Skip to main content
SpringerLink
Log in
Book cover

The International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery

ICNC-FSKD 2019: Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery pp 123–131Cite as

A Model and an Algorithm for Empty Car Distribution in Railway Transportation

  • Conference paper
  • First Online:

  • 1291 Accesses

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1074))

Abstract

The empty car distribution is an important part of technical plan for railway transportation and can deal with the unbalanced distribution of empty car reasonably. So, taken the uncertainty of freight supply and demand into account, a fuzzy empty car distribution model is established in the aim of minimizing the total empty car running kilometers. Furthermore, according to the characteristic of this problem, a two-phase genetic algorithm (GA) is put on emphases to propose based on simulation. Since different objective values can be obtained in different conditions rather than a crisp value, more information is provided for making adjustment measures. Therefore, the model and algorithm have theoretical value and practical value.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Liang, D., Lin, B.L., Yan, H.X., Li, J.W.: Study of railway empty car distribution with substitution of empty car types. J. China Railw. Soc. 27(4), 1–5 (2005)

    Google Scholar 

  2. Li, Z.P., Xia, J.F.: Time restraint-based model and algorithm for railway empty wagon distribution. J. Southwest Jiaotong Univ. 40(3), 361–365 (2005)

    Google Scholar 

  3. Lei, Z.L., He, S.W., Song, R., Cai, J.: Stochastic chance-constrained model and genetic algorithm for empty car distribution in railway transportation. J. China Railw. Soc. 27(5), 1–5 (2005)

    Google Scholar 

  4. Zhang, X., Zhang, Q.S.: Study on the real-coded genetic algorithm of the empty wagons flow assignment. Syst. Eng.-Theory Pract. 26(11), 130–138 (2006)

    Google Scholar 

  5. Wang, D., Yan, H.F., Tan, Y.J.: Ant Colony Algorithm for the adjustment of empty refrigerated wagons between railway network nodes. China Railw. Sci. 29(2), 131–135 (2008)

    Google Scholar 

  6. Cao, X.M., Wang, X.F., Lin, B.L.: Collaborative optimization model of the loaded & empty car flow routing and the empty car distribution of multiple car types. China Railw. Sci. 30(6), 114–118 (2009)

    Google Scholar 

  7. Wu, K.J., Lu, H.W., Wang, T.J.: Railway empty car allocation based on adaptive mutation particle swarm algorithm. J. Lanzhou Univ. Technol. 37(2), 102–105 (2011)

    Google Scholar 

  8. Jing, Y., He, S.W., Song, R., Li, H.D.: Dynamic empty car scheduling optimization based on immune clonal with cloud preference. J. Jilin Univ. 42(1), 261–265 (2012)

    Google Scholar 

  9. Liu, X., Zhu, X.N.: Study on distribution of railway empty cars resource. Railw. Transp. Econ. 36(10), 48–51, 69 (2012)

    Google Scholar 

  10. Zhao, C., Yang, L.X., Li, S.K.: Allocating freight empty cars in railway networks with dynamic demands. Discrete Dyn. Nat. Soc. 2014(3), 1–12 (2014)

    Google Scholar 

  11. Zhang, J., Hao, Y., Wei, Y., Wang, Y.G.: The empty wagons adjustment algorithm of Chinese heavy-haul railway. Chaos, Solitons Fractals 89, 91–99 (2016)

    Article  Google Scholar 

  12. Wang, L., Ma, J.J., Lin, B.L., Li, J., Zhang, J., Ni, S.Q.: Dynamic empty car distribution for the whole rail system and calculation for empty car flow discharged over network boundaries. J. China Railw. Soc. 37(6), 1–9 (2015)

    Google Scholar 

  13. Liu, S.T., Kao, C.: Solving fuzzy transportation problems based on extension principle. Eur. J. Oper. Res. 153(3), 661–674 (2004)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgement

This work is supported by the National Natural Science Foundation of China (No. 71761024 and No. 71671079), and the Science and Technology Development Project Plan for 2019 of Lanzhou Bureau Group Company (No. KY201978). The authors wish to thank anonymous referees and the editor for their comments and suggestions.

Author information

Authors and Affiliations

  1. School of Traffic and Transportation Engineering, Lanzhou Jiaotong University, Gansu, 730070, Lanzhou, China

    Lanfen Liu & Xinfeng Yang

Authors
  1. Lanfen Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xinfeng Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xinfeng Yang .

Editor information

Editors and Affiliations

  1. School of Computer Science and Engineering, The University of Aizu, Aizu-Wakamatsu, Fukushima, Japan

    Yong Liu

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Lipo Wang

  3. Computer Science and Mathematics, University of Sao Paulo, Ribeirao Preto, Brazil

    Liang Zhao

  4. School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China

    Zhengtao Yu

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Liu, L., Yang, X. (2020). A Model and an Algorithm for Empty Car Distribution in Railway Transportation. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1074. Springer, Cham. https://doi.org/10.1007/978-3-030-32456-8_13

Download citation

Publish with us

Policies and ethics

Search

Navigation