A Distributionally Robust Optimization Method for Passenger Flow Control Strategy and Train Scheduling on an Urban Rail Transit Line. (May 2022)

• Record Type: Journal Article
• Authors: Lu, Yahan; Yang, Lixing; Yang, Kai; Gao, Ziyou; Zhou, Housheng; Meng, Fanting; Qi, Jianguo
• Is Part Of: Engineering. Volume 12(2022)
• Page Start: 202
• Page End: 220
• Publication Date: 2022-05
• Subjects: Passenger flow control -- Train scheduling -- Distributionally robust optimization -- Stochastic and dynamic passenger demand -- Ambiguity set; Engineering -- Periodicals; Engineering -- China -- Periodicals; 620.005
• DOI: 10.1016/j.eng.2021.09.016
• Languages: English
• ISSNs: 2095-8099

Abstract

Abstract: Regular coronavirus disease 2019 (COVID-19) epidemic prevention and control have raised new requirements that necessitate operation-strategy innovation in urban rail transit. To alleviate increasingly serious congestion and further reduce the risk of cross-infection, a novel two-stage distributionally robust optimization (DRO) model is explicitly constructed, in which the probability distribution of stochastic scenarios is only partially known in advance. In the proposed model, the mean-conditional value-at-risk (CVaR) criterion is employed to obtain a tradeoff between the expected number of waiting passengers and the risk of congestion on an urban rail transit line. The relationship between the proposed DRO model and the traditional two-stage stochastic programming (SP) model is also depicted. Furthermore, to overcome the obstacle of model solvability resulting from imprecise probability distributions, a discrepancy-based ambiguity set is used to transform the robust counterpart into its computationally tractable form. A hybrid algorithm that combines a local search algorithm with a mixed-integer linear programming (MILP) solver is developed to improve the computational efficiency of large-scale instances. Finally, a series of numerical examples with real-world operation data are executed to validate the proposed approaches.