Optimizing port containers logistics considering blockchain technology for sustainable maritime shipping: A case study

• Bi-level model improves empty container balance across port networks. • Blockchain lowers warehousing and handling costs in maritime logistics. • Model includes pricing for hazardous vs. non-hazardous cargo. • GHG emissions reduced through optimized container repositioning. • Genetic-KKT algorithm improves solution quality and runtime. The maritime shipping industry faces persistent challenges caused by the global imbalance of empty containers, leading to elevated operational costs and increased greenhouse gas emissions. To address this, a bi-level programming model for sustainable empty container balancing in ports using blockchain technology (BP-SECBPB) is proposed. In this framework, the shipping company acts as the leader, optimizing freight pricing to maximize profit and minimize emissions, while the non-vessel operating common carrier (NVOCC) operates as the follower, seeking to maximize profit and minimize container imbalance. The shipping company establishes the initial price of shipping, and after the determination of the shipping price and actual demand, the NVOCC sets the transportation price for its customers. The model accounts for differentiated pricing based on hazardous and non-hazardous cargo type and leverages blockchain to reduce warehousing and cargo handling expenses. A novel matheuristic solution method, combining a genetic algorithm with the Karush–Kuhn–Tucker (KKT) conditions, is developed to solve the model. The model is validated using a real-world case study from key ports in Asia. Sensitivity analyses show that full blockchain adoption reduces total system cost by over 26 % and container imbalance by 6.6 %, while partial adoption at strategic ports still achieves a 17 % cost reduction. Furthermore, the model maintains stability under crisis-driven demand surges, with only a 4.5 % rise in imbalance. More elasticity evaluation demonstrates the model can react to different consumer behaviors. These results demonstrate the model’s practical robustness, strategic value, and responsiveness to real-world disruptions and pricing environments.