Multi-Objective Collaborative Optimization for Low-Carbon Cold-Chain Routing with Dynamic Demand

To address the challenges of high energy consumption, substantial carbon emissions, and dynamic customer demand in cold-chain logistics, this paper investigates the balance between sustainable development and operational efficiency for low-carbon distribution. We construct a Multi-Objective Low-Carbon Cold-Chain Vehicle Routing Problem with Dynamic Demand (MO-LC-CCDVRP) model to synergistically optimize the comprehensive costs, including vehicle dispatch, transportation adjustments, carbon emissions, and refrigeration, while maximizing customer satisfaction. To solve this model efficiently, we propose a novel deep reinforcement learning-enhanced Non-Dominated Sorting Genetic Algorithm II (DRL-NSGA-II). By using DRL to adaptively control the genetic operators, this algorithm significantly enhances both the convergence speed and distribution quality of the Pareto front. The solution process occurs in two stages: first, high-quality initial routes are generated from static information; then, upon dynamic information updates, rapid replanning is performed for unserved customers. Numerical experiments using adapted Solomon benchmark instances demonstrate the superiority of the proposed algorithm. Furthermore, a dynamic distribution case study confirms the model’s effectiveness, and a sensitivity analysis elucidates the complex impact of carbon pricing on total cost, customer satisfaction, and carbon emissions.