Less-than-container cargo scheduling for China Railway Express along belt and road initiative routes

Abstract

With the rapid expansion of global trade, the use of LCL(Less than container load) transportation in international trade is becoming increasingly widespread. This study explores the application of LCL transportation in the context of China Railway Express (CR Express). Addressing the challenges of low cargo loading efficiency and complex container scheduling in CR Express LCL services, we aim to maximize customer satisfaction and develop a multi-objective mixed-integer programming model. The model aims to minimize the number of containers used and the maximum transportation time. To effectively tackle large-scale instances, we have designed an efficient genetic algorithm enhanced with an iterative local search (ILS-GA). Computational experiments across small, medium, and large instances reveal that ILS-GA identifies optimal solutions in small-scale instances. ILS-GA discovers the optimal solution within an average runtime of 5.45 s, which is 95.56% faster than CPLEX’s 180 s, demonstrating its high solution efficiency. In medium and large instances, compared to CPLEX and SA, ILS-GA provides better solutions with higher computational efficiency, significantly outperforming the SA algorithm in terms of global search capability and optimization efficiency. Additionally, we analyze the initialization and local iterative search strategies through experiments, verifying the proposed strategies’ effectiveness in improving the ILS-GA solutions.