Models and model value in stochastic programming
dc.contributor.author | Birge, John R. | en_US |
dc.date.accessioned | 2006-09-11T14:30:24Z | |
dc.date.available | 2006-09-11T14:30:24Z | |
dc.date.issued | 1995-12 | en_US |
dc.identifier.citation | Birge, John R.; (1995). "Models and model value in stochastic programming." Annals of Operations Research 59(1): 1-18. <http://hdl.handle.net/2027.42/44253> | en_US |
dc.identifier.issn | 0254-5330 | en_US |
dc.identifier.issn | 1572-9338 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/44253 | |
dc.description.abstract | Finding optimal decisions often involves the consideration of certain random or unknown parameters. A standard approach is to replace the random parameters by the expectations and to solve a deterministic mathematical program. A second approach is to consider possible future scenarios and the decision that would be best under each of these scenarios. The question then becomes how to choose among these alternatives. Both approaches may produce solutions that are far from optimal in the stochastic programming model that explicitly includes the random parameters. In this paper, we illustrate this advantage of a stochastic program model through two examples that are representative of the range of problems considered in stochastic programming. The paper focuses on the relative value of the stochastic program solution over a deterministic problem solution. | en_US |
dc.format.extent | 1264075 bytes | |
dc.format.extent | 3115 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.language.iso | en_US | |
dc.publisher | Baltzer Science Publishers, Baarn/Kluwer Academic Publishers; J.C. Baltzer AG, Science Publishers ; Springer Science+Business Media | en_US |
dc.subject.other | Economics / Management Science | en_US |
dc.subject.other | Theory of Computation | en_US |
dc.subject.other | Combinatorics | en_US |
dc.subject.other | Operations Research/Decision Theory | en_US |
dc.subject.other | Stochastic Optimization Models | en_US |
dc.subject.other | Finance | en_US |
dc.subject.other | Quality Control | en_US |
dc.subject.other | Engineering Design | en_US |
dc.title | Models and model value in stochastic programming | en_US |
dc.type | Article | en_US |
dc.subject.hlbsecondlevel | Management | en_US |
dc.subject.hlbsecondlevel | Industrial and Operations Engineering | en_US |
dc.subject.hlbsecondlevel | Economics | en_US |
dc.subject.hlbtoplevel | Business | en_US |
dc.subject.hlbtoplevel | Engineering | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Department of Industrial and Operations Engineering, The University of Michigan, 48109, Ann Arbor, MI, USA | en_US |
dc.contributor.affiliationumcampus | Ann Arbor | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/44253/1/10479_2005_Article_BF02031741.pdf | en_US |
dc.identifier.doi | http://dx.doi.org/10.1007/BF02031741 | en_US |
dc.identifier.source | Annals of Operations Research | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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