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

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Аннотация

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.

Авторлар туралы

B. Calfa

Carnegie Mellon University

Email: grossmann@cmu.edu
АҚШ, Pittsburgh, PA, 15213

P. Garcia-Herreros

Carnegie Mellon University

Email: grossmann@cmu.edu
АҚШ, Pittsburgh, PA, 15213

Q. Zhang

Carnegie Mellon University

Email: grossmann@cmu.edu
АҚШ, Pittsburgh, PA, 15213

I. Grossmann

Carnegie Mellon University

Хат алмасуға жауапты Автор.
Email: grossmann@cmu.edu
АҚШ, Pittsburgh, PA, 15213

R. Apap

Carnegie Mellon University

Email: grossmann@cmu.edu
АҚШ, Pittsburgh, PA, 15213

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© Pleiades Publishing, Ltd., 2017