Dual-Network Optimization for Cold Chain Logistics: Balancing Network Investment and Operation Cost

Abstract

Cold chain logistics (CCL) is essential for maintaining the integrity of perishable products. An effective CCL system design is crucial for minimizing costs and improving system performance. The emergence of direct transportation has weakened traditional hub-based cold chain networks, exposing the shortcomings of structure-blind operating solutions. Without network-level optimization, operational strategies cannot guarantee long-term cost-efficiency and adaptability. This paper proposes a bilevel optimization model aimed at minimizing the entire operational cost (EOC), encompassing transportation, transshipment, carbon emissions, and cargo damage costs. The lower level enhances freight flow distribution based on a specified network architecture, while the upper level determines node and edge improvements within an assigned investment budget. To solve this model, a tailored hybrid heuristic algorithm is developed, combining a genetic algorithm with linear programming and Dijkstra’s method. A real-world case from the Beijing-Tianjin-Hebei region (BTHr) is employed for validation. In the BTHr case, the best-performing solution shows that as investment rises from 3.0 × 10⁸ to 1.2 × 10⁹ yuan, reduction of EOC increases from 8.93 × 10⁶ to 1.42 × 10⁷ yuan, while return on investment (ROI) declines from 1.86 to 0.74, indicating a clear decrease in investment efficiency. The majority of freight movements often prefer direct transportation within the transportation network (TN). The suggested approach offers a more pragmatic and flexible solution for optimizing cold chain networks by simultaneously addressing structural and operational aspects.