Optimization of Vehicle Routing in Green Cold Chain Logistics Distribution Considering Customer Satisfaction

Abstract

In response to growing environmental regulations and increasing customer expectations, this study proposes a comprehensive cost minimization model for green cold chain vehicle routing that jointly considers operational costs, carbon emissions, and customer satisfaction. The model incorporates fixed and load-dependent fuel costs, carbon tax, and time window penalties into a unified cost function. To solve this complex problem, an enhanced genetic algorithm is developed, integrating multiround roulette selection, partially-mapped crossover (PMX), and inversion mutation to improve convergence and solution diversity. A real-world case study based on data from a Shanghai cold chain distribution center shows that the proposed method reduces total cost by 31.1%, carbon-related costs by 50.0%, and time window penalties by 40.1% than their current operational scheme, while using fewer vehicles. These results demonstrate the practical value of our approach in balancing sustainability and service quality. Unlike prior models focused on isolated objectives or synthetic data, this study presents a unified, data-driven framework suitable for real operational environment in cold chain logistics.