Chance constrained programming approaches to congestion in stochastic data envelopment analysis
The models described in this paper for treating congestion in DEA are extended by according them chance constrained programming formulations. The usual route used in chance constrained programming is followed here by replacing these stochastic models with their “deterministic equivalents.” This lead...
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| Veröffentlicht in: | European journal of operational research 2004-06, Vol.155 (2), p.487-501 |
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| creator | Cooper, William W. Deng, H. Huang, Zhimin Li, Susan X. |
| description | The models described in this paper for treating congestion in DEA are extended by according them chance constrained programming formulations. The usual route used in chance constrained programming is followed here by replacing these stochastic models with their “deterministic equivalents.” This leads to a class of non-linear problems. However, it is shown to be possible to avoid some of the need for dealing with these non-linear problems by identifying conditions under which they can be replaced by ordinary (deterministic) DEA models. Examples which illustrate possible uses of these approaches are also supplied in an
Appendix A. |
| doi_str_mv | 10.1016/S0377-2217(02)00901-3 |
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Appendix A.</description><identifier>ISSN: 0377-2217</identifier><identifier>EISSN: 1872-6860</identifier><identifier>DOI: 10.1016/S0377-2217(02)00901-3</identifier><identifier>CODEN: EJORDT</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Chance constrained programming ; Congestion ; Data envelopment analysis ; DEA (data envelopment analysis) ; Inefficiency ; Nonlinear programming ; Studies</subject><ispartof>European journal of operational research, 2004-06, Vol.155 (2), p.487-501</ispartof><rights>2003 Elsevier B.V.</rights><rights>Copyright Elsevier Sequoia S.A. Jun 1, 2004</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c437t-73b8b6a0136f619ca8ff91d6249db2119ba3af3a7d43fb3e7ac2eae71a33442f3</citedby><cites>FETCH-LOGICAL-c437t-73b8b6a0136f619ca8ff91d6249db2119ba3af3a7d43fb3e7ac2eae71a33442f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/S0377-2217(02)00901-3$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>315,781,785,3551,4009,27929,27930,46000</link.rule.ids><backlink>$$Uhttp://econpapers.repec.org/article/eeeejores/v_3a155_3ay_3a2004_3ai_3a2_3ap_3a487-501.htm$$DView record in RePEc$$Hfree_for_read</backlink></links><search><creatorcontrib>Cooper, William W.</creatorcontrib><creatorcontrib>Deng, H.</creatorcontrib><creatorcontrib>Huang, Zhimin</creatorcontrib><creatorcontrib>Li, Susan X.</creatorcontrib><title>Chance constrained programming approaches to congestion in stochastic data envelopment analysis</title><title>European journal of operational research</title><description>The models described in this paper for treating congestion in DEA are extended by according them chance constrained programming formulations. The usual route used in chance constrained programming is followed here by replacing these stochastic models with their “deterministic equivalents.” This leads to a class of non-linear problems. However, it is shown to be possible to avoid some of the need for dealing with these non-linear problems by identifying conditions under which they can be replaced by ordinary (deterministic) DEA models. Examples which illustrate possible uses of these approaches are also supplied in an
Appendix A.</description><subject>Chance constrained programming</subject><subject>Congestion</subject><subject>Data envelopment analysis</subject><subject>DEA (data envelopment analysis)</subject><subject>Inefficiency</subject><subject>Nonlinear programming</subject><subject>Studies</subject><issn>0377-2217</issn><issn>1872-6860</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2004</creationdate><recordtype>article</recordtype><sourceid>X2L</sourceid><recordid>eNqFUU2LFDEUDKLguPoThOBJD60vH93pPokM6wcueFDP4XX69UyG6aRNsgPz783syF4NVF4eVBVFhbHXAt4LEN2Hn6CMaaQU5i3IdwADiEY9YRvRG9l0fQdP2eaR8py9yPkAAKIV7YbZ7R6DI-5iyCWhDzTxNcVdwmXxYcdxrRu6PWVe4oW1o1x8DNwHnkt0e6yr4xMW5BROdIzrQqFwDHg8Z59fsmczHjO9-jdv2O_Pt7-2X5u7H1--bT_dNU4rUxqjxn7sEITq5k4MDvt5HsTUST1MoxRiGFHhrNBMWs2jIoNOEpIRqJTWclY37M3Vt8b9c18z2kO8TzVEthK00KqXppLaK8mlmHOi2a7JL5jOVoC9VGkfqrSXnixI-1ClVVX3_apLtJJ7FFE9h5go25NVKNq23ucKCaDr8JdnxVqhe2Pb6rUvS3X7eHWj2sfJU7LZeaqfMPlErtgp-v_k-QvWupZn</recordid><startdate>20040601</startdate><enddate>20040601</enddate><creator>Cooper, William W.</creator><creator>Deng, H.</creator><creator>Huang, Zhimin</creator><creator>Li, Susan X.</creator><general>Elsevier B.V</general><general>Elsevier</general><general>Elsevier Sequoia S.A</general><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></search><sort><creationdate>20040601</creationdate><title>Chance constrained programming approaches to congestion in stochastic data envelopment analysis</title><author>Cooper, William W. ; Deng, H. ; Huang, Zhimin ; Li, Susan X.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c437t-73b8b6a0136f619ca8ff91d6249db2119ba3af3a7d43fb3e7ac2eae71a33442f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2004</creationdate><topic>Chance constrained programming</topic><topic>Congestion</topic><topic>Data envelopment analysis</topic><topic>DEA (data envelopment analysis)</topic><topic>Inefficiency</topic><topic>Nonlinear programming</topic><topic>Studies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cooper, William W.</creatorcontrib><creatorcontrib>Deng, H.</creatorcontrib><creatorcontrib>Huang, Zhimin</creatorcontrib><creatorcontrib>Li, Susan X.</creatorcontrib><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><jtitle>European journal of operational research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cooper, William W.</au><au>Deng, H.</au><au>Huang, Zhimin</au><au>Li, Susan X.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Chance constrained programming approaches to congestion in stochastic data envelopment analysis</atitle><jtitle>European journal of operational research</jtitle><date>2004-06-01</date><risdate>2004</risdate><volume>155</volume><issue>2</issue><spage>487</spage><epage>501</epage><pages>487-501</pages><issn>0377-2217</issn><eissn>1872-6860</eissn><coden>EJORDT</coden><abstract>The models described in this paper for treating congestion in DEA are extended by according them chance constrained programming formulations. The usual route used in chance constrained programming is followed here by replacing these stochastic models with their “deterministic equivalents.” This leads to a class of non-linear problems. However, it is shown to be possible to avoid some of the need for dealing with these non-linear problems by identifying conditions under which they can be replaced by ordinary (deterministic) DEA models. Examples which illustrate possible uses of these approaches are also supplied in an
Appendix A.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/S0377-2217(02)00901-3</doi><tpages>15</tpages></addata></record> |
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| source | RePEc; Access via ScienceDirect (Elsevier) |
| subjects | Chance constrained programming Congestion Data envelopment analysis DEA (data envelopment analysis) Inefficiency Nonlinear programming Studies |
| title | Chance constrained programming approaches to congestion in stochastic data envelopment analysis |
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