Trade Data Harmonization: A Multi-Objective Optimization Approach for Subcategory Alignment and Volume Optimization

Himadri Khargharia, Sid Shakya, Dymitr Ruta

2024

Abstract

Aligning trade data from disparate sources poses challenges due to volume disparities and category naming variations. This study aims to harmonize subcategories from a secondary dataset with those of a primary dataset, focusing on aligning the number and combined volumes of subcategories. We employ a multi-objective optimization approach using Non-dominated Sorting Genetic Algorithm II (NSGA-II) to facilitate trade-off assessments and decision-making via Pareto fronts. NSGA-II’s performance is compared with single-objective optimization techniques, including Genetic Algorithm (GA), Population-based Incremental Learning (PBIL), Distribution Estimation using Markov Random Field (DEUM), and Simulated Annealing (SA). The comparative analysis highlights NSGA-II’s efficacy in managing trade data complexities and achieving optimal solutions, demonstrating the effectiveness of meta-heuristic approaches in this context.

Paper Citation

in Harvard Style

Khargharia H., Shakya S. and Ruta D. (2024). Trade Data Harmonization: A Multi-Objective Optimization Approach for Subcategory Alignment and Volume Optimization. In Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: ECTA; ISBN 978-989-758-721-4, SciTePress, pages 338-345. DOI: 10.5220/0013052500003837

in Bibtex Style

@conference{ecta24,
author={Himadri Khargharia and Sid Shakya and Dymitr Ruta},
title={Trade Data Harmonization: A Multi-Objective Optimization Approach for Subcategory Alignment and Volume Optimization},
booktitle={Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: ECTA},
year={2024},
pages={338-345},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013052500003837},
isbn={978-989-758-721-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computational Intelligence - Volume 1: ECTA
TI - Trade Data Harmonization: A Multi-Objective Optimization Approach for Subcategory Alignment and Volume Optimization
SN - 978-989-758-721-4
AU - Khargharia H.
AU - Shakya S.
AU - Ruta D.
PY - 2024
SP - 338
EP - 345
DO - 10.5220/0013052500003837
PB - SciTePress