典型文献
A Distributionally Robust Optimization Method for Passenger Flow Control Strategy and Train Scheduling on an Urban Rail Transit Line
文献摘要：
Regular coronavirus disease 2019(COVID-19)epidemic prevention and control have raised new require-ments that necessitate operation-strategy innovation in urban rail transit.To alleviate increasingly seri-ous 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 passen-gers 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 pro-posed approaches.
文献关键词：
中图分类号：
作者姓名：
Yahan Lu;Lixing Yang;Kai Yang;Ziyou Gao;Housheng Zhou;Fanting Meng;Jianguo Qi
作者机构：
State Key Laboratory of Rail Traffic Control and Safety,Beijing Jiaotong University,Beijing 100044,China
文献出处：
引用格式：
[1]Yahan Lu;Lixing Yang;Kai Yang;Ziyou Gao;Housheng Zhou;Fanting Meng;Jianguo Qi-.A Distributionally Robust Optimization Method for Passenger Flow Control Strategy and Train Scheduling on an Urban Rail Transit Line)[J].工程（英文）,2022(05):202-220
A类：
Distributionally,passen
B类：
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AB值：
0.699112
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