Optimization under uncertainty: state-of-the-art and opportunities

A large number of problems in production planning and scheduling, location, transportation, finance, and engineering design require that decisions be made in the presence of uncertainty. Uncertainty, for instance, governs the prices of fuels, the availability of electricity, and the demand for chemicals. A key difficulty in optimization under uncertainty is in dealing with an uncertainty space that is huge and frequently leads to very large-scale optimization models. Decision-making under uncertainty is often further complicated by the presence of integer decision variables to model logical and other discrete decisions in a multi-period or multi-stage setting. This paper reviews theory and methodology that have been developed to cope with the complexity of optimization problems under uncertainty. We discuss and contrast the classical recourse-based stochastic programming, robust stochastic programming, probabilistic (chance-constraint) programming, fuzzy programming, and stochastic dy...

• Type: Journal
• Year: 2004
• Where: CCE
• Authors: Nikolaos V. Sahinidis
• Keywords: CCE 2004, CCE 2007, Large-scale Optimization Models, Recourse-based Stochastic Programming, Stochastic Programming