Open access
Jul 2026
Low-Carbon Urban Freight Optimization: Per-Order Adaptive Mode Mixing with Demonstration-Regularized Constrained Reinforcement Learning
The findings suggest that hard carbon budgets can be controlled in expectation online at modest cost—a step toward operator-facing low-carbon logistics whose average emissions honour a binding carbon budget, though external validation on operational data and a risk-sensitive formulation that upgrades this average control into per-day compliance are still required before deployment.
Shu-Kang Zheng, Genhua Ma, Hanpei Yang et al.
· Applied Sciences · 0 citations