Distributionally robust last-train coordination planning problem with dwell time adjustment strategy

Yang, Kai; Lu, Yahan; Yang, Lixing; Gao, Ziyou

doi:10.1016/j.apm.2020.10.035

Cited by 16 publications

(3 citation statements)

References 33 publications

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“…For simplicity, we adopt two common assumptions: (1) the walking times of all passengers at transfer stations are fixed [18] and (2) the train capacity is not considered so that passengers can board the first arriving train [26]. at is, a successful train connection must satisfy the requirements that the difference between the departure time of the connecting train and the arrival time of the feeder train is minimal and not less than the transfer time.…”

Section: Passenger Travel Timesmentioning

confidence: 99%

First-Train Timetable Synchronization in Metro Networks under Origin-Destination Demand Conditions

Chai

Tian

Niu

2022

Journal of Advanced Transportation

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This paper focuses on how to synchronize network-wide timetables of first trains in an urban metro system, in which the train-connection-based route can be exactly determined for each first-train-attached origin-destination (OD) demand pair. With the help of even headway scheduling on each line, the problem is actually to adjust the departure times of first trains and connecting trains from their origin stations and the departure interval on each line. Subjected to train operation and connection constraints, a biobjective nonlinear integer programming model is formulated to minimize the total travel time of OD-dependent passenger demands and the deviation between the known and expected schedules. Then, the Nondominated Sorted Genetic Algorithm-II (NSGA-II) is adopted to solve the proposed model, and an improved technique is elaborated to reduce the alternative route choices. Finally, numerical experiments are conducted to demonstrate the effectiveness and availability of the proposed model and methods.

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Section: Passenger Travel Timesmentioning

confidence: 99%

First-Train Timetable Synchronization in Metro Networks under Origin-Destination Demand Conditions

Chai

Tian

Niu

2022

Journal of Advanced Transportation

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“…Esfahani and Kuhn [40] proved that the dual form of a Wasserstein-metric DRO model could be decomposed into finitely convex optimization problems with tractable computational complexity. Recently, this approach has also attracted much attention from certain engineering fields [41] , [42] , [43] , [44] , [45] , [46] , [47] . For example, focusing on the planning and scheduling problem under uncertain demand, Shang and You [41] formulated a two-stage DRO model to produce less conservative solutions.…”

Section: Introductionmentioning

confidence: 99%

“…For a disaster relief management problem under demand uncertainty, Wang et al [44] proposed a DRO model to simultaneously optimize the integrated facility location, inventory pre-positioning, and delivery decisions. Yang et al [45] presented a distributionally robust chance-constrained program to model the last-train coordination planning problem in an urban rail transit system, in which uncertain passenger demand was captured by an ambiguous set. Wang et al [46] focused on the hub location problem with multiple commodities under uncertain demand and cost.…”

Section: Introductionmentioning

confidence: 99%

A Distributionally Robust Optimization Method for Passenger Flow Control Strategy and Train Scheduling on an Urban Rail Transit Line

Yang

et al. 2022

Engineering

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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 (mean-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 distributionally robust 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.

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Last-train timetable synchronization for service compatibility maximization in urban rail transit networks with arrival uncertainties

Du,

Ye,

Chen

et al. 2025

Applied Mathematical Modelling

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Distributionally robust last-train coordination planning problem with dwell time adjustment strategy

Cited by 16 publications

References 33 publications

First-Train Timetable Synchronization in Metro Networks under Origin-Destination Demand Conditions

First-Train Timetable Synchronization in Metro Networks under Origin-Destination Demand Conditions

A Distributionally Robust Optimization Method for Passenger Flow Control Strategy and Train Scheduling on an Urban Rail Transit Line

Last-train timetable synchronization for service compatibility maximization in urban rail transit networks with arrival uncertainties

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