Robust Solutions in Stochastic Linear Programming
作者:
SenguptaJati K.,
期刊:
Journal of the Operational Research Society
(Taylor Available online 1991)
卷期:
Volume 42,
issue 10
页码: 857-870
ISSN:0160-5682
年代: 1991
DOI:10.1057/jors.1991.166
出版商: Taylor&Francis
关键词: stochastic linear programming;minimax solutions;data envelopment analysis
数据来源: Taylor
摘要:
AbstractThe recent developments in stochastic linear programming are reviewed here broadly in their applied aspects. They include non-parametric methods which are applicable in situations of incomplete knowledge and partial uncertainty. This framework is shown to be most suitable for developing robust optimal solutions. For instance, a class of non-parametric methods based on the minimax principle and the criteria of stochastic dominance is developed here to illustrate its wide scope of application. It is shown that this class of methods provides a measure of robustness through the adoption of a cautious policy. Some examples are discussed using the recent field of data envelopment analysis.
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