An Integrated Optimization Method for Heavy Haul Trains with Virtual Coupling Based on Genetic Algorithm

Heavy haul transportation characterized by long trains enjoys the benefits of cost-efficiency but suffers from high cost in combination process. Virtual coupling (VC) is a state-of-art train control technology that can help resolving the dilemma. In this paper, we proposed an integer programming model that schedules station operation plans, timetables and train combination schemes of heavy haul trains (HHTs) with VC at the same time. A genetic algorithm with a deep first search decode method is proposed to efficiently solve this problem on a large scale. The simulation results show that our method can effectively improve the efficiency of HHTs in real-world scenarios.