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

详细

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.

作者简介

B. Calfa

Carnegie Mellon University

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

P. Garcia-Herreros

Carnegie Mellon University

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

Q. Zhang

Carnegie Mellon University

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

I. Grossmann

Carnegie Mellon University

编辑信件的主要联系方式.
Email: grossmann@cmu.edu
美国, Pittsburgh, PA, 15213

R. Apap

Carnegie Mellon University

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

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