Optimal Sampling Laws for Stochastically Constrained Simulation Optimization on Finite Sets
Consider the context of selecting an optimal system from among a finite set of competing systems, based on a "stochastic" objective function and subject to multiple "stochastic" constraints. In this context, we characterize the asymptotically optimal sample allocation that maximi...
Gespeichert in:
| Veröffentlicht in: | INFORMS journal on computing 2013-06, Vol.25 (3), p.527-542 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 542 |
|---|---|
| container_issue | 3 |
| container_start_page | 527 |
| container_title | INFORMS journal on computing |
| container_volume | 25 |
| creator | Hunter, Susan R Pasupathy, Raghu |
| description | Consider the context of selecting an optimal system from among a finite set of competing systems, based on a "stochastic" objective function and subject to multiple "stochastic" constraints. In this context, we characterize the asymptotically optimal sample allocation that maximizes the rate at which the probability of false selection tends to zero. Since the optimal allocation is the result of a concave maximization problem, its solution is particularly easy to obtain in contexts where the underlying distributions are known or can be assumed. We provide a consistent estimator for the optimal allocation and a corresponding sequential algorithm fit for implementation. Various numerical examples demonstrate how the proposed allocation differs from competing algorithms. |
| doi_str_mv | 10.1287/ijoc.1120.0519 |
| format | Article |
| fullrecord | <record><control><sourceid>gale_proqu</sourceid><recordid>TN_cdi_gale_infotracgeneralonefile_A339255148</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A339255148</galeid><sourcerecordid>A339255148</sourcerecordid><originalsourceid>FETCH-LOGICAL-c518t-f6b8b93257acbd07912802c1a281a311e90535e469aa2cccd73fa136bc48dcbb3</originalsourceid><addsrcrecordid>eNqFkUtr3DAUhU1poWmSbdaGbuuJHpYtLcPQtIWBLKZddSGuZdnRYEtTXZmS_PrKSSAtDBQJvfjOlY5OUVxRsqFMttfuEMyGUkY2RFD1pjijgjWVEEy-zWuiaKWkaN4XHxAPhJCa1-qs-Hl3TG6GqdzDfJycH8sd_MZyCLHcp2DuAZMzME0P5TZ4TBGct325d_MyQXLBl0969_i8yf3WeZdsubcJL4p3A0xoL1_m8-LH7efv26_V7u7Lt-3NrjKCylQNTSc7xZlowXQ9aVV2Q5ihwCQFTqlVRHBh60YBMGNM3_IBKG86U8vedB0_Lz4-1z3G8GuxmPQhLNHnKzWtmZCtkrx9pUaYrHZ-CNmNmR0afcO5YkLQWmaqOkGN1tsIU_B2cPn4H35zgs-tt7MzJwWf_hJ0C-YPxTygG-8TjrAgnqxvYkCMdtDHmPOKD5oSvcau19j1GrteY381sL4lzvg__g_lxq40</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1425879837</pqid></control><display><type>article</type><title>Optimal Sampling Laws for Stochastically Constrained Simulation Optimization on Finite Sets</title><source>INFORMS PubsOnLine</source><source>Business Source Complete</source><creator>Hunter, Susan R ; Pasupathy, Raghu</creator><creatorcontrib>Hunter, Susan R ; Pasupathy, Raghu</creatorcontrib><description>Consider the context of selecting an optimal system from among a finite set of competing systems, based on a "stochastic" objective function and subject to multiple "stochastic" constraints. In this context, we characterize the asymptotically optimal sample allocation that maximizes the rate at which the probability of false selection tends to zero. Since the optimal allocation is the result of a concave maximization problem, its solution is particularly easy to obtain in contexts where the underlying distributions are known or can be assumed. We provide a consistent estimator for the optimal allocation and a corresponding sequential algorithm fit for implementation. Various numerical examples demonstrate how the proposed allocation differs from competing algorithms.</description><identifier>ISSN: 1091-9856</identifier><identifier>EISSN: 1526-5528</identifier><identifier>EISSN: 1091-9856</identifier><identifier>DOI: 10.1287/ijoc.1120.0519</identifier><language>eng</language><publisher>Linthicum: INFORMS</publisher><subject>Algorithms ; constrained simulation optimization ; Finite element analysis ; Mathematical optimization ; Methods ; optimal allocation ; ranking and selection ; Simulation ; Simulation methods ; Stochastic models ; Studies</subject><ispartof>INFORMS journal on computing, 2013-06, Vol.25 (3), p.527-542</ispartof><rights>COPYRIGHT 2013 Institute for Operations Research and the Management Sciences</rights><rights>Copyright Institute for Operations Research and the Management Sciences Summer 2013</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c518t-f6b8b93257acbd07912802c1a281a311e90535e469aa2cccd73fa136bc48dcbb3</citedby><cites>FETCH-LOGICAL-c518t-f6b8b93257acbd07912802c1a281a311e90535e469aa2cccd73fa136bc48dcbb3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://pubsonline.informs.org/doi/full/10.1287/ijoc.1120.0519$$EHTML$$P50$$Ginforms$$H</linktohtml><link.rule.ids>314,780,784,3690,27923,27924,62615</link.rule.ids></links><search><creatorcontrib>Hunter, Susan R</creatorcontrib><creatorcontrib>Pasupathy, Raghu</creatorcontrib><title>Optimal Sampling Laws for Stochastically Constrained Simulation Optimization on Finite Sets</title><title>INFORMS journal on computing</title><description>Consider the context of selecting an optimal system from among a finite set of competing systems, based on a "stochastic" objective function and subject to multiple "stochastic" constraints. In this context, we characterize the asymptotically optimal sample allocation that maximizes the rate at which the probability of false selection tends to zero. Since the optimal allocation is the result of a concave maximization problem, its solution is particularly easy to obtain in contexts where the underlying distributions are known or can be assumed. We provide a consistent estimator for the optimal allocation and a corresponding sequential algorithm fit for implementation. Various numerical examples demonstrate how the proposed allocation differs from competing algorithms.</description><subject>Algorithms</subject><subject>constrained simulation optimization</subject><subject>Finite element analysis</subject><subject>Mathematical optimization</subject><subject>Methods</subject><subject>optimal allocation</subject><subject>ranking and selection</subject><subject>Simulation</subject><subject>Simulation methods</subject><subject>Stochastic models</subject><subject>Studies</subject><issn>1091-9856</issn><issn>1526-5528</issn><issn>1091-9856</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>N95</sourceid><recordid>eNqFkUtr3DAUhU1poWmSbdaGbuuJHpYtLcPQtIWBLKZddSGuZdnRYEtTXZmS_PrKSSAtDBQJvfjOlY5OUVxRsqFMttfuEMyGUkY2RFD1pjijgjWVEEy-zWuiaKWkaN4XHxAPhJCa1-qs-Hl3TG6GqdzDfJycH8sd_MZyCLHcp2DuAZMzME0P5TZ4TBGct325d_MyQXLBl0969_i8yf3WeZdsubcJL4p3A0xoL1_m8-LH7efv26_V7u7Lt-3NrjKCylQNTSc7xZlowXQ9aVV2Q5ihwCQFTqlVRHBh60YBMGNM3_IBKG86U8vedB0_Lz4-1z3G8GuxmPQhLNHnKzWtmZCtkrx9pUaYrHZ-CNmNmR0afcO5YkLQWmaqOkGN1tsIU_B2cPn4H35zgs-tt7MzJwWf_hJ0C-YPxTygG-8TjrAgnqxvYkCMdtDHmPOKD5oSvcau19j1GrteY381sL4lzvg__g_lxq40</recordid><startdate>20130622</startdate><enddate>20130622</enddate><creator>Hunter, Susan R</creator><creator>Pasupathy, Raghu</creator><general>INFORMS</general><general>Institute for Operations Research and the Management Sciences</general><scope>AAYXX</scope><scope>CITATION</scope><scope>N95</scope><scope>XI7</scope><scope>JQ2</scope></search><sort><creationdate>20130622</creationdate><title>Optimal Sampling Laws for Stochastically Constrained Simulation Optimization on Finite Sets</title><author>Hunter, Susan R ; Pasupathy, Raghu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c518t-f6b8b93257acbd07912802c1a281a311e90535e469aa2cccd73fa136bc48dcbb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Algorithms</topic><topic>constrained simulation optimization</topic><topic>Finite element analysis</topic><topic>Mathematical optimization</topic><topic>Methods</topic><topic>optimal allocation</topic><topic>ranking and selection</topic><topic>Simulation</topic><topic>Simulation methods</topic><topic>Stochastic models</topic><topic>Studies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hunter, Susan R</creatorcontrib><creatorcontrib>Pasupathy, Raghu</creatorcontrib><collection>CrossRef</collection><collection>Gale Business: Insights</collection><collection>Business Insights: Essentials</collection><collection>ProQuest Computer Science Collection</collection><jtitle>INFORMS journal on computing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hunter, Susan R</au><au>Pasupathy, Raghu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Optimal Sampling Laws for Stochastically Constrained Simulation Optimization on Finite Sets</atitle><jtitle>INFORMS journal on computing</jtitle><date>2013-06-22</date><risdate>2013</risdate><volume>25</volume><issue>3</issue><spage>527</spage><epage>542</epage><pages>527-542</pages><issn>1091-9856</issn><eissn>1526-5528</eissn><eissn>1091-9856</eissn><abstract>Consider the context of selecting an optimal system from among a finite set of competing systems, based on a "stochastic" objective function and subject to multiple "stochastic" constraints. In this context, we characterize the asymptotically optimal sample allocation that maximizes the rate at which the probability of false selection tends to zero. Since the optimal allocation is the result of a concave maximization problem, its solution is particularly easy to obtain in contexts where the underlying distributions are known or can be assumed. We provide a consistent estimator for the optimal allocation and a corresponding sequential algorithm fit for implementation. Various numerical examples demonstrate how the proposed allocation differs from competing algorithms.</abstract><cop>Linthicum</cop><pub>INFORMS</pub><doi>10.1287/ijoc.1120.0519</doi><tpages>16</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1091-9856 |
| ispartof | INFORMS journal on computing, 2013-06, Vol.25 (3), p.527-542 |
| issn | 1091-9856 1526-5528 1091-9856 |
| language | eng |
| recordid | cdi_gale_infotracgeneralonefile_A339255148 |
| source | INFORMS PubsOnLine; Business Source Complete |
| subjects | Algorithms constrained simulation optimization Finite element analysis Mathematical optimization Methods optimal allocation ranking and selection Simulation Simulation methods Stochastic models Studies |
| title | Optimal Sampling Laws for Stochastically Constrained Simulation Optimization on Finite Sets |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-13T00%3A40%3A18IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_proqu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Optimal%20Sampling%20Laws%20for%20Stochastically%20Constrained%20Simulation%20Optimization%20on%20Finite%20Sets&rft.jtitle=INFORMS%20journal%20on%20computing&rft.au=Hunter,%20Susan%20R&rft.date=2013-06-22&rft.volume=25&rft.issue=3&rft.spage=527&rft.epage=542&rft.pages=527-542&rft.issn=1091-9856&rft.eissn=1526-5528&rft_id=info:doi/10.1287/ijoc.1120.0519&rft_dat=%3Cgale_proqu%3EA339255148%3C/gale_proqu%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1425879837&rft_id=info:pmid/&rft_galeid=A339255148&rfr_iscdi=true |