Two-stage stochastic matching and spanning tree problems: Polynomial instances and approximation
This article deals with the two-stage stochastic model, which aims at explicitly taking into account uncertainty in optimization problems, that Kong and Schaefer have recently studied for the maximum weight matching problem [N. Kong, A.J. Schaefer, A factor 1/2 approximation algorithm for two-stage...
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| Veröffentlicht in: | European journal of operational research 2010-08, Vol.205 (1), p.19-30 |
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| creator | Escoffier, Bruno Gourvès, Laurent Monnot, Jérôme Spanjaard, Olivier |
| description | This article deals with the two-stage stochastic model, which aims at explicitly taking into account uncertainty in optimization problems, that Kong and Schaefer have recently studied for the maximum weight matching problem [N. Kong, A.J. Schaefer, A factor 1/2 approximation algorithm for two-stage stochastic matching problems, European Journal of Operational Research 172(3) (2006) 740–746]. They have proved that the problem is
NP-hard, and they have provided a factor
1
2
approximation algorithm. We further study this problem and strengthen the hardness results, slightly improve the approximation ratio and exhibit some polynomial cases. We similarly tackle the maximum weight spanning tree problem in the two-stage setting. Finally, we make numerical experiments on randomly generated instances to compare the quality of several interesting heuristics. |
| doi_str_mv | 10.1016/j.ejor.2009.12.004 |
| format | Article |
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NP-hard, and they have provided a factor
1
2
approximation algorithm. We further study this problem and strengthen the hardness results, slightly improve the approximation ratio and exhibit some polynomial cases. We similarly tackle the maximum weight spanning tree problem in the two-stage setting. Finally, we make numerical experiments on randomly generated instances to compare the quality of several interesting heuristics.</description><identifier>ISSN: 0377-2217</identifier><identifier>EISSN: 1872-6860</identifier><identifier>DOI: 10.1016/j.ejor.2009.12.004</identifier><identifier>CODEN: EJORDT</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Applied sciences ; Approximation ; Approximation algorithms ; Combinatorial optimization ; Computer Science ; Exact sciences and technology ; Flows in networks. Combinatorial problems ; Graph theory ; Heuristic ; Matching ; Mathematical programming ; Maximum spanning tree ; Operational research and scientific management ; Operational research. Management science ; Optimization algorithms ; Stochastic models ; Stochastic programming ; Stochastic programming Approximation algorithms Matching Maximum spanning tree Combinatorial optimization ; Studies ; Uncertainty</subject><ispartof>European journal of operational research, 2010-08, Vol.205 (1), p.19-30</ispartof><rights>2009 Elsevier B.V.</rights><rights>2015 INIST-CNRS</rights><rights>Copyright Elsevier Sequoia S.A. Aug 16, 2010</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c532t-dd0c3295eff564a88d5ccef711f7341d4bb531868e4682448296d346643205b93</citedby><cites>FETCH-LOGICAL-c532t-dd0c3295eff564a88d5ccef711f7341d4bb531868e4682448296d346643205b93</cites><orcidid>0000-0002-7452-6553 ; 0000-0002-9948-090X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.ejor.2009.12.004$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>230,314,780,784,885,3550,4008,27924,27925,45995</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=22504668$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttp://econpapers.repec.org/article/eeeejores/v_3a205_3ay_3a2010_3ai_3a1_3ap_3a19-30.htm$$DView record in RePEc$$Hfree_for_read</backlink><backlink>$$Uhttps://hal.science/hal-01170295$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Escoffier, Bruno</creatorcontrib><creatorcontrib>Gourvès, Laurent</creatorcontrib><creatorcontrib>Monnot, Jérôme</creatorcontrib><creatorcontrib>Spanjaard, Olivier</creatorcontrib><title>Two-stage stochastic matching and spanning tree problems: Polynomial instances and approximation</title><title>European journal of operational research</title><description>This article deals with the two-stage stochastic model, which aims at explicitly taking into account uncertainty in optimization problems, that Kong and Schaefer have recently studied for the maximum weight matching problem [N. Kong, A.J. Schaefer, A factor 1/2 approximation algorithm for two-stage stochastic matching problems, European Journal of Operational Research 172(3) (2006) 740–746]. They have proved that the problem is
NP-hard, and they have provided a factor
1
2
approximation algorithm. We further study this problem and strengthen the hardness results, slightly improve the approximation ratio and exhibit some polynomial cases. We similarly tackle the maximum weight spanning tree problem in the two-stage setting. Finally, we make numerical experiments on randomly generated instances to compare the quality of several interesting heuristics.</description><subject>Applied sciences</subject><subject>Approximation</subject><subject>Approximation algorithms</subject><subject>Combinatorial optimization</subject><subject>Computer Science</subject><subject>Exact sciences and technology</subject><subject>Flows in networks. Combinatorial problems</subject><subject>Graph theory</subject><subject>Heuristic</subject><subject>Matching</subject><subject>Mathematical programming</subject><subject>Maximum spanning tree</subject><subject>Operational research and scientific management</subject><subject>Operational research. Management science</subject><subject>Optimization algorithms</subject><subject>Stochastic models</subject><subject>Stochastic programming</subject><subject>Stochastic programming Approximation algorithms Matching Maximum spanning tree Combinatorial optimization</subject><subject>Studies</subject><subject>Uncertainty</subject><issn>0377-2217</issn><issn>1872-6860</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>X2L</sourceid><recordid>eNp9UU1v1DAUtBBILIU_wClC4sAh4dmOEwdxqSr6Ia0Eh3I2Xuel6ygbBztd2H_PS1PtEUv2s62ZeeMxY-85FBx49bkvsA-xEABNwUUBUL5gG65rkVe6gpdsA7KucyF4_Zq9SakHAK642rBf939Cnmb7gFmag9vbNHuXHezs9n58yOzYZmmy47gc5oiYTTHsBjykL9mPMJzGcPB2yPxIEqPD9ESwE4H-ehLxYXzLXnV2SPjuuV6wn9ff7q9u8-33m7ury23ulBRz3rbgpGgUdp2qSqt1q5zDrua8q2XJ23K3U5LrSmNZaVGWWjRVK8uqKqUAtWvkBfu06u7tYKZI3ePJBOvN7eXWLHfAeQ3U4cgJ-2HFks_fj5hm04fHOJI9I4AEha4VgcQKcjGkFLE7q3IwS-imN0voZgndcGEodCLdraSIE7ozA2kQFJM5GmnJMK2npx1pSTIpLac5LbUxEsx-PpDWx2eXNjk7dJES9umsKYQCer8m3NcVhxTv0WM0yXmk32h9RDebNvj_Wf4HtuewCg</recordid><startdate>20100816</startdate><enddate>20100816</enddate><creator>Escoffier, Bruno</creator><creator>Gourvès, Laurent</creator><creator>Monnot, Jérôme</creator><creator>Spanjaard, Olivier</creator><general>Elsevier B.V</general><general>Elsevier</general><general>Elsevier Sequoia S.A</general><scope>IQODW</scope><scope>DKI</scope><scope>X2L</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>1XC</scope><scope>VOOES</scope><orcidid>https://orcid.org/0000-0002-7452-6553</orcidid><orcidid>https://orcid.org/0000-0002-9948-090X</orcidid></search><sort><creationdate>20100816</creationdate><title>Two-stage stochastic matching and spanning tree problems: Polynomial instances and approximation</title><author>Escoffier, Bruno ; Gourvès, Laurent ; Monnot, Jérôme ; Spanjaard, Olivier</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c532t-dd0c3295eff564a88d5ccef711f7341d4bb531868e4682448296d346643205b93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Applied sciences</topic><topic>Approximation</topic><topic>Approximation algorithms</topic><topic>Combinatorial optimization</topic><topic>Computer Science</topic><topic>Exact sciences and technology</topic><topic>Flows in networks. Combinatorial problems</topic><topic>Graph theory</topic><topic>Heuristic</topic><topic>Matching</topic><topic>Mathematical programming</topic><topic>Maximum spanning tree</topic><topic>Operational research and scientific management</topic><topic>Operational research. Management science</topic><topic>Optimization algorithms</topic><topic>Stochastic models</topic><topic>Stochastic programming</topic><topic>Stochastic programming Approximation algorithms Matching Maximum spanning tree Combinatorial optimization</topic><topic>Studies</topic><topic>Uncertainty</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Escoffier, Bruno</creatorcontrib><creatorcontrib>Gourvès, Laurent</creatorcontrib><creatorcontrib>Monnot, Jérôme</creatorcontrib><creatorcontrib>Spanjaard, Olivier</creatorcontrib><collection>Pascal-Francis</collection><collection>RePEc IDEAS</collection><collection>RePEc</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><jtitle>European journal of operational research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Escoffier, Bruno</au><au>Gourvès, Laurent</au><au>Monnot, Jérôme</au><au>Spanjaard, Olivier</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Two-stage stochastic matching and spanning tree problems: Polynomial instances and approximation</atitle><jtitle>European journal of operational research</jtitle><date>2010-08-16</date><risdate>2010</risdate><volume>205</volume><issue>1</issue><spage>19</spage><epage>30</epage><pages>19-30</pages><issn>0377-2217</issn><eissn>1872-6860</eissn><coden>EJORDT</coden><abstract>This article deals with the two-stage stochastic model, which aims at explicitly taking into account uncertainty in optimization problems, that Kong and Schaefer have recently studied for the maximum weight matching problem [N. Kong, A.J. Schaefer, A factor 1/2 approximation algorithm for two-stage stochastic matching problems, European Journal of Operational Research 172(3) (2006) 740–746]. They have proved that the problem is
NP-hard, and they have provided a factor
1
2
approximation algorithm. We further study this problem and strengthen the hardness results, slightly improve the approximation ratio and exhibit some polynomial cases. We similarly tackle the maximum weight spanning tree problem in the two-stage setting. Finally, we make numerical experiments on randomly generated instances to compare the quality of several interesting heuristics.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.ejor.2009.12.004</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-7452-6553</orcidid><orcidid>https://orcid.org/0000-0002-9948-090X</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Applied sciences Approximation Approximation algorithms Combinatorial optimization Computer Science Exact sciences and technology Flows in networks. Combinatorial problems Graph theory Heuristic Matching Mathematical programming Maximum spanning tree Operational research and scientific management Operational research. Management science Optimization algorithms Stochastic models Stochastic programming Stochastic programming Approximation algorithms Matching Maximum spanning tree Combinatorial optimization Studies Uncertainty |
| title | Two-stage stochastic matching and spanning tree problems: Polynomial instances and approximation |
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