Optimization of Train Operation in Multiple Interstations with Multi-Population Genetic Algorithm

Huang, Youneng; Ma, Xuelei; Su, Shuai; Tang, Tao

doi:10.3390/en81212433

Cited by 48 publications

(31 citation statements)

References 29 publications

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“…It is proved to be efficient in solving NP-hard optimization problems in the transportation field [25][26][27][28]. This method was also used by Lee et al [13] to find a solution to the A/B mode-based skipstop operation problem.…”

Section: Solution Approachmentioning

confidence: 99%

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Optimized Skip-Stop Metro Line Operation Using Smart Card Data

Peitong¹,

Sun²,

Liu³

2017

Journal of Advanced Transportation

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Skip-stop operation is a low cost approach to improving the efficiency of metro operation and passenger travel experience. This paper proposes a novel method to optimize the skip-stop scheme for bidirectional metro lines so that the average passenger travel time can be minimized. Different from the conventional "A/B" scheme, the proposed Flexible Skip-Stop Scheme (FSSS) can better accommodate spatially and temporally varied passenger demand. A genetic algorithm (GA) based approach is then developed to efficiently search for the optimal solution. A case study is conducted based on a real world bidirectional metro line in Shenzhen, China, using the time-dependent passenger demand extracted from smart card data. It is found that the optimized skip-stop operation is able to reduce the average passenger travel time and transit agencies may benefit from this scheme due to energy and operational cost savings. Analyses are made to evaluate the effects of that fact that certain number of passengers fail to board the right train (due to skip operation). Results show that FSSS always outperforms the all-stop scheme even when most passengers of the skipped OD pairs are confused and cannot get on the right train.

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Section: Solution Approachmentioning

confidence: 99%

“…As our study targets off-peak operation, was set to be 15 minutes. The time loss was calculated based on the reachable maximum speed max using formulation (26). Here is the maximum acceleration rate and is the maximum deceleration rate.…”

Section: Data Processing and Experiments Setupmentioning

confidence: 99%

Optimized Skip-Stop Metro Line Operation Using Smart Card Data

Peitong¹,

Sun²,

Liu³

2017

Journal of Advanced Transportation

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“…A large amount of previous studies [5,15,18,21,[53][54][55] have demonstrated that the energy-efficient driving strategies of each section will be maximum traction (MT), coasting (CO) and maximum braking (MB). Additionally, in each phase, train movement is as in Equation (18).…”

Section: Energy Consumption Calculationmentioning

confidence: 99%

“…Li and Lo [34] proposed an integrated energy-efficient operation model to jointly optimize the timetable and speed profile with minimum net energy consumption. Huang et al [21] proposes an energy-efficient approach to reduce the traction energy by optimizing the train operation for multiple sections, considering both the trip time and driving strategy. Although a set of work has been done with a comprehensive view, more realistic work is needed to apply the optimal approach into practice.…”

Section: Introductionmentioning

confidence: 99%

“…A recent emerging research interest is in the field of regenerative energy utilization [1][2][3][4][5][6], which focuses on developing a timetable including the dwelling time at stations and running time at sections (between two adjacent stations) in order to improve the utilization of regenerative energy by synchronizing the operations of accelerating and braking trains [7]. Compared with the upper level of timetable optimization, the lower level of energy-efficient control strategy design at sections has long attracted widespread attention [8][9][10][11][12][13][14][15][16][17][18][19][20][21] to calculate the speed profile with minimum tractive energy consumption under the timetable constraints [22]. The simulation-based method is commonly used to calculate the train traction energy consumption under complex track alignments [23][24][25][26][27] by energy-efficient driving [28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

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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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Mirror rearrangement optimization for uniform flux distribution on the cavity receiver of solar parabolic dish concentrator system

2018

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Summary The nonuniform and high‐gradient solar radiation flux on the absorber surface of solar dish concentrator/cavity receiver (SDCR) system will affect its operational reliability and service lifetime. Therefore, homogenization of the flux distribution is critical and important. In this paper, 2 mirror rearrangement strategies and its optimization method by combining a novel ray tracing method and the genetic algorithm are proposed to optimize the parabolic dish concentrator (PDC) so as to realize the uniform flux distribution on the absorber surface inside the cavity receiver of SDCR system. The mirror rearrangement strategy includes a mirror rotation strategy and mirror translation strategy, which rotate and translate (along the focal axis) each mirror unit of the PDC to achieve multipoint aiming, respectively. Firstly, a correlation model between the focus spot radius and mirror rearrangement parameters is derived as constraint model to optimize the PDC. Secondly, a novel method named motion accumulation ray‐tracing method is proposed to reduce the optical simulation time. The optical model by motion accumulation ray‐tracing method and optimization model of SDCR system are established in detailed, and then, an optimization program by combining a ray‐tracing code and genetic algorithm code in C++ is developed and verified. Finally, 3 typical cavity receivers, namely, cylindrical, conical, and spherical, are taken as examples to fully verify the effectiveness of these proposed methods. The results show that the optimized PDC by mirror rearrangement strategies can not only greatly improve the flux uniformity (ie, reduce the nonuniformity factor) and reduce the peak local concentration ratio of the absorber surface but also obtain excellent optical efficiency and direct useful energy ratio. A better optimization results when the PDC is optimized by mirror rotation strategy at aperture radius of 7.0 m, focal length of 6.00 m, and ring number of 6; the nonuniform factor of the cylindrical, conical, and spherical cavity receivers is greatly reduced from 0.63, 0.67, and 0.45 to 0.18, 0.17, and 0.26, respectively; the peak local concentration ratio is reduced from 1140.00, 1399.00, and 633.30 to 709.10, 794.00, and 505.90, respectively; and the optical efficiency of SDCR system is as high as 92.01%, 92.13%, and 92.71%, respectively. These results also show that the dish concentrator with same focal length can match different cavity receivers by mirror rearrangement and it can obtain excellent flux uniformity.

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Optimization of Train Operation in Multiple Interstations with Multi-Population Genetic Algorithm

Cited by 48 publications

References 29 publications

Optimized Skip-Stop Metro Line Operation Using Smart Card Data

Optimized Skip-Stop Metro Line Operation Using Smart Card Data

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

Mirror rearrangement optimization for uniform flux distribution on the cavity receiver of solar parabolic dish concentrator system

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