Skip to main content
SpringerLink
Log in

A New Method for the Minimum Concave Cost Transportation Problem in Smart Transportation

  • Conference paper
  • First Online:
Advancements in Smart City and Intelligent Building (ICSCIB 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 890 ))

Included in the following conference series:

  • 1220 Accesses

Abstract

The minimum concave cost transportation problem is the benchmark problem in numerical computing and it has been used widely in the schedule of smart transportation. In this paper, a deterministic annealing neural network algorithm is proposed to solve the minimum concave cost transportation problem. The algorithm is derived from two neural network models and Lagrange-barrier functions. The Lagrange function is used to handle linear equality constraints and the barrier function is used to force the solution to move to the global or near-global optimal solution. The computer simulations on test problem are made and the results indicate that the proposed algorithm always generates global or near global optimal solutions.

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

Access this chapter

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 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
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

Institutional subscriptions

References

  1. Erickson, R.E., Monma, C.L., Veinott Jr., A.L.: Send-and-split method for minimum-concave-cost network flows. Math. Oper. Res. 12, 634–664 (1987)

    Article  MathSciNet  Google Scholar 

  2. Fang, S.C., Tsao, H.-S.J.: Linearly-constrained entropy maximization problem with quadratic cost and its applications to transportation planning problems. Transp. Sci. 29, 353–365 (1995)

    Article  MathSciNet  Google Scholar 

  3. Gallo, G., Sodini, C.: Concave cost minimization on networks. Eur. J. Oper. Res. 3, 239–249 (1979)

    Article  MathSciNet  Google Scholar 

  4. Pardalos, P.M., Rosen, J.B.: Constrained global optimization: algorithms and applications. In: Lecture Notes in Computer Science, vol. 268. Springer (1987)

    Google Scholar 

  5. Zangwill, W.I.: Minimum concave cost flows in certain networks. Manag. Sci. 14, 429–450 (1968)

    Article  MathSciNet  Google Scholar 

  6. Guisewite, G.M.: Network problems. In: Horst, R., Pardalos, P.M. (eds.) Handbook of Global Optimization. Kluwer Academic Publishers (1995)

    Google Scholar 

  7. Hopfield, J.J., Tank, D.W.: Neural computation of decisions in optimization problems. Biol. Cybern. 52, 141–152 (1985)

    MATH  Google Scholar 

  8. Liu, Q., Dang, C., Cao, J.: A novel recurrent neural network with one neuron and finite-time convergence for k-winners-take-all operation. IEEE Trans. Neural Netw. 21(7), 1140–1148 (2010)

    Article  Google Scholar 

  9. Hu, X., Zhang, B.: A new recurrent neural network for solving convex quadratic programming problems with an application to the k-winners-take-all problem. IEEE Trans. Neural Netw. 20(4), 654–664 (2009)

    Google Scholar 

  10. Hu, X., Wang, J.: An improved dual neural network for solving a class of quadratic programming problems and its k-winners-take-all application. IEEE Trans. Neural Netw. 19(12), 2022–2031 (2008)

    Article  Google Scholar 

  11. Lau, K.M., Chan, S.M., Xu, L.: Comparison of the Hopfield scheme to the hybrid of Lagrange and transformation approaches for solving the traveling salesman problem. In: Proceedings of International IEEE Symposium on Intelligence in Neural and Biological Systems, Hendon, pp. 209–217 (1995)

    Google Scholar 

  12. Xu, L.: Combinatorial optimization neural nets based on a hybrid of Lagrange and transformation approaches. In: Proceedings of World Congress on Neural Networks, San Diego, pp. 399–404 (1994)

    Google Scholar 

  13. Xu, L.: On the hybrid LT combinatorial optimization: new U-shape barrier, sigmoid activation, least leaking energy and maximum entropy. In: Proceedings of International Conference on Neural Information Processing (ICONIP’95), pp. 309–312. Publishing House of Electronics Industry, Beijing (1995)

    Google Scholar 

  14. Floudas, C.A., Pardalos, P.M.: A collection of test problems for constrained global optimization algorithms. In: Lecture Notes in Computer Science, vol. 455. Springer (1990)

    Google Scholar 

  15. Wu, Z., Li, B., Dang, C., Hu, F., Zhu, Q., Fu, B.: Solving long haul airline disruption problem caused by groundings using a distributed fixed-point computational approach to integer programming. Neurocomputing 269, 232–255 (2017)

    Google Scholar 

  16. Wu, Z., Li, B., Dang, C.: Solving multiple fleet airline disruption problems using a distributed-computation approach to integer programming. IEEE Access 5, 19116–19131 (2017)

    Article  Google Scholar 

  17. Wu, Z., Gao, Q., Li, B., Dang, C., Hu, F.: A rapid solving method to large airline disruption problems caused by airports closure. IEEE Access 5, 26545–26555 (2017)

    Google Scholar 

Download references

Acknowledgements

This work was partially supported by NSFC under Grant No. 61672371, GRF: CityU 11302715 of Hong Kong SAR Government, Jiangsu Provincial Department of Housing and Urban–Rural Development under grants No. 2017ZD253, Ministry of housing and urban and rural construction under grants No. 2018-K1-007, the grant from Suzhou University of Science and Technology under grants No. XKZ2017011, and China scholarship council.

Author information

Authors and Affiliations

  1. Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, China

    Chuan Li & Jinjin Zheng

  2. Department of Systems Engineering and Engineering Management, City University of Hong Kong, Kowloon Tong, Hong Kong, China

    Chuan Li & Chuangyin Dang

  3. School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, China

    Zhengtian Wu & Baochuan Fu

Authors
  1. Chuan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhengtian Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Baochuan Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chuangyin Dang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jinjin Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhengtian Wu .

Editor information

Editors and Affiliations

  1. Anhui Jianzhu University, Hefei, China

    Qiansheng Fang

  2. University of the West of England, Bristol, UK

    Quanmin Zhu

  3. Shenyang Jianzhu University, Shenyang, China

    Feng Qiao

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Li, C., Wu, Z., Fu, B., Dang, C., Zheng, J. (2019). A New Method for the Minimum Concave Cost Transportation Problem in Smart Transportation. In: Fang, Q., Zhu, Q., Qiao, F. (eds) Advancements in Smart City and Intelligent Building. ICSCIB 2018. Advances in Intelligent Systems and Computing, vol 890 . Springer, Singapore. https://doi.org/10.1007/978-981-13-6733-5_34

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-6733-5_34

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-6732-8

  • Online ISBN: 978-981-13-6733-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation