A Neurodynamic Duplex for Distributionally Robust Joint Chance-Constrained Optimization

Siham Tassouli, Abdel Lisser

2024

Abstract

This paper introduces a new neurodynamic duplex approach to address distributionally robust joint chance-constrained optimization problems. We assume that the constraints’ row vectors are independent, and their probability distributions belong to a specific distributional uncertainty set that is not known beforehand. Within our study, we examine two uncertainty sets for these unknown distributions. Our framework’s key feature is the use of a neural network-based method to solve distributionally robust joint chance-constrained optimization problems, achieving an almost sure convergence to the optimum without relying on standard state-of-the-art solving methods. In the numerical section, we apply our proposed approach to solve a profit maximization problem, demonstrating its performance and comparing it against existing state-of-the-art methods.

Paper Citation

in Harvard Style

Tassouli S. and Lisser A. (2024). A Neurodynamic Duplex for Distributionally Robust Joint Chance-Constrained Optimization. In Proceedings of the 13th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES; ISBN 978-989-758-681-1, SciTePress, pages 15-24. DOI: 10.5220/0012262100003639

in Bibtex Style

@conference{icores24,
author={Siham Tassouli and Abdel Lisser},
title={A Neurodynamic Duplex for Distributionally Robust Joint Chance-Constrained Optimization},
booktitle={Proceedings of the 13th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES},
year={2024},
pages={15-24},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012262100003639},
isbn={978-989-758-681-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES
TI - A Neurodynamic Duplex for Distributionally Robust Joint Chance-Constrained Optimization
SN - 978-989-758-681-1
AU - Tassouli S.
AU - Lisser A.
PY - 2024
SP - 15
EP - 24
DO - 10.5220/0012262100003639
PB - SciTePress