Skip to main content
SpringerLink
Log in

Path Optimization Method of Logistics Distribution Based on Mixed Multi-Intelligence Algorithms

  • Chapter
  • First Online:
Ecosystem Assessment and Fuzzy Systems Management

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 254))

  • 1715 Accesses

Abstract

In order to improve the efficiency and benefit for logistics distribution system, a path optimization method based on mixed multi-intelligence algorithms for vehicle routing problem was developed. In the process of proposed method, it firstly calculates the shortest paths between nodes of road network by Floyd algorithm and then obtains the merge distribution paths by means of saving method. Finally, genetic algorithm is employed to optimize the merge distribution paths. The experimental results show that the proposed method of logistics distribution can well solve the complex network conditions and lower the cost of logistics distribution, illustrating that the proposed model is practical and effective.

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. Deng, X., Wang, X., Ada, S.F.N., Lin, Y.: Analysis of the total factor productivity of China logistics enterprises. Syst. Eng. 26, 1–9 (2008)

    Google Scholar 

  2. Li, Y.: Research on Logistics Distribution Vehicle Scheduling Decision Method Based on Multi Agent. Central South University, Traffic and Transportation Engineering (2012)

    Google Scholar 

  3. Qi, C.: Application of improved discrete particle swarm optimization in logistics distribution routing problem. Procedia Eng. 15, 3673–3677 (2011)

    Article  Google Scholar 

  4. Lang, M., Hu, S.: Study on optimization of logistics distribution routing problem with hybrid genetic algorithm. Chin. J. Manage. Sci. 51–56 (2012)

    Google Scholar 

  5. Asgharzadeh, A., Valiollahi, R., Raqab, M.Z.: Estimation of the stress–strength reliability for the generalized logistic distribution. Stat. Methodol. 15(1), 73–94 (2013)

    MathSciNet  Google Scholar 

  6. Hossain, M., Wright, S., Petersen, L.A.: Comparing performance of multinomial logistic regression and discriminant analysis for monitoring access to care for acute myocardial infarction. J. Clin. Epidemiol. 55(4), 400–406 (2012)

    Article  Google Scholar 

  7. Perrier, N., Agard, B., Baptiste, P.: A survey of models and algorithms for emergency response logistics in electric distribution systems, Part I: Reliability planning with fault considerations. Comput. Oper. Res. 40(7), 1895–1906 (2013)

    Article  Google Scholar 

  8. Balakrishnan, N., Saleh, H.M.: Relations for moments of progressively type-II censored order statistics from half-logistics distribution with applications to inference. Comput. Stat. Data Anal. 55(10), 2775–2792 (2011)

    Article  MATH  MathSciNet  Google Scholar 

  9. Nassar, M.M., Elmasry, A.: A study of generalized logistics distributions. J. Egypt. Math. Soc. 20(2), 126–133 (2012)

    Article  MATH  MathSciNet  Google Scholar 

Download references

Acknowledgments

This work is supported by International Science & Technology Cooperation Program of China (2012DFA11270); Hainan International Cooperation Key Project (GJXM201105); Hainan Provincial Natural Science Fundunder (612120, 612126); and National Natural Science Fund (No. 61362016).

Author information

Authors and Affiliations

  1. Center of Network, Hainan Normal University, Haikou, 571158, China

    Rui-qiong Zhou

  2. College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China

    Jun-kuo Cao

Authors
  1. Rui-qiong Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jun-kuo Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jun-kuo Cao .

Editor information

Editors and Affiliations

  1. School of Mathematics and Information Science, Key Laboratory of Mathematics and Interdisciplinary Sciences of Guangdong Higher Education Institutes, Guangzhou University, Guangzhou, People's Republic of China

    Bing-Yuan Cao

  2. School of Information Science and Technology, Hainan Normal University, Haikou, People's Republic of China

    Sheng-Quan Ma

  3. Department of Geography, University of Ottawa, Ottawa, Ontario, Canada

    Hu-hua Cao

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Zhou, Rq., Cao, Jk. (2014). Path Optimization Method of Logistics Distribution Based on Mixed Multi-Intelligence Algorithms. In: Cao, BY., Ma, SQ., Cao, Hh. (eds) Ecosystem Assessment and Fuzzy Systems Management. Advances in Intelligent Systems and Computing, vol 254. Springer, Cham. https://doi.org/10.1007/978-3-319-03449-2_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-03449-2_25

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03448-5

  • Online ISBN: 978-3-319-03449-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation