigned
last_indexed 2024-03-01T17:14:04.795Z
geogr_code_person not assigned
openURL url_ver=Z39.88-2004&ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fvufind.svn.sourceforge.net%3Agenerator&rft.title=Augmented+Go+%26+See%3A+An+approach+for+improved+bottleneck+identification+in+production+lines&rft.date=2019-01-01&genre=article&issn=2351-9789&volume=31&spage=148&epage=154&pages=148-154&jtitle=Procedia+Manufacturing&atitle=Augmented+Go+%26+See%3A+An+approach+for+improved+bottleneck+identification+in+production+lines&aulast=Lanza&aufirst=Gisela&rft_id=info%3Adoi%2F10.1016%2Fj.promfg.2019.03.023&rft.language%5B0%5D=eng
SOLR
_version_ 1792344845218480129
author Hofmann, Constantin, Staehr, Tom, Cohen, Samuel, Stricker, Nicole, Haefner, Benjamin, Lanza, Gisela
author_facet Hofmann, Constantin, Staehr, Tom, Cohen, Samuel, Stricker, Nicole, Haefner, Benjamin, Lanza, Gisela, Hofmann, Constantin, Staehr, Tom, Cohen, Samuel, Stricker, Nicole, Haefner, Benjamin, Lanza, Gisela
author_sort hofmann, constantin
container_start_page 148
container_title Procedia Manufacturing
container_volume 31
description
doi_str_mv 10.1016/j.promfg.2019.03.023
facet_avail Online, Free
finc_class_facet Informatik, Technik
format ElectronicArticle
format_de105 Article, E-Article
format_de14 Article, E-Article
format_de15 Article, E-Article
format_de520 Article, E-Article
format_de540 Article, E-Article
format_dech1 Article, E-Article
format_ded117 Article, E-Article
format_degla1 E-Article
format_del152 Buch
format_del189 Article, E-Article
format_dezi4 Article
format_dezwi2 Article, E-Article
format_finc Article, E-Article
format_nrw Article, E-Article
geogr_code not assigned
geogr_code_person not assigned
id ai-49-aHR0cDovL2R4LmRvaS5vcmcvMTAuMTAxNi9qLnByb21mZy4yMDE5LjAzLjAyMw
imprint Elsevier BV, 2019
imprint_str_mv Elsevier BV, 2019
institution DE-D275, DE-Bn3, DE-Brt1, DE-Zwi2, DE-D161, DE-Gla1, DE-Zi4, DE-15, DE-Pl11, DE-Rs1, DE-105, DE-14, DE-Ch1, DE-L229
issn 2351-9789
issn_str_mv 2351-9789
language English
last_indexed 2024-03-01T17:14:04.795Z
match_str hofmann2019augmentedgoseeanapproachforimprovedbottleneckidentificationinproductionlines
mega_collection Elsevier BV (CrossRef)
physical 148-154
publishDate 2019
publishDateSort 2019
publisher Elsevier BV
record_format ai
recordtype ai
series Procedia Manufacturing
source_id 49
spelling Hofmann, Constantin Staehr, Tom Cohen, Samuel Stricker, Nicole Haefner, Benjamin Lanza, Gisela 2351-9789 Elsevier BV Artificial Intelligence Industrial and Manufacturing Engineering http://dx.doi.org/10.1016/j.promfg.2019.03.023 Augmented Go & See: An approach for improved bottleneck identification in production lines Procedia Manufacturing
spellingShingle Hofmann, Constantin, Staehr, Tom, Cohen, Samuel, Stricker, Nicole, Haefner, Benjamin, Lanza, Gisela, Procedia Manufacturing, Augmented Go & See: An approach for improved bottleneck identification in production lines, Artificial Intelligence, Industrial and Manufacturing Engineering
title Augmented Go & See: An approach for improved bottleneck identification in production lines
title_full Augmented Go & See: An approach for improved bottleneck identification in production lines
title_fullStr Augmented Go & See: An approach for improved bottleneck identification in production lines
title_full_unstemmed Augmented Go & See: An approach for improved bottleneck identification in production lines
title_short Augmented Go & See: An approach for improved bottleneck identification in production lines
title_sort augmented go & see: an approach for improved bottleneck identification in production lines
title_unstemmed Augmented Go & See: An approach for improved bottleneck identification in production lines
topic Artificial Intelligence, Industrial and Manufacturing Engineering
url http://dx.doi.org/10.1016/j.promfg.2019.03.023