Machine Learning-Based Predictive Modeling of Conversion in an Industrial Visbreaker Unit

Rural areas face significant logistical challenges, including inadequate infrastructure, safety risks, and high transfer costs. Traditional inventory routing models often overlook these complexities, particularly the need for alternative routes and backhaul integration, where routes include deliveries and pickups. This study introduces a Multi-Objective Inventory Routing Problem in a Multigraph with Backhauls (MO-IRPMGB) and employs the Non-dominated Sorting Genetic Algorithm II (NSGA-II) for optimization, achieving average savings of 7% to 14%.

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Publication Details

Published
2025-06-24
DOI
https://doi.org/10.23919/ecc65951.2025.11187293
Primary Topic
Oil and Gas Production Techniques
Type
article
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article

Machine Learning-Based Predictive Modeling of Conversion in an Industrial Visbreaker Unit

Gizem Kuşoğlu Kaya, Erdal Aydın, Sena Kurban, Melike Duvanoğlu
Oil and Gas Production Techniques
article

Machine Learning-Based Predictive Modeling of Conversion in an Industrial Visbreaker Unit

Gizem Kuşoğlu Kaya, Erdal Aydın, Sena Kurban, Melike Duvanoğlu
article en

Abstract

Rural areas face significant logistical challenges, including inadequate infrastructure, safety risks, and high transfer costs. Traditional inventory routing models often overlook these complexities, particularly the need for alternative routes and backhaul integration, where routes include deliveries and pickups. This study introduces a Multi-Objective Inventory Routing Problem in a Multigraph with Backhauls (MO-IRPMGB) and employs the Non-dominated Sorting Genetic Algorithm II (NSGA-II) for optimization, achieving average savings of 7% to 14%.

X-Fab (Germany) (DE), CPCL Polytechnic College (IN)
Openalex Percentile: Top 64%
Oil and Gas Production Techniques
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