Distributed search in railway scheduling problems

Abril, M.; Salido, Miguel Á.; Barber, Federico

doi:10.1016/j.engappai.2008.03.008

Cited by 26 publications

(6 citation statements)

References 11 publications

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“…As a consequence, new strategies are being implemented to reduce energy consumption. Some of these efficiency strategies are focused on improving the traffic management from the point of view of traffic design [2]- [6] and regulation [7]- [12], on the optimal use of regenerative braking [13]- [15] and on eco-driving [16]- [18]. These actions can be implemented in the short term with low investments compared with other energy efficiency actions as the renewal of trains [19] or the improvement of the infrastructure.…”

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confidence: 99%

Design of Robust and Energy-Efficient ATO Speed Profiles of Metropolitan Lines Considering Train Load Variations and Delays

Fernández-Rodríguez

Fernández-Cardador

Cucala

et al. 2015

IEEE Trans. Intell. Transport. Syst.

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Metropolitan railway operators' strategic plans include nowadays actions to reduce energy consumption. The application of ecodriving initiatives in lines equipped with automatic train operation (ATO) systems can provide important savings with low investments. Previous studies carried out under the ATO framework have not considered the main uncertainties in the traffic operation: the train load and delays in the line. This paper proposes a method to design robust and efficient speed profiles to be programmed in the ATO equipment of a metro line. First, an optimal Pareto front for ATO speed profiles that are robust to changes in train load is constructed. There are two objectives: running time and energy consumption. A robust optimization technique and an alternative method based on the conservation of the shape of the speed profiles (pattern robustness) are compared. Both procedures make use of a multi objective particle swarm optimization algorithm. Then, the set of speed profiles to be programmed in the ATO equipment is selected from the robust Pareto front by means of an optimization model. This model is a particle swarm optimization algorithm (PSO) to minimize the total energy consumption considering the statistical information about delays in the line. This procedure has been applied to a case study. The results showed that the pattern robustness is more restrictive and meaningful than the robust optimization technique as it provides information about shapes that are more comfortable for passengers. In addition, the use of statistical information about delays provides additional energy savings between 3% and 14%.Index Terms-Communication-based train control (CBTC), energy saving, multi objective particle swarm optimization (MOPSO), subway systems, train load variations, train operation, uncertainty.

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confidence: 99%

Design of Robust and Energy-Efficient ATO Speed Profiles of Metropolitan Lines Considering Train Load Variations and Delays

Fernández-Rodríguez

Fernández-Cardador

Cucala

et al. 2015

IEEE Trans. Intell. Transport. Syst.

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“…Abril et al. (2008) took railway scheduling problems as constraint satisfaction problems (CSPS) and designed a distributed and asynchronous search algorithm (DTS) to solve the problems [3]. Castillo et al.…”

Section: Related Workmentioning

confidence: 99%

“…Tornquist and Persson (2007) presented an optimization approach to the problem of rescheduling railway traffic in an n-tracked network when a disturbance had occurred [2]. Abril et al (2008) took railway scheduling problems as constraint satisfaction problems (CSPS) and designed a distributed and asynchronous search algorithm (DTS) to solve the problems [3]. Castillo et al (2011) studied the timetabling problem for a mixed multiple-and single-tracked railway network [4].…”

Section: Related Workmentioning

confidence: 99%

An integrated model for train rescheduling and station track assignment

Meng

Wang

Xiang

et al. 2020

IET Intelligent Trans Sys

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Both train rescheduling and station track assignment have become hot topics in recent years. It is fundamentally important to do the rescheduling and track assignment work at the same time to avoid the feasibility risk of the re‐scheduled timetable. The purpose of this paper is to design an integrated model for train rescheduling and track assignment in order to provide an integrative plan for the trains to run on the railway sections and go through stations. Based on the existing train rescheduling model, the model is designed by adding the constraints and the optimization goal of track assignment. The goal of track assignment is to maximize the equilibrium of the track usage time, and the constraint is that two trains cannot occupy a same track at the same time. An artificial bee colony algorithm is used to solve the model to get the operation plan. A computing experiment was carried out to prove the effectiveness of the model and the efficiency of the algorithm. The approach presented in this paper can provide a reference for the developers of a railway dispatching system.

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“…Eco-driving is the obtention of the speed profile with the minimum energy consumption associated. The eco-driving speed profile is calculated for a specific target running time considering the previously designed timetable [3,[5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Eco-Driving in Railway Lines Considering the Uncertainty Associated with Climatological Conditions

et al. 2022

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Eco-driving is a keystone in energy reduction in railways and a fundamental tool to contribute to the Sustainable Development Goals in the transport sector. However, its results in real applications are subject to uncertainties such as climatological factors that are not considered in the train driving optimisation. This paper aims to develop an eco-driving model to design efficient driving commands considering the uncertainty of climatological conditions. This uncertainty in temperature, pressure, and wind is modelled by means of fuzzy numbers, and the optimisation problem is solved using a Genetic Algorithm with fuzzy parameters making use of an accurate railway simulator. It has been applied to a realistic Spanish high-speed railway scenario, proving the importance of considering the uncertainty of climatological parameters to adapt driving commands to them. The results obtained show that the energy savings expected without considering climatological factors account for 29.76%, but if they are considered, savings can rise up to 34.7% in summer conditions. With the proposed model, a variation in energy of 5.32% is obtained when summer and winter scenarios are compared while punctuality constraints are fulfiled. In conclusion, the model allows the operator to estimate better energy by obtaining optimised driving adapted to the climate.

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Distributed search in railway scheduling problems

Cited by 26 publications

References 11 publications

Design of Robust and Energy-Efficient ATO Speed Profiles of Metropolitan Lines Considering Train Load Variations and Delays

Design of Robust and Energy-Efficient ATO Speed Profiles of Metropolitan Lines Considering Train Load Variations and Delays

An integrated model for train rescheduling and station track assignment

Eco-Driving in Railway Lines Considering the Uncertainty Associated with Climatological Conditions

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