Code and Data Repository for Machine Learning for K-Adaptability in Two-Stage Robust Optimization

This directory contains the code for the K-adaptability branch-and-bound (K-B&B) algorithm, with random node selection and ML-informed node selection (i.e., K-B&B-NodeSelection). K-adaptability is a solution approach for two-stage robust optimization problems.

Authors

Krzysztof Postek (ORCID: https://orcid.org/0000-0002-4028-3725)
Esther Julien (ORCID: https://orcid.org/0000-0002-7337-1086)
Ş. İlker Birbil (ORCID: https://orcid.org/0000-0001-7472-7032)

Publication Details

Journal: INFORMS journal on computing
Published: 2024-09-02
DOI: https://doi.org/10.1287/ijoc.2022.0314.cd
Primary Topic: Metaheuristic Optimization Algorithms Research
Type: article
Field-Weighted Citation Impact: 0.00

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article

Code and Data Repository for Machine Learning for K-Adaptability in Two-Stage Robust Optimization

Krzysztof Postek, Esther Julien, Ş. İlker Birbil

Sep 2, 2024

INFORMS journal on computing

Metaheuristic Optimization Algorithms Research

article

Code and Data Repository for Machine Learning for K-Adaptability in Two-Stage Robust Optimization

Krzysztof Postek, Esther Julien, Ş. İlker Birbil

September 2, 2024article en

Abstract

INFORMS journal on computing

Openalex Percentile: Top 88%

Metaheuristic Optimization Algorithms Research

DOI Source OpenAlex

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Code and Data Repository for Machine Learning for K-Adaptability in Two-Stage Robust Optimization — Krzysztof Postek, Esther Julien, et al. · INFORMS journal on computing (2024) | TGRS Research Map | TGRS