Robust Optimization of Energy-Saving Train Trajectories Under Passenger Load Uncertainty Based on p-NSGA-II

Railway electrification has attracted substantial interest in recent years as a key part of the global effort to achieve transport decarbonization. To improve energy efficiency train operations, particular is optimization speed trajectories. However, most studies formulate problem single-objective model and do not take into account mass uncertainty associated with passenger load variations. This article formulates biobjective robust minimize both consumption journey time, which robustness against uncertain considered viewed decision-maker preference. A novel multiobjective algorithm, namely, p-nondominated sorting genetic algorithm-II (NSGA-II), proposed, incorporating original NSGA-II proposed preference dominance criterion handle DM With p-NSGA-II, only all solutions will converge optimal Pareto front but also better be automatically selected retained; meanwhile, spread maintained. The effectiveness p-NSGA-II generate set performance-robust driving schemes verified by numerical case studies. results show that can up 40.59% improvement compared NSGA-II.