Mathematical Programming Techniques for Optimization under Uncertainty and Their Application in Process Systems Engineering

I. E. Grossmann, R. M. Apap, B. A. Calfa, P. Garcia-Herreros, Q. Zhang

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

In this paper we give an overview of some of the advances that have taken place to address challenges in the area of optimization under uncertainty. We first describe the incorporation of recourse in robust optimization to reduce the conservative results obtained with this approach, and illustrate it with interruptible load in demand side management. Second, we describe computational strategies for effectively solving two stage programming problems, which is illustrated with supply chains under the risk of disruption. Third, we consider the use of historical data in stochastic programming to generate the probabilities and outcomes, and illustrate it with an application to process networks. Finally, we briefly describe multistage stochastic programming with both exogenous and endogenous uncertainties, which is applied to the design of oilfield infrastructures.

Original languageEnglish (US)
Pages (from-to)893-909
Number of pages17
JournalTheoretical Foundations of Chemical Engineering
Volume51
Issue number6
DOIs
StatePublished - Nov 1 2017
Externally publishedYes

Bibliographical note

Funding Information:
The authors would like to acknowledge financial support from NSF Grant No. 1159443, Praxair, Dow Chemical and the Center for Advanced Process Decision-making.

Publisher Copyright:
© 2017, Pleiades Publishing, Ltd.

Keywords

  • endogenous uncertainty
  • exogenous uncertainty
  • robust optimization
  • scenario generation
  • stochastic programming

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