An efficient evolutionary algorithm for high-speed train rescheduling under a partial station blockage - Wang, Rongsheng; Zhang, Qi; Dai, Xuewu; Yuan, Zhiming; Zhang, Tao; Ding, Shuxin; Jin, Yaochu (Applied soft computing, 2023)
This paper investigates the high-speed train rescheduling (HSTR) problem under a partial station blockage and proposes an efficient problem-specific strengthen elitist genetic algorithm (PS-SEGA) for HSTR. Firstly, a HSTR model subject to train operation constraints is established to minimize the total train delay. A permutation-based encoding method is developed to define an efficient search space based on the train departure sequence. A heuristic decoding method is employed to eliminate all train operation constraints and output the rescheduled timetable. Moreover, a hybrid initialization method involving an efficient heuristic strategy (EHS) is put forward to accelerate the convergence speed of PS-SEGA. Using problem-specific knowledge, EHS generates an efficient and feasible solution for the initial population. Finally, a restart strategy is presented to maintain genetic diversity. Compared with other advanced evolutionary algorithms and their improved variants also using the improvements of PS-SEGA, experimental results demonstrate the effectiveness of the proposed PS-SEGA for addressing HSTR scenarios under the partial station blockage. As for the scenarios that CPLEX cannot obtain optimal solutions within 10 min, PS-SEGA can provide quasi-optimal solutions in real time. Furthermore, compared with the other two heuristics algorithms (i.e., First-Scheduled-First-Served and EHS), PS-SEGA can give the train departure sequence with a smaller total train delay. •A high-speed train rescheduling model with time constraints is established.•A permutation-based encoding method is proposed to improve solution efficiency.•A heuristic decoding method is developed to eliminate all time constraints.•Problem-specific knowledge is designed to provide an efficient rescheduling solution.•Ten real-world cases are used to verify the effectiveness of the proposed algorithm.