Brief An algorithm for constrained nonlinear optimization under uncertainty

Authors:
J. Darlington;C. C. Pantelides;B. Rustem;B. A. Tanyi
Affiliations:
Department of Computing, Imperial College of Science, Technology and Medicine, 180 Queens Gate, London SW7 2BZ UK;Centre for Process Systems Engineering Imperial College of Science, Technology and Medicine, Prince Consort Road, London SW7, UK;Department of Computing, Imperial College of Science, Technology and Medicine, 180 Queens Gate, London SW7 2BZ UK;Department of Computing, Imperial College of Science, Technology and Medicine, 180 Queens Gate, London SW7 2BZ UK
Venue:
Automatica (Journal of IFAC)
Year:
1999

Citing 5
Cited 1

Expected utility, penalty functions, and duality in stochastic nonlinear programming

Management Science
A recourse certainty equivalent for decisions under uncertainty

Annals of Operations Research
Robust capacity planning under uncertainty

Management Science
Dynamic Programming and Stochastic Control

Dynamic Programming and Stochastic Control
An Algorithm for the Inequality-Constrained Discrete Min--Max Problem

SIAM Journal on Optimization

A robust optimization model for a cross-border logistics problem with fleet composition in an uncertain environment

Mathematical and Computer Modelling: An International Journal

Quantified Score

Hi-index	22.14

Visualization

Abstract

This paper considers robust formulations for the constrained control of systems under uncertainty. The underlying model is nonlinear and stochastic. A mean-variance robustness framework is adopted. We consider formulations to ensure feasibility over the entire domain of the uncertain parameters. However, strict feasibility may not always be possible, and can also be very expensive. We consider two alternative approaches to address feasibility. Flexibility in the operational conditions is provided via a penalty framework. The robust strategies are tested on a dynamic optimization problem arising from a chemical engineering application.