Article: Optimisation of energy supply chain and global value chain based on genetic algorithm Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.38 pp.23 - 41 Abstract: Amid accelerating energy transition and geopolitical shifts, optimising energy supply and global value chains faces complex challenges. Traditional methods struggle with renewable volatility and policy divergence. Genetic algorithms demonstrate superior multi-objective optimisation. Compared to non-dominated sorting genetic algorithm II (NSGA-II), they achieve 24.3% better Pareto solution coverage and 37.6% faster convergence. A 12-dimensional chromosome encoding enables dynamic global energy network reconstruction, raising the integrated wind-solar-hydrogen utilisation rate to 89.4% - a 19.2-point increase. An adaptive mutation operator (range 0.05-0.15) improves system recovery by 43.8% under simulated geopolitical disruptions. Integrated blockchain ensures under-0.8-second verification and 98.7% accuracy in cross-border carbon tracing. Overall, the model cuts global energy trade costs by 22.4% and reduces carbon emission intensity by 31.9%, providing quantitative support for resilient value chains. Inderscience Publishers - linking academia, business and industry through research

Title: Optimisation of energy supply chain and global value chain based on genetic algorithm

Authors: Qing Bai

Addresses: School of Economics and Management, North University of China, Taiyuan, 030051, China

Abstract: Amid accelerating energy transition and geopolitical shifts, optimising energy supply and global value chains faces complex challenges. Traditional methods struggle with renewable volatility and policy divergence. Genetic algorithms demonstrate superior multi-objective optimisation. Compared to non-dominated sorting genetic algorithm II (NSGA-II), they achieve 24.3% better Pareto solution coverage and 37.6% faster convergence. A 12-dimensional chromosome encoding enables dynamic global energy network reconstruction, raising the integrated wind-solar-hydrogen utilisation rate to 89.4% - a 19.2-point increase. An adaptive mutation operator (range 0.05-0.15) improves system recovery by 43.8% under simulated geopolitical disruptions. Integrated blockchain ensures under-0.8-second verification and 98.7% accuracy in cross-border carbon tracing. Overall, the model cuts global energy trade costs by 22.4% and reduces carbon emission intensity by 31.9%, providing quantitative support for resilient value chains.

Keywords: genetic algorithm; energy supply chain; global value chain; collaborative optimisation; low carbonisation.

DOI: 10.1504/IJICT.2026.153305

International Journal of Information and Communication Technology, 2026 Vol.27 No.38, pp.23 - 41

Received: 10 Sep 2025
Accepted: 02 Dec 2025
Published online: 01 May 2026 *

Title: Optimisation of energy supply chain and global value chain based on genetic algorithm

Keep up-to-date