Long queues of arrival trucks are a common problem in seaports, and thus, carbon emissions generated from trucks in the queue cause environmental pollution. In order to relieve gate congestion and reduce carbon emissions, this paper proposes a lane allocation framework combining the truck appointment system (TAS) for four types of trucks. Based on the distribution of arrival times obtained from the TAS, lane allocation decisions in each appointment period are determined in order to minimize the total cost, including the operation cost and carbon emissions cost. The resultant optimization model is a non-linear fractional integer program. This model was firstly transformed to an equivalent integer program with bilinear constraints. Then, an improved branch-and-bound algorithm was designed, which includes further transforming the program into a linear program using the McCormick approximation method and iteratively generating a tighter outer approximation along the branch-and-bound procedure. Numerical studies confirmed the validity of the proposed model and algorithm, while demonstrating that the lane allocation decisions could significantly reduce carbon emissions and operation costs.

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The intermodal container terminal is an important node linking the routes of shipping and China Railway Express. This paper proposes a Markov process model to explore the optimal capacity of the reserved storage yard in the intermodal container terminal. The flow balance equations are formulated to calculate the steady-state probability. The total cost is taken as the performance measure. Numerical analysis manifests that the railway preference rate and the strategy with and without transfer obviously affect the optimum capacity of the reserved storage yard. Some useful insights for the management of the intermodal container terminal are discussed.