Hierarchical layout robust optimization model for integrated passenger hub at national level

In order to improve the efficiency of integrated passenger hub at national level, the relationship between integrated passenger hub classification and passenger transport demand is analyzed. The uncertainty of the passenger transport demand in the process of urbanization is taken into account. A model based on capacitated hierarchical location model and stochastic p-robust (p is relative regret restricted coefficient) optimization is proposed. An immune clonal algorithm is designed to solve the problem and a numerical example is given to validate the effectiveness of the model and algorithm. The results show that hierarchical layout model is superior to non-hierarchical layout model. When p is less than the maximum relative regret of hierarchical stochastic (HS) model, the result of hierarchical robust (HR) model has stronger robustness than that of the HS model and the immune clonal algorithm performs more efficiently than optimization software CPLEX. The value of p, the coverage of hubs and the scenario probability greatly affect the layout planning scheme. The HR model provides decision references for integrated passenger hub layout planning. ©, 2015, Southeast University. All right reserved.