A machine learning and evolutionary optimization framework for carbon-aware supply chain routing

Abstract

The increasing urgency of carbon footprint reduction in supply chain operations demands innovative optimization approaches that balance economic efficiency with environmental sustainability. This paper presents a novel carbon-aware route optimization framework that integrates machine learning-based emission prediction with genetic algorithm optimization for sustainable supply chain management. Our hybrid approach combines Random Forest and XGBoost models in an optimized ensemble to predict carbon emissions with high accuracy (MAPE: 9.48%, R${}^2$: 0.928), while a genetic algorithm optimizes routes considering both cost and carbon constraints. The framework is validated through two complementary scenarios: (1) controlled experiments on synthetic datasets (n=3,500 routes across three network sizes: 500, 1000, and 2000 routes) derived from real-world emission factors demonstrate 19.5% average emission reduction with 4.7% cost increase, and (2) a quasi-real case study on Salamanca regional distribution network (n=12 routes, 776.6 tons CO2e annually) achieves a 41.4% emission reduction with 8.6% cost increase through strategic modal shifts to rail transport. Both scenarios significantly outperform traditional cost-only optimization methods. The proposed approach provides supply chain managers with actionable insights for achieving sustainability goals while maintaining operational efficiency.