Recent advances in mathematical programming techniques for the optimization of process systems under uncertainty

Ignacio E. Grossmann, Robert M. Apap, Bruno A. Calfa, Pablo García-Herreros, Qi Zhang

Research output: Contribution to journalArticlepeer-review

88 Scopus citations

Abstract

Optimization under uncertainty has been an active area of research for many years. However, its application in Process Systems Engineering has faced a number of important barriers that have prevented its effective application. Barriers include availability of information on the uncertainty of the data (ad-hoc or historical), determination of the nature of the uncertainties (exogenous vs. endogenous), selection of an appropriate strategy for hedging against uncertainty (robust/chance constrained optimization vs. stochastic programming), large computational expense (often orders of magnitude larger than deterministic models), and difficulty of interpretation of the results by non-expert users. In this paper, we describe recent advances that have addressed some of these barriers for mostly linear models.

Original languageEnglish (US)
Pages (from-to)3-14
Number of pages12
JournalComputers and Chemical Engineering
Volume91
DOIs
StatePublished - Sep 26 2016
Externally publishedYes

Keywords

  • Decision rule
  • Endogenous uncertainty
  • Exogenous uncertainty
  • Robust optimization
  • Scenario generation
  • Stochastic programming

Fingerprint Dive into the research topics of 'Recent advances in mathematical programming techniques for the optimization of process systems under uncertainty'. Together they form a unique fingerprint.

Cite this