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Sensitivity analysis and uncertainty in linear programming

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Sensitivity analysis and uncertainty in linear programming. / Higle, Julia; Wallace, Stein W.
In: Interfaces, Vol. 33, No. 4, 07.2003, p. 53-60.

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Higle J, Wallace SW. Sensitivity analysis and uncertainty in linear programming. Interfaces. 2003 Jul;33(4):53-60. doi: 10.1287/inte.33.4.53.16370

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Higle, Julia ; Wallace, Stein W. / Sensitivity analysis and uncertainty in linear programming. In: Interfaces. 2003 ; Vol. 33, No. 4. pp. 53-60.

Bibtex

@article{6a3bf44707bf4f46b34a0dfe06bc5e79,
title = "Sensitivity analysis and uncertainty in linear programming",
abstract = "Linear programming (LP) is one of the great successes to emerge from operations research and management science. It is well developed and widely used. LP problems in practice are often based on numerical data that represent rough approximations of quantities that are inherently difficult to estimate. Because of this, most LP-based studies include a postoptimality investigation of how a change in the data changes the solution. Researchers routinely undertake this type of sensitivity analysis (SA), and most commercial packages for solving linear programs include the results of such an analysis as part of the standard output report. SA has shortcomings that run contrary to conventional wisdom. Alternate models address these shortcomings.",
keywords = "Philosophy of modeling. Programming: stochastic., Programming: stochastic.",
author = "Julia Higle and Wallace, {Stein W}",
year = "2003",
month = jul,
doi = "10.1287/inte.33.4.53.16370",
language = "English",
volume = "33",
pages = "53--60",
journal = "Interfaces",
issn = "0092-2102",
publisher = "INFORMS Inst.for Operations Res.and the Management Sciences",
number = "4",

}

RIS

TY - JOUR

T1 - Sensitivity analysis and uncertainty in linear programming

AU - Higle, Julia

AU - Wallace, Stein W

PY - 2003/7

Y1 - 2003/7

N2 - Linear programming (LP) is one of the great successes to emerge from operations research and management science. It is well developed and widely used. LP problems in practice are often based on numerical data that represent rough approximations of quantities that are inherently difficult to estimate. Because of this, most LP-based studies include a postoptimality investigation of how a change in the data changes the solution. Researchers routinely undertake this type of sensitivity analysis (SA), and most commercial packages for solving linear programs include the results of such an analysis as part of the standard output report. SA has shortcomings that run contrary to conventional wisdom. Alternate models address these shortcomings.

AB - Linear programming (LP) is one of the great successes to emerge from operations research and management science. It is well developed and widely used. LP problems in practice are often based on numerical data that represent rough approximations of quantities that are inherently difficult to estimate. Because of this, most LP-based studies include a postoptimality investigation of how a change in the data changes the solution. Researchers routinely undertake this type of sensitivity analysis (SA), and most commercial packages for solving linear programs include the results of such an analysis as part of the standard output report. SA has shortcomings that run contrary to conventional wisdom. Alternate models address these shortcomings.

KW - Philosophy of modeling. Programming: stochastic.

KW - Programming: stochastic.

U2 - 10.1287/inte.33.4.53.16370

DO - 10.1287/inte.33.4.53.16370

M3 - Journal article

VL - 33

SP - 53

EP - 60

JO - Interfaces

JF - Interfaces

SN - 0092-2102

IS - 4

ER -