A linear decision-based approximation approach to stochastic programming

Xin Chen, Melvyn Sim, Peng Sun, Jiawei Zhang

Research output: Contribution to journalArticlepeer-review


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 addressing a general class of multistage stochastic optimization problems, which assume only limited information of the distributions of the underlying uncertainties, such as known mean, support, and covariance. One basic idea of our methods is to approximate the recourse decisions via decision rules. We first examine linear decision rules in detail and show that even for problems with complete recourse, linear decision rules can be inadequate and even lead, to infeasible instances. Hence, we propose several new decision rules that improve upon linear decision rules, while keeping the approximate models computationally tractable. Specifically, our approximate models are in the forms of the so-called second-order cone (SOC) programs, which could be solved efficiently both in theory and in practice. We also present computational evidence indicating that our approach is a viable alternative, and possibly advantageous, to existing stochastic optimization solution techniques in solving a two-stage stochastic optimization problem with complete recourse.

Original languageEnglish (US)
Pages (from-to)344-357
Number of pages14
JournalOperations Research
Issue number2
StatePublished - Mar 2008

ASJC Scopus subject areas

  • Computer Science Applications
  • Management Science and Operations Research


Dive into the research topics of 'A linear decision-based approximation approach to stochastic programming'. Together they form a unique fingerprint.

Cite this