Energy-efficient metro train rescheduling with uncertain time-variant passenger demands: An approximate dynamic programming approach

Yin, Jianqin; Tang, Tao; Yang, Lixing; Gao, Ziyou; Ran, Bin

doi:10.1016/j.trb.2016.05.009

Cited by 266 publications

(106 citation statements)

References 53 publications

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“…Barrena et al [14] studied the design and optimization of train timetables for a rail rapid transit line to minimize the average passenger waiting time in a dynamic demand environment, in which an adaptive large neighborhood search metaheuristic was proposed to solve large instances of the problem. Yin et al [15] developed a stochastic programming model for metro train rescheduling problem in order to jointly reduce the time delay of affected passengers, their total traveling time, and operational costs of trains. Yin et al [16] converted the train operation problem into a Markov decision process with nondeterministic state transition probabilities to minimize the cost for both the total time delay and energy consumption in a subway line.…”

Section: Literature Reviewmentioning

confidence: 99%

Last-Train Timetabling under Transfer Demand Uncertainty: Mean-Variance Model and Heuristic Solution

Yang

Gao

et al. 2017

Journal of Advanced Transportation

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Traditional models of timetable generation for last trains do not account for the fact that decision-maker (DM) often incorporates transfer demand variability within his/her decision-making process. This study aims to develop such a model with particular consideration of the decision-makers' risk preferences in subway systems under uncertainty. First, we formulate an optimization model for last-train timetabling based on mean-variance (MV) theory that explicitly considers two significant factors including the number of successful transfer passengers and the running time of last trains. Then, we add the mean-variance risk measure into the model to generate timetables by adjusting the last trains' departure times and running times for each line. Furthermore, we normalize two heterogeneous terms of the risk measure to provide assistance in getting reasonable results. Due to the complexity of MV model, we design a tabu search (TS) algorithm with specifically designed operators to solve the proposed timetabling problem. Through computational experiments involving the Beijing subway system, we demonstrate the computational efficiency of the proposed MV model and the heuristic approach.

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Section: Literature Reviewmentioning

confidence: 99%

Last-Train Timetabling under Transfer Demand Uncertainty: Mean-Variance Model and Heuristic Solution

Yang

Gao

et al. 2017

Journal of Advanced Transportation

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“…The passenger arriving and alighting at the metro station for a given period 0 ,   final tt can be modelled by a time-dependent origin-destination table [44][45][46].…”

Section: Passenger Characteristicsmentioning

confidence: 99%

Optimizing the Energy-Efficient Metro Train Timetable and Control Strategy in Off-Peak Hours with Uncertain Passenger Demands

Feng

Liu

et al. 2017

Energies

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How to reduce the energy consumption of metro trains by optimizing both the timetable and control strategy is a major focus. Due to the complexity and difficulty of the combinatorial operation problem, the commonly-used method to optimize the train operation problem is based on an unchanged dwelling time for all trains at a specific station. Here, we develop a simulation-based method to design an energy-efficient train control strategy under the optimized timetable constraints, which assign the dwelling time margin to the running time. This time margin is caused by dynamically uncertain passenger demands in off-peak hours. Firstly, we formulate a dwelling time calculation model to minimize the passenger boarding and alighting time. Secondly, we design an optimal train control strategy with fixed time and develop a time-based model to describe mass-belt train movement. Finally, based on this simulation module, we present numerical examples based on the real-world operation data from the Beijing metro Line 2, in which the energy consumption of one train can be reduced by 21.9%. These results support the usefulness of the proposed approach.

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“…In manufacturing domain, most of the rescheduling problems deal with machine disruptions [81][82][83][84][85] or machine breakdowns [86][87][88][89][90][91][92], while disturbances vary a lot in service industries. For example, in the transportation domain, disruptions may be caused by the delay of transportation tools [93][94][95][96][97]; in hospital, the arrival of emergency patients is the major reason for rescheduling [98,99], while disruptions can be observed in computer system because of resource request failures [100,101].…”

Section: 2mentioning

confidence: 99%

“…According to the distribution of publications among different optimization criteria, as shown in Figure 5, it is obvious that much more managerial and societal criteria are considered in rescheduling optimization; this phenomenon is due to the fact that most of the rescheduling problems are raised from the field of service managements, such as hospital management [102,103] and transportation management [93,96], where the requirements of the customers must also be considered.…”

Section: 2mentioning

confidence: 99%

A Survey of Recent Research on Optimization Models and Algorithms for Operations Management from the Process View

Fei

Shen

2017

Scientific Programming

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Over the past decades, optimization in operations management has grown ever more popular not only in the academic literature but also in practice. However, the problems have varied a lot, and few literature reviews have provided an overview of the models and algorithms that are applied to the optimization in operations management. In this paper, we first classify crucial optimization areas of operations management from the process point of view and then analyze the current status and trends of the studies in those areas. The purpose of this study is to give an overview of optimization modelling and resolution approaches, which are applied to operations management.

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Energy-efficient metro train rescheduling with uncertain time-variant passenger demands: An approximate dynamic programming approach

Cited by 266 publications

References 53 publications

Last-Train Timetabling under Transfer Demand Uncertainty: Mean-Variance Model and Heuristic Solution

Last-Train Timetabling under Transfer Demand Uncertainty: Mean-Variance Model and Heuristic Solution

Optimizing the Energy-Efficient Metro Train Timetable and Control Strategy in Off-Peak Hours with Uncertain Passenger Demands

A Survey of Recent Research on Optimization Models and Algorithms for Operations Management from the Process View

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