Rollout Algorithms for Stochastic Scheduling Problems
Stochastic scheduling problems are difficult stochastic control problems with combinatorial decision spaces. In this paper we focus on a class of stochastic scheduling problems, the quiz problem and its variations. We discuss the use of heuristics for their solution, and we propose rollout algorithm...
Gespeichert in:
| Veröffentlicht in: | Journal of heuristics 1999-04, Vol.5 (1), p.89 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | |
|---|---|
| container_issue | 1 |
| container_start_page | 89 |
| container_title | Journal of heuristics |
| container_volume | 5 |
| creator | Bertsekas, Dimitri P Castanon, David A |
| description | Stochastic scheduling problems are difficult stochastic control problems with combinatorial decision spaces. In this paper we focus on a class of stochastic scheduling problems, the quiz problem and its variations. We discuss the use of heuristics for their solution, and we propose rollout algorithms based on these heuristics which approximate the stochastic dynamic programming algorithm. We show how the rollout algorithms can be implemented efficiently, with considerable savings in computation over optimal algorithms. We delineate circumstances under which the rollout algorithms are guaranteed to perform better than the heuristics on which they are based. We also show computational results which suggest that the performance of the rollout policies is near-optimal, and is substantially better than the performance of their underlying heuristics. [PUBLICATION ABSTRACT] |
| doi_str_mv | 10.1023/A:1009634810396 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_199262121</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>395708441</sourcerecordid><originalsourceid>FETCH-LOGICAL-c226t-411ab886bb2e08d34a6bc1938f1adbe78e215949d9c29be070b85c914ccb7a8c3</originalsourceid><addsrcrecordid>eNotjUFLwzAYhoMoOKdnr8V7Nd-XNsnnrQydwkBxeh5Jmq4d2aJN-v8t6Ol9Dg_Py9gt8HvgKB6aR-CcpKg0cEHyjC2gVliSIHU-s9BQAgq4ZFcpHfis6losWP0RQ4hTLpqwj-OQ-2MqujgW2xxdb1IeXLF1vW-nMJz2xfsYbfDHdM0uOhOSv_nfJft6fvpcvZSbt_XrqtmUDlHmsgIwVmtpLXquW1EZaR2Q0B2Y1nqlPUJNFbXkkKzniltdO4LKOauMdmLJ7v6632P8mXzKu0OcxtN8uQMilAgI4hf5kkdL</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>199262121</pqid></control><display><type>article</type><title>Rollout Algorithms for Stochastic Scheduling Problems</title><source>SpringerLink Journals - AutoHoldings</source><creator>Bertsekas, Dimitri P ; Castanon, David A</creator><creatorcontrib>Bertsekas, Dimitri P ; Castanon, David A</creatorcontrib><description>Stochastic scheduling problems are difficult stochastic control problems with combinatorial decision spaces. In this paper we focus on a class of stochastic scheduling problems, the quiz problem and its variations. We discuss the use of heuristics for their solution, and we propose rollout algorithms based on these heuristics which approximate the stochastic dynamic programming algorithm. We show how the rollout algorithms can be implemented efficiently, with considerable savings in computation over optimal algorithms. We delineate circumstances under which the rollout algorithms are guaranteed to perform better than the heuristics on which they are based. We also show computational results which suggest that the performance of the rollout policies is near-optimal, and is substantially better than the performance of their underlying heuristics. [PUBLICATION ABSTRACT]</description><identifier>ISSN: 1381-1231</identifier><identifier>EISSN: 1572-9397</identifier><identifier>DOI: 10.1023/A:1009634810396</identifier><language>eng</language><publisher>Boston: Springer Nature B.V</publisher><subject>Algorithms ; Computer engineering ; Computer programming ; Dynamic programming ; Electrical engineering ; Heuristic ; Scandals ; Scheduling ; Studies ; Traveling salesman problem</subject><ispartof>Journal of heuristics, 1999-04, Vol.5 (1), p.89</ispartof><rights>Copyright (c) 1999 Kluwer Academic Publishers</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c226t-411ab886bb2e08d34a6bc1938f1adbe78e215949d9c29be070b85c914ccb7a8c3</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27923,27924</link.rule.ids></links><search><creatorcontrib>Bertsekas, Dimitri P</creatorcontrib><creatorcontrib>Castanon, David A</creatorcontrib><title>Rollout Algorithms for Stochastic Scheduling Problems</title><title>Journal of heuristics</title><description>Stochastic scheduling problems are difficult stochastic control problems with combinatorial decision spaces. In this paper we focus on a class of stochastic scheduling problems, the quiz problem and its variations. We discuss the use of heuristics for their solution, and we propose rollout algorithms based on these heuristics which approximate the stochastic dynamic programming algorithm. We show how the rollout algorithms can be implemented efficiently, with considerable savings in computation over optimal algorithms. We delineate circumstances under which the rollout algorithms are guaranteed to perform better than the heuristics on which they are based. We also show computational results which suggest that the performance of the rollout policies is near-optimal, and is substantially better than the performance of their underlying heuristics. [PUBLICATION ABSTRACT]</description><subject>Algorithms</subject><subject>Computer engineering</subject><subject>Computer programming</subject><subject>Dynamic programming</subject><subject>Electrical engineering</subject><subject>Heuristic</subject><subject>Scandals</subject><subject>Scheduling</subject><subject>Studies</subject><subject>Traveling salesman problem</subject><issn>1381-1231</issn><issn>1572-9397</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1999</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNotjUFLwzAYhoMoOKdnr8V7Nd-XNsnnrQydwkBxeh5Jmq4d2aJN-v8t6Ol9Dg_Py9gt8HvgKB6aR-CcpKg0cEHyjC2gVliSIHU-s9BQAgq4ZFcpHfis6losWP0RQ4hTLpqwj-OQ-2MqujgW2xxdb1IeXLF1vW-nMJz2xfsYbfDHdM0uOhOSv_nfJft6fvpcvZSbt_XrqtmUDlHmsgIwVmtpLXquW1EZaR2Q0B2Y1nqlPUJNFbXkkKzniltdO4LKOauMdmLJ7v6632P8mXzKu0OcxtN8uQMilAgI4hf5kkdL</recordid><startdate>19990401</startdate><enddate>19990401</enddate><creator>Bertsekas, Dimitri P</creator><creator>Castanon, David A</creator><general>Springer Nature B.V</general><scope>3V.</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>8AL</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8FL</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FRNLG</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K60</scope><scope>K6~</scope><scope>K7-</scope><scope>L.-</scope><scope>M0C</scope><scope>M0N</scope><scope>P5Z</scope><scope>P62</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYYUZ</scope><scope>Q9U</scope></search><sort><creationdate>19990401</creationdate><title>Rollout Algorithms for Stochastic Scheduling Problems</title><author>Bertsekas, Dimitri P ; Castanon, David A</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c226t-411ab886bb2e08d34a6bc1938f1adbe78e215949d9c29be070b85c914ccb7a8c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1999</creationdate><topic>Algorithms</topic><topic>Computer engineering</topic><topic>Computer programming</topic><topic>Dynamic programming</topic><topic>Electrical engineering</topic><topic>Heuristic</topic><topic>Scandals</topic><topic>Scheduling</topic><topic>Studies</topic><topic>Traveling salesman problem</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bertsekas, Dimitri P</creatorcontrib><creatorcontrib>Castanon, David A</creatorcontrib><collection>ProQuest Central (Corporate)</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>Computer Science Database</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Global</collection><collection>Computing Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ABI/INFORM Collection China</collection><collection>ProQuest Central Basic</collection><jtitle>Journal of heuristics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bertsekas, Dimitri P</au><au>Castanon, David A</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Rollout Algorithms for Stochastic Scheduling Problems</atitle><jtitle>Journal of heuristics</jtitle><date>1999-04-01</date><risdate>1999</risdate><volume>5</volume><issue>1</issue><spage>89</spage><pages>89-</pages><issn>1381-1231</issn><eissn>1572-9397</eissn><abstract>Stochastic scheduling problems are difficult stochastic control problems with combinatorial decision spaces. In this paper we focus on a class of stochastic scheduling problems, the quiz problem and its variations. We discuss the use of heuristics for their solution, and we propose rollout algorithms based on these heuristics which approximate the stochastic dynamic programming algorithm. We show how the rollout algorithms can be implemented efficiently, with considerable savings in computation over optimal algorithms. We delineate circumstances under which the rollout algorithms are guaranteed to perform better than the heuristics on which they are based. We also show computational results which suggest that the performance of the rollout policies is near-optimal, and is substantially better than the performance of their underlying heuristics. [PUBLICATION ABSTRACT]</abstract><cop>Boston</cop><pub>Springer Nature B.V</pub><doi>10.1023/A:1009634810396</doi></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1381-1231 |
| ispartof | Journal of heuristics, 1999-04, Vol.5 (1), p.89 |
| issn | 1381-1231 1572-9397 |
| language | eng |
| recordid | cdi_proquest_journals_199262121 |
| source | SpringerLink Journals - AutoHoldings |
| subjects | Algorithms Computer engineering Computer programming Dynamic programming Electrical engineering Heuristic Scandals Scheduling Studies Traveling salesman problem |
| title | Rollout Algorithms for Stochastic Scheduling Problems |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-12T14%3A48%3A54IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Rollout%20Algorithms%20for%20Stochastic%20Scheduling%20Problems&rft.jtitle=Journal%20of%20heuristics&rft.au=Bertsekas,%20Dimitri%20P&rft.date=1999-04-01&rft.volume=5&rft.issue=1&rft.spage=89&rft.pages=89-&rft.issn=1381-1231&rft.eissn=1572-9397&rft_id=info:doi/10.1023/A:1009634810396&rft_dat=%3Cproquest%3E395708441%3C/proquest%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=199262121&rft_id=info:pmid/&rfr_iscdi=true |