A Linear Decision-Based Approximation Approach to Stochastic Programming

Stochastic optimization, especially multistage models, is well known to be computationally excruciating. Moreover, such models require exact specifications of the probability distributions of the underlying uncertainties, which are often unavailable. In this paper, we propose tractable methods of ad...

Full description

Saved in:

Bibliographic Details
Published in:	Operations research Vol. 56; no. 2; pp. 344 - 357
Main Authors:	Chen, Xin, Sim, Melvyn, Sun, Peng, Zhang, Jiawei
Format:	Journal Article
Language:	English
Published:	Linthicum, MD INFORMS 01.03.2008 Institute for Operations Research and the Management Sciences
Subjects:	Analysis Applied sciences Approximation Covariance Decision making Decision theory. Utility theory Exact sciences and technology Linear programming Mathematical vectors Mathematics Matrices Objective functions Operational research and scientific management Operational research. Management science Optimization programming Random variables Robust optimization Sampling methods stochastic Stochastic models Stochastic programming Studies United States Stochastic model conic programming Probabilistic approach programming: stochastic Stochastic approximation Modeling Stochastic programming Decision rule Uncertain system Covariance Limit distribution Linear approximation Model specification
ISSN:	0030-364X, 1526-5463
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!