@inproceedings{a290249b84684bdd9c2de64e0a36a702,
title = "Adaptive Neural Network for CO 2 Reduction",
abstract = "This paper proposes an innovative machine learning algorithm that integrates Graph Attention Networks (GAT) with Monte Carlo Markov Chain (MCMC) techniques to optimize the Work Breakdown Structure (WBS) in crude oil vessel production. This novel approach aims to reduce CO2 emissions, minimize lead times, and enhance cost savings within the supply chain. A synthetic dataset representing 15,000 companies in the oil and gas sector was used to test the algorithm. The results demonstrate potential improvements in key metrics, paving the way for more sustainable and efficient supply chain operations.",
keywords = "Carbon Footprint, GAT, Graph Theory, MCMC, Neural Network",
author = "Lapo Chirici and Yi Wang and Kesheng Wang",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.; Advanced Manufacturing and Automation XIV ; Conference date: 11-10-2024 Through 12-10-2024",
year = "2025",
month = feb,
day = "15",
language = "English",
isbn = "9789819626243",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer",
pages = "330--336",
editor = "Yi Wang and Tao Yu and Kesheng Wang",
booktitle = "Advanced Manufacturing and Automation XIV",
address = "Germany",
}