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Adaptive Neural Network for CO 2 Reduction

Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Open access

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.

Publication Information

Output type

Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 330-336 (7 pages)

Publication milestones

  • Published - 15/02/2025

Publication status

Published - 15/02/2025

Publisher

Springer, Japan, India, Australia, Germany, United States, United Arab Emirates, Austria, Switzerland, Italy, China, United Kingdom, Netherlands, Brazil, France, Singapore

Publication series

  • Publication series name: Lecture Notes in Electrical Engineering
    ISSN (Print): 1876-1100
    ISSN (Electronic): 1876-1119
    Volume: 1364 LNEE

ISBN (Print)

9789819626243

External Publication IDs

  • handle.net: 10547/626811
  • Scopus: 85218946165

Host publication title

Advanced Manufacturing and Automation XIV

Host publication editors

  • Yi Wang
  • Tao Yu
  • Kesheng Wang

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