Optimization of port collection and distribution network considering demand uncertainty and inland port congestion

Demand uncertainty and inland port congestion pose significant challenges to the efficient operation of port collection and distribution systems. This paper proposes a bi-level programming model that simultaneously account for demand uncertainty and inland port congestion. The upper-level model optimizes inland port location and capacity configuration by minimizing construction costs and congestion costs,while the lower-level model optimizes transport paths by minimizing transportation costs,transfer costs,carbon emission costs,and time costs. A Genetic Algorithm is employed to solve the model,with empirical analysis based on data from the Liaoning port cluster and its northeastern hinterland. The results show that higher-grade inland ports effectively absorb demand fluctuations and reduce congestion costs,while reducing carbon emissions by increasing the share of railway transport. A maximum regret value of θ = 0.25 achieves the optimal balance between total cost and stability of the Liaoning network. Additionally,a carbon tax of ￥0.5/kg achieves the best emission reduction and congestion control outcomes.