Skip to main content
SpringerLink
Log in

Research Optimization on Logistic Distribution Center Location Based on Improved Harmony Search Algorithm

  • Conference paper
  • First Online:
Book cover Advances in Swarm Intelligence (ICSI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10941))

Included in the following conference series:

Abstract

Logistics distribution center are important logistics nodes and the choice of locations are critical management decisions. This study addresses a logistics distribution center location problem that aims at determining the location and allocation of the distribution centers. Considering the characteristic and complexity of problem, we propose an improved harmony search algorithm, in which we employ a novel way of improvising new harmony. The improved algorithm is compared with genetic algorithm, particle swarm optimization, generalized particle swarm optimization, and classical harmony search algorithm in solving a simulated distribution center location problem. Experiment results show that the improved algorithm can solve the logistics distribution center problem with more stable convergence speed and higher accuracy.

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. Gu, W., Foster, K., Shang, J.: Enhancing market service and enterprise operations through a large-scale GIS-based distribution system. Expert Syst. Appl. 55, 157–171 (2016)

    Article  Google Scholar 

  2. Yang, L., et al.: Logistics distribution centers location problem and algorithm under fuzzy environment. J. Comput. Appl. Math. 208(2), 303–315 (2007)

    Article  MathSciNet  Google Scholar 

  3. Zhang, S., et al.: Swarm intelligence applied in green logistics: a literature review. Eng. Appl. Artif. Intell. 37, 154–169 (2015)

    Article  Google Scholar 

  4. Jayaram, J., Avittathur, B.: Green supply chains: a perspective from an emerging economy. Int. J. Prod. Econ. 164, 234–244 (2015)

    Article  Google Scholar 

  5. Tu, C.-S., et al.: Applying an AHP -QFD conceptual model and zero-one goal programming to requirement-based site selection for an airport cargo logistics center. Int. J. Inf. Manag. Sci. 21(4), 407–430 (2010)

    MATH  Google Scholar 

  6. Kuo, M.S.: Optimal location selection for an international distribution center by using a new hybrid method. Expert Syst. Appl. 38(6), 7208–7221 (2011)

    Article  Google Scholar 

  7. Esnaf, S., Kucukdeniz, T.: A fuzzy clustering-based hybrid method for a multi-facility location problem. J. Intell. Manuf. 20(2), 259–265 (2009)

    Article  Google Scholar 

  8. Zhou, Y., Peng, F., Wang, G.: A study on the dynamic characteristics of the drive at center of gravity (DCG) feed drives. Int. J. Adv. Manuf. Technol. 66, 325–336 (2013)

    Article  Google Scholar 

  9. Manzini, R., Gamberi, M., Regattieri, A.: Applying mixed integer programming to the design of a distribution logistic network. Int. J. Ind. Eng. Theory Appl. Pract. 13(2), 207–218 (2006)

    Google Scholar 

  10. Wen-Jun, F.U., et al.: Application of improved genetic algorithm in logistics distribution. J. Yanan Univ. 33(1), 19–21 (2014)

    Google Scholar 

  11. Hua, X., Hu, X., Yuan, W.: Research optimization on logistics distribution center location based on adaptive particle swarm algorithm. Optik – Int. J. Light Electron Opt. 127(20), 8443–8450 (2016)

    Article  Google Scholar 

  12. Zini, H., Elbernoussi, S.: Minimizing makespan in hybrid flow shop scheduling with multiprocessor task problems using a discrete harmony search. In: IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications. IEEE, pp. 177–180 (2017)

    Google Scholar 

  13. Zong, W.G., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: harmony search. Simul. Trans. Soc. Model. Simul. Int. 76(2), 60–68 (2016)

    Google Scholar 

  14. Garcíagonzalo, E., Fernándezmartínez, J.L.: A brief historical review of particle swarm optimization (PSO). J. Bioinform. Intell. Control. 1(1), 3–16 (2012)

    Google Scholar 

Download references

Acknowledgement

This work is supported by the Guangdong Provincial Science and Technology Plan Project (No. 2013B040403005) and Youth Creative Talents Project (2015KQNCX138).

Author information

Authors and Affiliations

  1. Shenzhen University, Shenzhen, China

    Xiaobing Gan, Entao Jiang, Shuang Geng & Mijat Kustudic

  2. Lancaster University, Lancaster, UK

    Yingying Peng

Authors
  1. Xiaobing Gan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Entao Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yingying Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shuang Geng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mijat Kustudic
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shuang Geng .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  3. Tongji University, Shanghai, China

    Qirong Tang

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Gan, X., Jiang, E., Peng, Y., Geng, S., Kustudic, M. (2018). Research Optimization on Logistic Distribution Center Location Based on Improved Harmony Search Algorithm. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10941. Springer, Cham. https://doi.org/10.1007/978-3-319-93815-8_39

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-93815-8_39

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93814-1

  • Online ISBN: 978-3-319-93815-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation