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

Author

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  • Youneng Huang

    (School of Electronics and Information Engineering, Beijing Jiaotong University, Haidian District, Beijing 100044, China
    National Engineering Research Center of Rail Transportation Operation and Control System, Beijing Jiaotong University, Haidian District, Beijing 100044, China
    Beijing Lab of Rail Traffic, Beijing Jiaotong University, Haidian District, Beijing 100044, China)

  • Xiao Ma

    (School of Electronics and Information Engineering, Beijing Jiaotong University, Haidian District, Beijing 100044, China
    National Engineering Research Center of Rail Transportation Operation and Control System, Beijing Jiaotong University, Haidian District, Beijing 100044, China)

  • Shuai Su

    (School of Electronics and Information Engineering, Beijing Jiaotong University, Haidian District, Beijing 100044, China
    National Engineering Research Center of Rail Transportation Operation and Control System, Beijing Jiaotong University, Haidian District, Beijing 100044, China)

  • Tao Tang

    (State Key Lab of Rail Traffic Control and Safety, Beijing Jiaotong University, Haidian District, Beijing 100044, China
    Beijing Lab of Rail Traffic, Beijing Jiaotong University, Haidian District, Beijing 100044, China)

Abstract

Subway systems consume a large amount of energy each year. How to reduce the energy consumption of subway systems has already become an issue of concern in recent years. This paper proposes an energy-efficient approach to reduce the traction energy by optimizing the train operation for multiple interstations. Both the trip time and driving strategy are considered in the proposed optimization approach. Firstly, a bi-level programming model of multiple interstations is developed for the energy-efficient train operation problem, which is then converted into an integrated model to calculate the driving strategy for multiple interstations. Additionally, the multi-population genetic algorithm (MPGA) is used to solve the problem, followed by calculating the energy-efficient trip times. Finally, the paper presents some examples based on the operation data of the Beijing Changping subway line. The simulation results show that the proposed approach presents a better energy-efficient performance than that with only optimizing the driving strategy for a single interstation.

Suggested Citation

  • Youneng Huang & Xiao Ma & Shuai Su & Tao Tang, 2015. "Optimization of Train Operation in Multiple Interstations with Multi-Population Genetic Algorithm," Energies, MDPI, vol. 8(12), pages 1-19, December.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:12:p:12433-14329:d:60839
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    References listed on IDEAS

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    1. Ghoseiri, Keivan & Szidarovszky, Ferenc & Asgharpour, Mohammad Jawad, 2004. "A multi-objective train scheduling model and solution," Transportation Research Part B: Methodological, Elsevier, vol. 38(10), pages 927-952, December.
    2. Cheng Gong & Shiwen Zhang & Feng Zhang & Jianguo Jiang & Xinheng Wang, 2014. "An Integrated Energy-Efficient Operation Methodology for Metro Systems Based on a Real Case of Shanghai Metro Line One," Energies, MDPI, vol. 7(11), pages 1-25, November.
    3. Liu, Rongfang (Rachel) & Golovitcher, Iakov M., 2003. "Energy-efficient operation of rail vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 37(10), pages 917-932, December.
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    Cited by:

    1. Agostinho Rocha & Armando Araújo & Adriano Carvalho & João Sepulveda, 2018. "A New Approach for Real Time Train Energy Efficiency Optimization," Energies, MDPI, vol. 11(10), pages 1-21, October.
    2. Youneng Huang & Chen Yang & Shaofeng Gong, 2016. "Energy Optimization for Train Operation Based on an Improved Ant Colony Optimization Methodology," Energies, MDPI, vol. 9(8), pages 1-18, August.
    3. Jie Yang & Limin Jia & Shaofeng Lu & Yunxiao Fu & Ji Ge, 2016. "Energy-Efficient Speed Profile Approximation: An Optimal Switching Region-Based Approach with Adaptive Resolution," Energies, MDPI, vol. 9(10), pages 1-27, September.
    4. Manuel Blanco-Castillo & Adrián Fernández-Rodríguez & Antonio Fernández-Cardador & Asunción P. Cucala, 2022. "Eco-Driving in Railway Lines Considering the Uncertainty Associated with Climatological Conditions," Sustainability, MDPI, vol. 14(14), pages 1-26, July.
    5. Alejandro Cunillera & Adrián Fernández-Rodríguez & Asunción P. Cucala & Antonio Fernández-Cardador & Maria Carmen Falvo, 2020. "Assessment of the Worthwhileness of Efficient Driving in Railway Systems with High-Receptivity Power Supplies," Energies, MDPI, vol. 13(7), pages 1-24, April.
    6. Xuan Lin & Qingyuan Wang & Pengling Wang & Pengfei Sun & Xiaoyun Feng, 2017. "The Energy-Efficient Operation Problem of a Freight Train Considering Long-Distance Steep Downhill Sections," Energies, MDPI, vol. 10(6), pages 1-26, June.

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