Skip to main content
SpringerLink
Log in

A Method Based on SNSO for Solving Slot Planning Problem of Container Vessel Bays

  • Conference paper
  • First Online:
Internet and Distributed Computing Systems (IDCS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9864))

Included in the following conference series:

  • 1617 Accesses

Abstract

Stowage planning has an important effect in container shipping and is also a hard combinatorial problem. In order to improve the operation efficiency and reduce the cost, a new optimization method called Social Network-based Swarm Optimization Algorithm (SNSO) is applied to solve the slot planning problem of container vessel bays. As a swarm intelligence optimization algorithm, SNSO is designed with considering population topology, neighborhood and individual behavior comprehensively to improve the swarm search ability. An effective coding and decoding strategy is proposed to optimize the slot planning problem for using SNSO. Finally, fourteen cases of slot planning with different scales are selected to test the proposed algorithm and five swarm intelligence algorithms are selected for comparison in the experiment. The results show that the SNSO has a better performance on solving stowage plan problem in the terms of convergence and accuracy than other selected algorithms.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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. Delgado, A., Jensen, R., Janstrup, K., et al.: A constraint programming model for fast optimal stowage of container vessel bays. Eur. J. Oper. Res. 220(1), 251–261 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  2. Pacino, D., Jensen, R.: Fast slot planning using constraint-based local search. In: Yang, G.-C., Ao, S.-I., Huang, X., Castillo, O. (eds.) IAENG Transaction Engineering Technologies, pp. 49–63. Springer, New York (2013)

    Chapter  Google Scholar 

  3. Liang, X., Li, W., Liu, P.P., et al.: Social network-based swarm optimization algorithm. In: Proceedings of the 2015 IEEE 12th International Conference Networking, Sensing and Control (ICNSC), pp. 360–365 (2015)

    Google Scholar 

  4. Liang, J., Qin, A., Suganthan, P.N., et al.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans. Evol. Comput. 10(3), 281–295 (2006)

    Article  Google Scholar 

  5. Karaboga, D., Akay, B.: A comparative study of Artificial Bee Colony algorithm. Appl. Math. Comput. 214(1), 108–132 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  6. Yang, X., Deb, S.: Engineering optimisation by cuckoo search. Int. J. Math. Model. Numer. Optim. 1(4), 330–343 (2010)

    MATH  Google Scholar 

  7. Yang, X., Deb, S.: Cuckoo search via Lévy flights. In: World Congress on Nature & Biologically Inspired Computing, pp. 210–214 (2009)

    Google Scholar 

  8. Passino, K.M.: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Syst. 22(3), 52–67 (2002)

    Article  MathSciNet  Google Scholar 

  9. Mirjalili, S., Mirjalili, S.M., Yang, X.S.: Binary bat algorithm. Neural Comput. Appl. 25(3–4), 663–681 (2014)

    Article  Google Scholar 

  10. Yang, X., Gandomi, A.H.: Bat algorithm: a novel approach for global engineering optimization. Eng. Comput. 29(5), 464–483 (2012)

    Article  Google Scholar 

  11. Yang, X.: A new metaheuristic bat-inspired algorithm. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) Nature inspired cooperative strategies for optimization, 65-74. Springer, Berlin (2010)

    Google Scholar 

  12. Liang, X., Li, W., Zhang, Y.: A novel swarm intelligence optimization algorithm for solving constrained multimodal transportation planning. J. Shanghai Jiaotong Univ. (Sci.) 49(8), 1220–1229 (2015)

    MathSciNet  Google Scholar 

  13. Liang, X., Li, W., Zhang, Y., Zhou, M.: An adaptive particle swarm optimization method based on clustering. Soft. Comput. 19(2), 431–448 (2015)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by National Natural Science Foundation of China (No. 61304210, No.61571336) and Foundation of WUST Fund for Young Teachers (No. 2016xz029).

Author information

Authors and Affiliations

  1. School of Automobile and Traffic Engineering, WUST, Wuhan, China

    Xiaolei Liang

  2. School of Transportation, Fujian University of Technology, Fuzhou, China

    Bin Li

  3. School of Logistics Engineering, WUT, Wuhan, China

    Wenfeng Li, Yu Zhang & Lin Yang

Authors
  1. Xiaolei Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wenfeng Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lin Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Xiaolei Liang or Bin Li .

Editor information

Editors and Affiliations

  1. Wuhan University of Technology , Wuhan, China

    Wenfeng Li

  2. Central Queensland University , North Rockhampton, Queensland, Australia

    Shawkat Ali

  3. Delft University of Technology , Delft, The Netherlands

    Gabriel Lodewijks

  4. University of Calabria , Rende (CS), Italy

    Giancarlo Fortino

  5. University of Reading , Reading, United Kingdom

    Giuseppe Di Fatta

  6. Huazhong University of Science and Technology, Wuhan, China

    Zhouping Yin

  7. CSIRO ICT , Acton, Australia

    Mukaddim Pathan

  8. ICAR-CNR, Rende (CS), Italy

    Antonio Guerrieri

  9. Wuhan University of Technology , Wuhan, China

    Qiang Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Liang, X., Li, B., Li, W., Zhang, Y., Yang, L. (2016). A Method Based on SNSO for Solving Slot Planning Problem of Container Vessel Bays. In: Li, W., et al. Internet and Distributed Computing Systems. IDCS 2016. Lecture Notes in Computer Science(), vol 9864. Springer, Cham. https://doi.org/10.1007/978-3-319-45940-0_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-45940-0_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45939-4

  • Online ISBN: 978-3-319-45940-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation