An improved ant colony optimization strategy for dual-objective high-speed train scheduling - Hui Zhao; Jiahuan Zhang; Haixing Li; Dong Li (Journal of King Saud University: Computer and Information Sciences)
Abstract In this paper, for the high-speed train scheduling problem with operation delay of trains, a dual-objective high-speed train scheduling model with the objective function of minimizing the total delay time and total energy consumption is established by considering multiple operation constraints. Then, an improved ant colony optimization algorithm is proposed to solve the model. The heuristic information of algorithm is improved and a state transition mechanism is investigated to comprehensively consider delay time and energy consumption. In addition, the positive and negative feedback pheromone updating rules are utilized to solve train scheduling problem. The Pareto optimal solution set satisfying the dual-objective is obtained through simulation experiments. Finally, the typical solutions are selected as the scheduling scheme to analyze the scheduling results and effectiveness of the proposed scheduling strategy.