Optimizing high-speed railway express system under uncertainty

Abstract

The rapid expansion of high-speed railway (HSR) networks has elevated the HSR express system, which combines road and HSR transport, as a notable intermodal transport choice. This research delves into the optimization challenges present in the HSR express system's operation, given the unpredictable freight demands. These challenges encompass vehicle arrangement, station selection, freight allocation, and the optimization of HSR freight routes. To address these challenges, we introduce a two-stage mixed integer linear programming model aiming to optimize the anticipated net gains of the HSR express system. We also put forward a meta-heuristic method to efficiently solve the model. Empirical tests and sensitivity studies, grounded in a real-world example from the China Railway Nanchang Group Co., Ltd., are executed. Additionally, advanced techniques, including parallel computing tailored to the problem's nature, have been employed to trim down computational times, especially for intricate real-world scenarios. The findings from this study provide valuable insights for industry practitioners.