A methodology for generation of optimal schedules for an underground railway system

Cury, J.E.R.; Gomide, Fernando; Mendes, Mateus

doi:10.1109/cdc.1979.270075

Cited by 34 publications

(33 citation statements)

References 3 publications

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“…With reference to the existing studies, 13,14,16,17 a bidirectional urban rail line with only one rail yard located at one end is considered in our studies, whose layout is shown in Figure 1. The station on the end where the yard is located is called the start terminal, while the station on the other end is called return terminal.…”

Section: Problem Statementmentioning

confidence: 99%

Pareto optimal train scheduling for urban rail transit using generalized particle swarm optimization

Chu

Zhang

Chen

et al. 2016

Advances in Mechanical Engineering

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In urban rail transport, train timetable plays a crucial role, whose quality determines the whole system's performance to a large extent. In practical urban rail operation, two contradictive aspects-service quality and operation cost-should be considered during train scheduling. A good train timetable should achieve considerable service quality with as little operation cost as possible. Previously, many studies have been conducted specific to urban rail train scheduling, although most of them do not put enough emphasis on its multi-objective nature. In this article, therefore, Pareto optimal urban rail train scheduling which can give more instruction to practical operation is studied. First, referring to some existing studies, the problem is reasonably defined, which takes time-dependent origin-destination demand as the input and aims at minimizing the passengers' total travel time and the number of used train stocks. Then, an efficient iteration algorithm and a valid train stock assignment procedure are designed to calculate the passengers' total travel time and required train stock number, respectively. On that basis, the studied problem is reasonably formulated as a bi-objective optimization model and a Pareto-based particle swarm optimization procedure is designed to solve it. Finally, with two different scaled urban rail lines, the whole methodology is illustrated and the algorithm is tested.

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Section: Problem Statementmentioning

confidence: 99%

Pareto optimal train scheduling for urban rail transit using generalized particle swarm optimization

Chu

Zhang

Chen

et al. 2016

Advances in Mechanical Engineering

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“…Many researchers have studied deeply on optimization of train diagram, such as literature [1][2] proposed a hierarchical of designing diagram by layer, the former used the method of Lagrangian relaxation to decompose the original problem, then the sub-problems were solved by the alghrithm; the latter constructed a layered superposed model which was solved by an improved genetic algorithm; literature [3] proposed a optimization method of temporal window scroll local to organically combine local optimization and global optimization; Literature [4][5] proposed an method based on ordinal optimization, the former used the method of relaxation to resolute conflict; the latter introduced the method of ordinal optimization to improve computational efficiency.…”

Section: Introductionmentioning

confidence: 99%

A Method for Optimizing Train Diagram of High-speed Railway with Soft Time-window

Zeng¹,

Zhang²,

Zhao³

2016

Proceedings of the 2016 International Conference on Civil, Transportation and Environment

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This paper studies optimization of train diagram with different speed of high-speed railways, and introduces soft time window to construct the optimization model of train diagram, aiming at minimizing the difference between the arrival and departure time of the trains and the expectation of passengers with the constraints of reasonable overtakes. A global optimization heuristic algorithm is designed with the combination of strategies on conflict resolution of the lines and simulated annealing, and the case study proves the effectiveness of the optimization method and shows the convergence of the algorithm.

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“…The dynamics of metro line systems have been deeply studied by several researchers [1][2][3][4][5][7][8][9][10]. Most of these dynamical models are based on the Sasama and Ohkawa [9] linear model.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, some dynamic traffic linear controllers [1][2][3]7] and real time simulators [3,4] have been proposed. Some researchers [4,5] state the importance of nonlinear behaviour of metro lines.…”

Section: Introductionmentioning

confidence: 99%

Lyapunov based stability analysis for metro lines

Berbey

Galán

Segundo

et al. 2008

WIT Transactions on the Built Environment

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In this work a direct method to measure the stability of metro system lines with respect to a previously constructed time schedule is presented. For this purpose we first model saturation effects using a real time discrete space state representation and then apply a Lyapunov-based stability analysis considering time delays of trains as disturbances. As a result we have been able to define a new set of indexes that relate time delays with the validity of the actual time schedule when falling inside a particular 'stability area'. Results obtained in a simulated environment show that the new stability indexes are able to evaluate quantitatively and qualitatively the effects of saturation in metro lines as well as predict the need for rescheduling

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A methodology for generation of optimal schedules for an underground railway system

Cited by 34 publications

References 3 publications

Pareto optimal train scheduling for urban rail transit using generalized particle swarm optimization

Pareto optimal train scheduling for urban rail transit using generalized particle swarm optimization

A Method for Optimizing Train Diagram of High-speed Railway with Soft Time-window

Lyapunov based stability analysis for metro lines

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