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Related Experiment Video

Updated: May 15, 2026

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

A Comparative Theoretical and Computational Study on Robust Counterpart Optimization: II. Probabilistic Guarantees on

Zukui Li¹, Christodoulos A Floudas

¹Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544, USA.

Industrial & Engineering Chemistry Research

|January 19, 2013

Summary

This study provides probabilistic guarantees for robust counterpart optimization under various uncertainty sets. Findings offer practitioners more flexibility in selecting tighter probability bounds for less conservative robust solutions.

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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

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Area of Science:

Operations Research
Optimization Theory

Background:

Robust counterpart optimization addresses uncertainty in decision-making.
Existing methods often yield conservative solutions.

Purpose of the Study:

To derive probabilistic guarantees for constraint satisfaction in robust counterpart optimization.
To investigate different uncertainty set formulations and their impact on solution conservatism.

Main Methods:

Formulating robust counterpart optimization problems based on box, ellipsoidal, polyhedral, and combined uncertainty sets.
Deriving probability bounds on constraint satisfaction for bounded and unbounded uncertainty, with and without distribution information.

Main Results:

New probability bounds are derived, extending existing literature.
Numerical studies compare the tightness of probability bounds and conservatism of different formulations.
Guiding rules for model selection and uncertainty set sizing are established.

Conclusions:

The research enhances flexibility for practitioners in robust optimization.
Findings facilitate the selection of less conservative robust solutions.
Applicability demonstrated in production planning and process scheduling.