SciTePress - Affiliation Profile

Research.Publish.Connect.

*Please fill out at least one Field. *Value must be an number!

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

*Please fill out at least one Field.

Name:
Country:
Subject:

Advanced Search Affiliations Search

If you're looking for an exact phrase use quotation marks on text fields.

Proceedings

Proceedings Search *Please fill out at least one Field. *Value must be an number!

Title:
ISBN:
Year:
Acronym:
Subject:

Advanced Search Proceedings Search

If you're looking for an exact phrase use quotation marks on text fields.

Papers

Papers Search *Please fill out at least one Field.

Title:
Author:
Affiliation:
Subject:

Advanced Search Papers Search

If you're looking for an exact phrase use quotation marks on text fields.

Authors

Authors Search *Please fill out at least one Field.

Name:
Affiliation:
Country:
Conference:
Subject:

Advanced Search Authors Search

If you're looking for an exact phrase use quotation marks on text fields.

Advanced Search

Affiliation

Laboratoire des Signaux et Systèmes (L2S), CentraleSupélec, Université Paris Saclay, 3, rue Joliot Curie, 91192 Gif Sur Yvette Cedex, France

Context (please use complete words only)

Field

Sort by

1 Papers

A Neurodynamic Duplex for Distributionally Robust Joint Chance-Constrained Optimization

Topics: Simulation; Dynamic Programming; Stochastic Optimization; OR in Logistics, Maintenance, and Supply

Authors: Siham Tassouli and Abdel Lisser

Affiliation: Laboratoire des Signaux et Systèmes (L2S), CentraleSupélec, Université Paris Saclay, 3, rue Joliot Curie, 91192 Gif Sur Yvette Cedex, France

Digital Object Identifier: 10.5220/0012262100003639

Proceedings of ICORES 2024, ISBN: 978-989-758-681-1

Quick 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.