Skip to main content
SpringerLink
Log in

Logistics automation control based on machine learning algorithm

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

In order to improve the logistics problem, taking automated logistics system as research platform, a new optimization algorithm is proposed for the route planning of multi-goods picking operation of stacker in stereoscopic warehouse. First, the hardware composition of the automated logistics system is introduced, and then the characteristics of the picking operation of the stacker are deeply analyzed. According to these characteristics, a mathematical model for the time cost of the sorting operation is set up. Various algorithms for solving the problem are analyzed and compared. Aiming at the advantages and disadvantages of ant colony system and parthenogenetic algorithm, the two algorithms are properly improved and fused, and a new improved algorithm—parthenogenetic ant colony algorithm is proposed. The validity is verified by the simulation experiment. The simulation is carried out in the Matlab environment, and the satisfactory optimization results are obtained. The simulation result shows that the algorithm is used to optimize the picking path of the stacker. Therefore, it is concluded that the parthenogenetic algorithm greatly reduces the time of the picking operation, and greatly improves the efficiency.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Zhu, Z., Xu, B., Brunner, C., et al.: Distributed topology processing solution for distributed controls in distribution automation systems. IET Gener. Transm. Distrib. 11(3), 776–784 (2017)

    Article  Google Scholar 

  2. Fereidunian, A., Hosseini, M.M., Talabari, M.A.: Toward self-financed distribution automation development: time allocation of automatic switches installation in electricity distribution systems. IET Gener. Transm. Distrib. 11(13), 3350–3358 (2017)

    Article  Google Scholar 

  3. Dede, A., Giustina, D.D., Massa, G., et al.: Toward a new standard for secondary substations: the viewpoint of a distribution utility. IEEE Transacti. Power Deliv. 99, 1 (2017)

    Google Scholar 

  4. Kisseleff, S., Akyildiz, I.F., Gerstacker, W.H.: magnetic induction-based simultaneous wireless information and power transfer for single information and multiple power receivers. IEEE Transact. Commun. 65(3), 1396–1410 (2017)

    Article  Google Scholar 

  5. Ebben, M.J.R.: Logistic control in automated transportation networks. Prod. Eng. 5(4), 373–382 (2017)

    Google Scholar 

  6. Zheng, F., Zecchin, A.C., Newman, J.P., et al.: An adaptive convergence-trajectory controlled ant colony optimization algorithm with application to water distribution system design problems. IEEE Transact. Evol. Comput. 21(5), 773–791 (2017)

    Article  Google Scholar 

  7. Yin, Z., Du, C., Liu, J., et al.: Research on auto-disturbance-rejection control of induction motors based on ant colony optimization algorithm. IEEE Transact. Ind. Electron. 99, 1 (2017)

    Google Scholar 

  8. Volochienko, V.: Improvement of automated control systems for production and production logistics development. Управление 3(2), 22–26 (2015)

    Google Scholar 

  9. Bannoura, A., Höflinger, F., Gorgies, O., Gamm, G., Albesa, J., Reindl, L.: Acoustic wake-up receivers for home automation control applications. Electronics 5(1), 4 (2016)

    Article  Google Scholar 

  10. Vytautas, Ostasevicius, Rolanas, Dauksevicius: Intelligent systems, control and automation: science and engineering. Prov. Med. Surg. J. (1844–1852) 12(6), 154–159 (2016)

    Google Scholar 

Download references

Acknowledgements

1. This study was supported by the National Natural Science Foundation of China (Nos. 61741203, 61163012 and 71462005); 2. Guangxi Natural Science Foundation (No. 2016GXNSFAA380243); 3. Guangxi innovation-driven development of special funds project (No. Gui Ke AA17204011); 4. Research Foundation of Guangxi Teachers Education University in 2017.

Author information

Authors and Affiliations

  1. School of Logistics Management and Engineering, Guangxi Teachers Education University, Nan Ning, Guang Xi, China

    Xiaomo Yu, Wenjing Li, Xinquan Liu & Zhang Tao

  2. College of Mechanical Engineering, Guangxi University, Nan Ning, Guang Xi, China

    Xiaoping Liao

Authors
  1. Xiaomo Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaoping Liao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wenjing Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xinquan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhang Tao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhang Tao.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yu, X., Liao, X., Li, W. et al. Logistics automation control based on machine learning algorithm. Cluster Comput 22 (Suppl 6), 14003–14011 (2019). https://doi.org/10.1007/s10586-018-2169-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-2169-0

Keywords

Search

Navigation