A Distributionally Robust Optimization Method for Passenger Flow Control Strategy and Train Scheduling on an Urban Rail Transit Line
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 distributional...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-05-01
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| Series: | Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2095809921005373 |
| _version_ | 1817970663304986624 |
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| author | Yahan Lu Lixing Yang Kai Yang Ziyou Gao Housheng Zhou Fanting Meng Jianguo Qi |
| author_facet | Yahan Lu Lixing Yang Kai Yang Ziyou Gao Housheng Zhou Fanting Meng Jianguo Qi |
| author_sort | Yahan Lu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T20:37:00Z |
| format | Article |
| id | doaj.art-636be72625264c9f8b139e5315f77b7a |
| institution | Directory Open Access Journal |
| issn | 2095-8099 |
| language | English |
| last_indexed | 2024-04-13T20:37:00Z |
| publishDate | 2022-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Engineering |
| spelling | doaj.art-636be72625264c9f8b139e5315f77b7a2022-12-22T02:31:00ZengElsevierEngineering2095-80992022-05-0112202220A Distributionally Robust Optimization Method for Passenger Flow Control Strategy and Train Scheduling on an Urban Rail Transit LineYahan Lu0Lixing Yang1Kai Yang2Ziyou Gao3Housheng Zhou4Fanting Meng5Jianguo Qi6State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, ChinaCorresponding authors.; State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, ChinaState Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, ChinaCorresponding authors.; State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, ChinaState Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, ChinaState Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, ChinaState Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, ChinaRegular 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.http://www.sciencedirect.com/science/article/pii/S2095809921005373Passenger flow controlTrain schedulingDistributionally robust optimizationStochastic and dynamic passenger demandAmbiguity set |
| spellingShingle | 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 Engineering Passenger flow control Train scheduling Distributionally robust optimization Stochastic and dynamic passenger demand Ambiguity set |
| title | A Distributionally Robust Optimization Method for Passenger Flow Control Strategy and Train Scheduling on an Urban Rail Transit Line |
| title_full | A Distributionally Robust Optimization Method for Passenger Flow Control Strategy and Train Scheduling on an Urban Rail Transit Line |
| title_fullStr | A Distributionally Robust Optimization Method for Passenger Flow Control Strategy and Train Scheduling on an Urban Rail Transit Line |
| title_full_unstemmed | A Distributionally Robust Optimization Method for Passenger Flow Control Strategy and Train Scheduling on an Urban Rail Transit Line |
| title_short | A Distributionally Robust Optimization Method for Passenger Flow Control Strategy and Train Scheduling on an Urban Rail Transit Line |
| title_sort | distributionally robust optimization method for passenger flow control strategy and train scheduling on an urban rail transit line |
| topic | Passenger flow control Train scheduling Distributionally robust optimization Stochastic and dynamic passenger demand Ambiguity set |
| url | http://www.sciencedirect.com/science/article/pii/S2095809921005373 |
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