Using linear programming to analyze and optimize stochastic flow lines
Abstract This paper presents a linear programming approach to analyze and optimize flow lines with limited buffer capacities and stochastic processing times. The basic idea is to solve a huge but simple linear program that models an entire simulation run of a multi-stage production process in discre...
Ausführliche Beschreibung
Autor*in: |
Helber, Stefan [verfasserIn] |
---|
Format: |
Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2010 |
---|
Schlagwörter: |
---|
Anmerkung: |
© Springer Science+Business Media, LLC 2010 |
---|
Übergeordnetes Werk: |
Enthalten in: Annals of operations research - Springer US, 1984, 182(2010), 1 vom: 11. Feb., Seite 193-211 |
---|---|
Übergeordnetes Werk: |
volume:182 ; year:2010 ; number:1 ; day:11 ; month:02 ; pages:193-211 |
Links: |
---|
DOI / URN: |
10.1007/s10479-010-0692-3 |
---|
Katalog-ID: |
OLC2111147249 |
---|
LEADER | 01000naa a22002652 4500 | ||
---|---|---|---|
001 | OLC2111147249 | ||
003 | DE-627 | ||
005 | 20230502202716.0 | ||
007 | tu | ||
008 | 230502s2010 xx ||||| 00| ||eng c | ||
024 | 7 | |a 10.1007/s10479-010-0692-3 |2 doi | |
035 | |a (DE-627)OLC2111147249 | ||
035 | |a (DE-He213)s10479-010-0692-3-p | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
082 | 0 | 4 | |a 004 |q VZ |
084 | |a 3,2 |2 ssgn | ||
100 | 1 | |a Helber, Stefan |e verfasserin |4 aut | |
245 | 1 | 0 | |a Using linear programming to analyze and optimize stochastic flow lines |
264 | 1 | |c 2010 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
338 | |a Band |b nc |2 rdacarrier | ||
500 | |a © Springer Science+Business Media, LLC 2010 | ||
520 | |a Abstract This paper presents a linear programming approach to analyze and optimize flow lines with limited buffer capacities and stochastic processing times. The basic idea is to solve a huge but simple linear program that models an entire simulation run of a multi-stage production process in discrete time, to determine a production rate estimate. As our methodology is purely numerical, it offers the full modeling flexibility of stochastic simulation with respect to the probability distribution of processing times. However, unlike discrete-event simulation models, it also offers the optimization power of linear programming and hence allows us to solve buffer allocation problems. We show under which conditions our method works well by comparing its results to exact values for two-machine models and approximate simulation results for longer lines. | ||
650 | 4 | |a Buffer Size | |
650 | 4 | |a Inventory Level | |
650 | 4 | |a Buffer Space | |
650 | 4 | |a Buffer Allocation | |
650 | 4 | |a Longe Line | |
700 | 1 | |a Schimmelpfeng, Katja |4 aut | |
700 | 1 | |a Stolletz, Raik |4 aut | |
700 | 1 | |a Lagershausen, Svenja |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Annals of operations research |d Springer US, 1984 |g 182(2010), 1 vom: 11. Feb., Seite 193-211 |w (DE-627)12964370X |w (DE-600)252629-3 |w (DE-576)018141862 |x 0254-5330 |
773 | 1 | 8 | |g volume:182 |g year:2010 |g number:1 |g day:11 |g month:02 |g pages:193-211 |
856 | 4 | 1 | |u https://doi.org/10.1007/s10479-010-0692-3 |z lizenzpflichtig |3 Volltext |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_OLC | ||
912 | |a SSG-OLC-WIW | ||
912 | |a SSG-OLC-MAT | ||
912 | |a GBV_ILN_26 | ||
912 | |a GBV_ILN_4029 | ||
912 | |a GBV_ILN_4317 | ||
951 | |a AR | ||
952 | |d 182 |j 2010 |e 1 |b 11 |c 02 |h 193-211 |
author_variant |
s h sh k s ks r s rs s l sl |
---|---|
matchkey_str |
article:02545330:2010----::snlnapormigonlzadpiiet |
hierarchy_sort_str |
2010 |
publishDate |
2010 |
allfields |
10.1007/s10479-010-0692-3 doi (DE-627)OLC2111147249 (DE-He213)s10479-010-0692-3-p DE-627 ger DE-627 rakwb eng 004 VZ 3,2 ssgn Helber, Stefan verfasserin aut Using linear programming to analyze and optimize stochastic flow lines 2010 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2010 Abstract This paper presents a linear programming approach to analyze and optimize flow lines with limited buffer capacities and stochastic processing times. The basic idea is to solve a huge but simple linear program that models an entire simulation run of a multi-stage production process in discrete time, to determine a production rate estimate. As our methodology is purely numerical, it offers the full modeling flexibility of stochastic simulation with respect to the probability distribution of processing times. However, unlike discrete-event simulation models, it also offers the optimization power of linear programming and hence allows us to solve buffer allocation problems. We show under which conditions our method works well by comparing its results to exact values for two-machine models and approximate simulation results for longer lines. Buffer Size Inventory Level Buffer Space Buffer Allocation Longe Line Schimmelpfeng, Katja aut Stolletz, Raik aut Lagershausen, Svenja aut Enthalten in Annals of operations research Springer US, 1984 182(2010), 1 vom: 11. Feb., Seite 193-211 (DE-627)12964370X (DE-600)252629-3 (DE-576)018141862 0254-5330 volume:182 year:2010 number:1 day:11 month:02 pages:193-211 https://doi.org/10.1007/s10479-010-0692-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OLC-MAT GBV_ILN_26 GBV_ILN_4029 GBV_ILN_4317 AR 182 2010 1 11 02 193-211 |
spelling |
10.1007/s10479-010-0692-3 doi (DE-627)OLC2111147249 (DE-He213)s10479-010-0692-3-p DE-627 ger DE-627 rakwb eng 004 VZ 3,2 ssgn Helber, Stefan verfasserin aut Using linear programming to analyze and optimize stochastic flow lines 2010 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2010 Abstract This paper presents a linear programming approach to analyze and optimize flow lines with limited buffer capacities and stochastic processing times. The basic idea is to solve a huge but simple linear program that models an entire simulation run of a multi-stage production process in discrete time, to determine a production rate estimate. As our methodology is purely numerical, it offers the full modeling flexibility of stochastic simulation with respect to the probability distribution of processing times. However, unlike discrete-event simulation models, it also offers the optimization power of linear programming and hence allows us to solve buffer allocation problems. We show under which conditions our method works well by comparing its results to exact values for two-machine models and approximate simulation results for longer lines. Buffer Size Inventory Level Buffer Space Buffer Allocation Longe Line Schimmelpfeng, Katja aut Stolletz, Raik aut Lagershausen, Svenja aut Enthalten in Annals of operations research Springer US, 1984 182(2010), 1 vom: 11. Feb., Seite 193-211 (DE-627)12964370X (DE-600)252629-3 (DE-576)018141862 0254-5330 volume:182 year:2010 number:1 day:11 month:02 pages:193-211 https://doi.org/10.1007/s10479-010-0692-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OLC-MAT GBV_ILN_26 GBV_ILN_4029 GBV_ILN_4317 AR 182 2010 1 11 02 193-211 |
allfields_unstemmed |
10.1007/s10479-010-0692-3 doi (DE-627)OLC2111147249 (DE-He213)s10479-010-0692-3-p DE-627 ger DE-627 rakwb eng 004 VZ 3,2 ssgn Helber, Stefan verfasserin aut Using linear programming to analyze and optimize stochastic flow lines 2010 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2010 Abstract This paper presents a linear programming approach to analyze and optimize flow lines with limited buffer capacities and stochastic processing times. The basic idea is to solve a huge but simple linear program that models an entire simulation run of a multi-stage production process in discrete time, to determine a production rate estimate. As our methodology is purely numerical, it offers the full modeling flexibility of stochastic simulation with respect to the probability distribution of processing times. However, unlike discrete-event simulation models, it also offers the optimization power of linear programming and hence allows us to solve buffer allocation problems. We show under which conditions our method works well by comparing its results to exact values for two-machine models and approximate simulation results for longer lines. Buffer Size Inventory Level Buffer Space Buffer Allocation Longe Line Schimmelpfeng, Katja aut Stolletz, Raik aut Lagershausen, Svenja aut Enthalten in Annals of operations research Springer US, 1984 182(2010), 1 vom: 11. Feb., Seite 193-211 (DE-627)12964370X (DE-600)252629-3 (DE-576)018141862 0254-5330 volume:182 year:2010 number:1 day:11 month:02 pages:193-211 https://doi.org/10.1007/s10479-010-0692-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OLC-MAT GBV_ILN_26 GBV_ILN_4029 GBV_ILN_4317 AR 182 2010 1 11 02 193-211 |
allfieldsGer |
10.1007/s10479-010-0692-3 doi (DE-627)OLC2111147249 (DE-He213)s10479-010-0692-3-p DE-627 ger DE-627 rakwb eng 004 VZ 3,2 ssgn Helber, Stefan verfasserin aut Using linear programming to analyze and optimize stochastic flow lines 2010 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2010 Abstract This paper presents a linear programming approach to analyze and optimize flow lines with limited buffer capacities and stochastic processing times. The basic idea is to solve a huge but simple linear program that models an entire simulation run of a multi-stage production process in discrete time, to determine a production rate estimate. As our methodology is purely numerical, it offers the full modeling flexibility of stochastic simulation with respect to the probability distribution of processing times. However, unlike discrete-event simulation models, it also offers the optimization power of linear programming and hence allows us to solve buffer allocation problems. We show under which conditions our method works well by comparing its results to exact values for two-machine models and approximate simulation results for longer lines. Buffer Size Inventory Level Buffer Space Buffer Allocation Longe Line Schimmelpfeng, Katja aut Stolletz, Raik aut Lagershausen, Svenja aut Enthalten in Annals of operations research Springer US, 1984 182(2010), 1 vom: 11. Feb., Seite 193-211 (DE-627)12964370X (DE-600)252629-3 (DE-576)018141862 0254-5330 volume:182 year:2010 number:1 day:11 month:02 pages:193-211 https://doi.org/10.1007/s10479-010-0692-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OLC-MAT GBV_ILN_26 GBV_ILN_4029 GBV_ILN_4317 AR 182 2010 1 11 02 193-211 |
allfieldsSound |
10.1007/s10479-010-0692-3 doi (DE-627)OLC2111147249 (DE-He213)s10479-010-0692-3-p DE-627 ger DE-627 rakwb eng 004 VZ 3,2 ssgn Helber, Stefan verfasserin aut Using linear programming to analyze and optimize stochastic flow lines 2010 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2010 Abstract This paper presents a linear programming approach to analyze and optimize flow lines with limited buffer capacities and stochastic processing times. The basic idea is to solve a huge but simple linear program that models an entire simulation run of a multi-stage production process in discrete time, to determine a production rate estimate. As our methodology is purely numerical, it offers the full modeling flexibility of stochastic simulation with respect to the probability distribution of processing times. However, unlike discrete-event simulation models, it also offers the optimization power of linear programming and hence allows us to solve buffer allocation problems. We show under which conditions our method works well by comparing its results to exact values for two-machine models and approximate simulation results for longer lines. Buffer Size Inventory Level Buffer Space Buffer Allocation Longe Line Schimmelpfeng, Katja aut Stolletz, Raik aut Lagershausen, Svenja aut Enthalten in Annals of operations research Springer US, 1984 182(2010), 1 vom: 11. Feb., Seite 193-211 (DE-627)12964370X (DE-600)252629-3 (DE-576)018141862 0254-5330 volume:182 year:2010 number:1 day:11 month:02 pages:193-211 https://doi.org/10.1007/s10479-010-0692-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OLC-MAT GBV_ILN_26 GBV_ILN_4029 GBV_ILN_4317 AR 182 2010 1 11 02 193-211 |
language |
English |
source |
Enthalten in Annals of operations research 182(2010), 1 vom: 11. Feb., Seite 193-211 volume:182 year:2010 number:1 day:11 month:02 pages:193-211 |
sourceStr |
Enthalten in Annals of operations research 182(2010), 1 vom: 11. Feb., Seite 193-211 volume:182 year:2010 number:1 day:11 month:02 pages:193-211 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
Buffer Size Inventory Level Buffer Space Buffer Allocation Longe Line |
dewey-raw |
004 |
isfreeaccess_bool |
false |
container_title |
Annals of operations research |
authorswithroles_txt_mv |
Helber, Stefan @@aut@@ Schimmelpfeng, Katja @@aut@@ Stolletz, Raik @@aut@@ Lagershausen, Svenja @@aut@@ |
publishDateDaySort_date |
2010-02-11T00:00:00Z |
hierarchy_top_id |
12964370X |
dewey-sort |
14 |
id |
OLC2111147249 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2111147249</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502202716.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">230502s2010 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10479-010-0692-3</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2111147249</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10479-010-0692-3-p</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">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">3,2</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Helber, Stefan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Using linear programming to analyze and optimize stochastic flow lines</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2010</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">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media, LLC 2010</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract This paper presents a linear programming approach to analyze and optimize flow lines with limited buffer capacities and stochastic processing times. The basic idea is to solve a huge but simple linear program that models an entire simulation run of a multi-stage production process in discrete time, to determine a production rate estimate. As our methodology is purely numerical, it offers the full modeling flexibility of stochastic simulation with respect to the probability distribution of processing times. However, unlike discrete-event simulation models, it also offers the optimization power of linear programming and hence allows us to solve buffer allocation problems. We show under which conditions our method works well by comparing its results to exact values for two-machine models and approximate simulation results for longer lines.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Buffer Size</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Inventory Level</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Buffer Space</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Buffer Allocation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Longe Line</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Schimmelpfeng, Katja</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Stolletz, Raik</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lagershausen, Svenja</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Annals of operations research</subfield><subfield code="d">Springer US, 1984</subfield><subfield code="g">182(2010), 1 vom: 11. Feb., Seite 193-211</subfield><subfield code="w">(DE-627)12964370X</subfield><subfield code="w">(DE-600)252629-3</subfield><subfield code="w">(DE-576)018141862</subfield><subfield code="x">0254-5330</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:182</subfield><subfield code="g">year:2010</subfield><subfield code="g">number:1</subfield><subfield code="g">day:11</subfield><subfield code="g">month:02</subfield><subfield code="g">pages:193-211</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10479-010-0692-3</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-WIW</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_26</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4029</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4317</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">182</subfield><subfield code="j">2010</subfield><subfield code="e">1</subfield><subfield code="b">11</subfield><subfield code="c">02</subfield><subfield code="h">193-211</subfield></datafield></record></collection>
|
author |
Helber, Stefan |
spellingShingle |
Helber, Stefan ddc 004 ssgn 3,2 misc Buffer Size misc Inventory Level misc Buffer Space misc Buffer Allocation misc Longe Line Using linear programming to analyze and optimize stochastic flow lines |
authorStr |
Helber, Stefan |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)12964370X |
format |
Article |
dewey-ones |
004 - Data processing & computer science |
delete_txt_mv |
keep |
author_role |
aut aut aut aut |
collection |
OLC |
remote_str |
false |
illustrated |
Not Illustrated |
issn |
0254-5330 |
topic_title |
004 VZ 3,2 ssgn Using linear programming to analyze and optimize stochastic flow lines Buffer Size Inventory Level Buffer Space Buffer Allocation Longe Line |
topic |
ddc 004 ssgn 3,2 misc Buffer Size misc Inventory Level misc Buffer Space misc Buffer Allocation misc Longe Line |
topic_unstemmed |
ddc 004 ssgn 3,2 misc Buffer Size misc Inventory Level misc Buffer Space misc Buffer Allocation misc Longe Line |
topic_browse |
ddc 004 ssgn 3,2 misc Buffer Size misc Inventory Level misc Buffer Space misc Buffer Allocation misc Longe Line |
format_facet |
Aufsätze Gedruckte Aufsätze |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
nc |
hierarchy_parent_title |
Annals of operations research |
hierarchy_parent_id |
12964370X |
dewey-tens |
000 - Computer science, knowledge & systems |
hierarchy_top_title |
Annals of operations research |
isfreeaccess_txt |
false |
familylinks_str_mv |
(DE-627)12964370X (DE-600)252629-3 (DE-576)018141862 |
title |
Using linear programming to analyze and optimize stochastic flow lines |
ctrlnum |
(DE-627)OLC2111147249 (DE-He213)s10479-010-0692-3-p |
title_full |
Using linear programming to analyze and optimize stochastic flow lines |
author_sort |
Helber, Stefan |
journal |
Annals of operations research |
journalStr |
Annals of operations research |
lang_code |
eng |
isOA_bool |
false |
dewey-hundreds |
000 - Computer science, information & general works |
recordtype |
marc |
publishDateSort |
2010 |
contenttype_str_mv |
txt |
container_start_page |
193 |
author_browse |
Helber, Stefan Schimmelpfeng, Katja Stolletz, Raik Lagershausen, Svenja |
container_volume |
182 |
class |
004 VZ 3,2 ssgn |
format_se |
Aufsätze |
author-letter |
Helber, Stefan |
doi_str_mv |
10.1007/s10479-010-0692-3 |
dewey-full |
004 |
title_sort |
using linear programming to analyze and optimize stochastic flow lines |
title_auth |
Using linear programming to analyze and optimize stochastic flow lines |
abstract |
Abstract This paper presents a linear programming approach to analyze and optimize flow lines with limited buffer capacities and stochastic processing times. The basic idea is to solve a huge but simple linear program that models an entire simulation run of a multi-stage production process in discrete time, to determine a production rate estimate. As our methodology is purely numerical, it offers the full modeling flexibility of stochastic simulation with respect to the probability distribution of processing times. However, unlike discrete-event simulation models, it also offers the optimization power of linear programming and hence allows us to solve buffer allocation problems. We show under which conditions our method works well by comparing its results to exact values for two-machine models and approximate simulation results for longer lines. © Springer Science+Business Media, LLC 2010 |
abstractGer |
Abstract This paper presents a linear programming approach to analyze and optimize flow lines with limited buffer capacities and stochastic processing times. The basic idea is to solve a huge but simple linear program that models an entire simulation run of a multi-stage production process in discrete time, to determine a production rate estimate. As our methodology is purely numerical, it offers the full modeling flexibility of stochastic simulation with respect to the probability distribution of processing times. However, unlike discrete-event simulation models, it also offers the optimization power of linear programming and hence allows us to solve buffer allocation problems. We show under which conditions our method works well by comparing its results to exact values for two-machine models and approximate simulation results for longer lines. © Springer Science+Business Media, LLC 2010 |
abstract_unstemmed |
Abstract This paper presents a linear programming approach to analyze and optimize flow lines with limited buffer capacities and stochastic processing times. The basic idea is to solve a huge but simple linear program that models an entire simulation run of a multi-stage production process in discrete time, to determine a production rate estimate. As our methodology is purely numerical, it offers the full modeling flexibility of stochastic simulation with respect to the probability distribution of processing times. However, unlike discrete-event simulation models, it also offers the optimization power of linear programming and hence allows us to solve buffer allocation problems. We show under which conditions our method works well by comparing its results to exact values for two-machine models and approximate simulation results for longer lines. © Springer Science+Business Media, LLC 2010 |
collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OLC-MAT GBV_ILN_26 GBV_ILN_4029 GBV_ILN_4317 |
container_issue |
1 |
title_short |
Using linear programming to analyze and optimize stochastic flow lines |
url |
https://doi.org/10.1007/s10479-010-0692-3 |
remote_bool |
false |
author2 |
Schimmelpfeng, Katja Stolletz, Raik Lagershausen, Svenja |
author2Str |
Schimmelpfeng, Katja Stolletz, Raik Lagershausen, Svenja |
ppnlink |
12964370X |
mediatype_str_mv |
n |
isOA_txt |
false |
hochschulschrift_bool |
false |
doi_str |
10.1007/s10479-010-0692-3 |
up_date |
2024-07-04T08:50:49.988Z |
_version_ |
1803637804227362816 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2111147249</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502202716.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">230502s2010 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s10479-010-0692-3</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2111147249</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s10479-010-0692-3-p</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">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">3,2</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Helber, Stefan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Using linear programming to analyze and optimize stochastic flow lines</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2010</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">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer Science+Business Media, LLC 2010</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract This paper presents a linear programming approach to analyze and optimize flow lines with limited buffer capacities and stochastic processing times. The basic idea is to solve a huge but simple linear program that models an entire simulation run of a multi-stage production process in discrete time, to determine a production rate estimate. As our methodology is purely numerical, it offers the full modeling flexibility of stochastic simulation with respect to the probability distribution of processing times. However, unlike discrete-event simulation models, it also offers the optimization power of linear programming and hence allows us to solve buffer allocation problems. We show under which conditions our method works well by comparing its results to exact values for two-machine models and approximate simulation results for longer lines.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Buffer Size</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Inventory Level</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Buffer Space</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Buffer Allocation</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Longe Line</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Schimmelpfeng, Katja</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Stolletz, Raik</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lagershausen, Svenja</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Annals of operations research</subfield><subfield code="d">Springer US, 1984</subfield><subfield code="g">182(2010), 1 vom: 11. Feb., Seite 193-211</subfield><subfield code="w">(DE-627)12964370X</subfield><subfield code="w">(DE-600)252629-3</subfield><subfield code="w">(DE-576)018141862</subfield><subfield code="x">0254-5330</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:182</subfield><subfield code="g">year:2010</subfield><subfield code="g">number:1</subfield><subfield code="g">day:11</subfield><subfield code="g">month:02</subfield><subfield code="g">pages:193-211</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s10479-010-0692-3</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-WIW</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_26</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4029</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4317</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">182</subfield><subfield code="j">2010</subfield><subfield code="e">1</subfield><subfield code="b">11</subfield><subfield code="c">02</subfield><subfield code="h">193-211</subfield></datafield></record></collection>
|
score |
7.3984165 |