An approximate method for solving stochastic two-stage programming problems

Maili Wang, Kevin Lansey, Diana Yakowitz

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

A procedure for solving stochastic two-stage programming problems has been developed. The approach consists of genetic algorithm optimization with point estimate procedures. It has several advantages over traditional methods, such as evaluating function values only, no continuous or gradient requirements and it can solve integer or continuous problems. To improve the performance of the method, a modification of a standard genetic algorithm is suggested and coded. Point estimation methods are used to efficiently evaluate the second stage expected value objective function. Finally, the overall procedure is applied to several linear and nonlinear problems.

Original languageEnglish (US)
Pages (from-to)279-302
Number of pages24
JournalEngineering Optimization
Volume33
Issue number3
DOIs
StatePublished - Feb 2001

Keywords

  • Genetic algorithms
  • Point estimation methods
  • Stochastic optimization

ASJC Scopus subject areas

  • Computer Science Applications
  • Control and Optimization
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering
  • Applied Mathematics

Fingerprint Dive into the research topics of 'An approximate method for solving stochastic two-stage programming problems'. Together they form a unique fingerprint.

  • Cite this