Vehicle Routing Optimization for Cold Chain Logistics Considering Customer Types and Simultaneous Pick-up and Delivery of Heterogeneous Fleet under Carbon Emissions

Vehicle routing optimization has proven effective in reducing enterprise operating costs and improving customer satisfaction in logistics management. However, existing studies rarely incorporate customer value differentiation into operational decision-making processes, resulting in a disconnect between service priority and customer importance in cold chain logistics. To bridge this gap, this paper proposes a customer-centered optimization model that prioritizes service for high-value customers through differentiated penalty functions for time window violations. The objective function minimizes the total cost, including carbon emission, energy consumption, fixed cost, refrigeration, cargo damage, courier waiting, and customer penalty costs. A genetic algorithm (GA) was developed and validated against CPLEX on small-scale instances, achieving an absolute optimal solution gap within 2.0%. For medium- and large-scale instances, CPLEX encountered memory errors, while the GA efficiently obtained feasible solutions for all instances. Experiments were conducted on instances adapted from Solomon benchmarks with three customer distribution patterns and five random seeds. A comprehensive sensitivity analysis confirmed the robustness of the optimization approach, ensuring that high-value customers do not experience time window violations. The proposed penalty function for different customer types achieved the best performance in relation to total cost. These findings provide decision support and a theoretical basis for customer-centered cold chain logistics operations.