Optimizing trains movement on a railway network

Yang, Lixing; Li, Keping; Gao, Ziyou; Li, Xiang

doi:10.1016/j.omega.2011.12.001

Cited by 146 publications

(76 citation statements)

References 21 publications

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“…It can be seen by comparing Figure 6a and Figure 6b that G1 is effective in optimising the system described by Yang [16]. However, after optimisation there is still a large variation in the objective score of results, caused by the trajectories of the optimised results that are illustrated in Figure 7.…”

Section: /34mentioning

confidence: 92%

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Multi-train trajectory optimisation to maximise rail network energy efficiency under travel-time constraints

Goodwin

Fletcher

Harrison

2015

Proceedings of the Institution of Mechanical Engineers, Part F:

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Optimising the trajectories of multiple interacting trains to maximise energy efficiency is a difficult but highly desirable problem to solve. A bespoke genetic algorithm (GA) has been developed for the multitrain trajectory optimisation problem and used to seek a near optimal set of control point distances for multiple trains, such that a weighted sum of the time and energy objectives is minimised. Genetic operators tailored to the problem are developed including a new mutation operation and the insertion and deletion pairs of control points during the reproduction process. Compared to published results, the new GA was shown to increase the quality of solutions found by an average of 27.6% and increase consistency by a factor of 28. This allows more precise control over the relative priority given to achieving time targets or increasing energy efficiency. Keywords 2/34Multi-train trajectory optimisation, trajectory planning, train control, energy efficiency, railway network optimisation.

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Section: /34mentioning

confidence: 92%

“…al. in [16] G2 Introduces a new (link-wise) mutation operation to replace the original mutation operation of G1.…”

Section: /34mentioning

confidence: 99%

Multi-train trajectory optimisation to maximise rail network energy efficiency under travel-time constraints

Goodwin

Fletcher

Harrison

2015

Proceedings of the Institution of Mechanical Engineers, Part F:

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show abstract

“…[4][5][6] Moreover, various algorithms were proposed to determine the optimal switching strategy among different phases considering the real-world operation environments such as track gradient and speed limits, commercial punctuality constraints, and the uncertainty of some performance parameters. [7][8][9] Although the single-mass-point model makes the controller design much convenient, it overlooks the intrain dynamics which play essential roles when considering the operation safety for a long train. Research based on the cascade-mass-point model, therefore, has become a new trend in recent years.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Optimal operation of high-speed train based on fuzzy model predictive control

Wang

Tang

2017

Advances in Mechanical Engineering

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Focusing on exploring optimal control method of high-speed train, this research explicitly introduces a fuzzy predictive control framework to enhance safety, comfort, and energy-efficiency. The dynamic equation of the high-speed train is modeled as a cascade of cars connected by spring-like couplers and then represented by Takagi-Sugeno fuzzy model. Based on model predictive control approach, an effort is made on the design of fuzzy predictive controller such that each car tracks the target speed, and every coupler is stabilized to the equilibrium state as well as optimizing the performance function value at every time instant with regard to accelerating and braking constraints. Sufficient conditions for the existence of corresponding controller optimization problem are given in a set of linear matrix inequalities. Some simulations are performed to show the effectiveness of the model and the algorithm.

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“…Some new robust research studies are also presented in [28]. Yang et al [29] proposed a mathematical programming model, considering total energy consumption and total traversing time optimization in a railway network with multiple trains and multiple links in stations. An integrated GA and simulation are used to obtain an approximate optimal strategy.…”

Section: Introductionmentioning

confidence: 99%

Train Timetabling on double track and multiple station capacity railway with useful upper and lower bounds

Jadid

Eshghi

2017

Scientia Iranica

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Abstract. Train scheduling has been one of the signi cant issues in the railway industry in recent years since it has an important role in e cacy of railway infrastructure. In this paper, the timetabling problem of a multiple-tracked railway network is discussed. More speci cally, a general model is presented here in which a set of operational and safety requirements is considered. The model handles the trains overtaking in stations and considers the stations' capacity. The objective function is to minimize the total travel time. Unfortunately, the problem is NP-hard, and real-sized problems cannot be solved in an acceptable amount of time. In order to reduce the processing time, we presented some heuristic rules, which reduce the number of binary variables. These rules are based on problem's parameters, such as travel time, dwell time, and safety time of stations, and try to remove the impracticable areas of the solution space. Furthermore, a Lagrangian Relaxation algorithm model is presented in order to nd a lower bound. Finally, comprehensive numerical experiments on the Tehran Metro case are reported. Results show the e ciency of the heuristic rules and also the Lagrangian Relaxation method in a way that the optimum values are obtained for all analyzed problems.

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Optimizing trains movement on a railway network

Cited by 146 publications

References 21 publications

Multi-train trajectory optimisation to maximise rail network energy efficiency under travel-time constraints

Multi-train trajectory optimisation to maximise rail network energy efficiency under travel-time constraints

Optimal operation of high-speed train based on fuzzy model predictive control

Train Timetabling on double track and multiple station capacity railway with useful upper and lower bounds

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