Skip to main content

Advertisement

SpringerLink
Log in

Cost Optimization Control of Logistics Service Supply Chain Based on Cloud Genetic Algorithm

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

As a kind of service supply chain, logistics service supply chain is an important trend in the development of logistics industry. Compared with developed countries in foreign countries, the level of logistics development in China is lagging behind. Facing the unprecedented competition pressure from foreign logistics enterprises, how to realize the construction and operation with high quality and low cost in logistics service supply chain is one of the key problems at present. Quality cost is the cross field of quality management and cost control, and its function has been widely verified at home and abroad. Therefore, through the research and analysis of the quality cost, the construction and cost optimization method of logistics service supply chain based on cloud genetic algorithm is proposed. The quality cost theory is applied to the related research of logistics service supply chain to realize the integration between integrated logistics service providers and functional logistics service providers with high quality and low cost. At the same time, it can provide the basis and means for quality management and cost control in the long-term operation of logistics service supply chain. Finally, the effectiveness of quality cost in the construction and operation optimization of logistics service supply chain is verified through the case of S Company.

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

Similar content being viewed by others

References

  1. Dasgupta, K., Mandal, B., Dutta, P., et al. (2013). A genetic algorithm (GA) based load balancing strategy for cloud computing. Procedia Technology, 10(2), 340–347.

    Article  Google Scholar 

  2. Qiu, M., Zhong, M., Li, J., et al. (2015). Phase-change memory optimization for green cloud with genetic algorithm. IEEE Transactions on Computers, 64(12), 3528–3540.

    Article  MathSciNet  MATH  Google Scholar 

  3. Tao, F., Feng, Y., Zhang, L., et al. (2014). CLPS-GA: A case library and Pareto solution-based hybrid genetic algorithm for energy-aware cloud service scheduling. Applied Soft Computing, 19(6), 264–279.

    Article  Google Scholar 

  4. Huang, S. C., Jiau, M. K., & Lin, C. H. (2015). A genetic-algorithm-based approach to solve carpool service problems in cloud computing. IEEE Transactions on Intelligent Transportation Systems, 16(1), 352–364.

    Article  Google Scholar 

  5. Verma, A., & Kaushal, S. (2014). Deadline constraint heuristic-based genetic algorithm for workflow scheduling in cloud. International Journal of Grid and Utility Computing, 5(2), 96–106.

    Article  Google Scholar 

  6. Ding, J., & Yang, S. (2013). Classification rules mining model with genetic algorithm in cloud computing. International Journal of Computer Applications, 48(18), 24–32.

    Article  Google Scholar 

  7. Moghaddam, F. F., Moghaddam, R. F., & Cheriet, M. (2015). Carbon-aware distributed cloud: Multi-level grouping genetic algorithm. Cluster Computing, 18(1), 477–491.

    Article  Google Scholar 

  8. Kaaouache, M. A., & Bouamama, S. (2015). Solving bin packing problem with a hybrid genetic algorithm for VM placement in cloud. Procedia Computer Science, 60(1), 1061–1069.

    Article  Google Scholar 

  9. Messias, V. R., Estrella, J. C., Ehlers, R., et al. (2016). Combining time series prediction models using genetic algorithm to autoscaling Web applications hosted in the cloud infrastructure. Neural Computing and Applications, 27(8), 2383–2406.

    Article  Google Scholar 

  10. Sellami, K., Ahmed-Nacer, M., Tiako, P. F., et al. (2013). Immune genetic algorithm for scheduling service workflows with QoS constraints in cloud computing. South African Journal of Industrial Engineering, 24(24), 68–82.

    Google Scholar 

  11. Guo, W., & Wang, X. (2015). A data placement strategy based on genetic algorithm in cloud computing platform. International Journal of Intelligence Science, 5(3), 145–157.

    Article  Google Scholar 

  12. Jung, D., Suh, T., Yu, H., et al. (2014). A workflow scheduling technique using genetic algorithm in spot instance-based cloud. Ksii Transactions on Internet and Information Systems, 8(9), 3126–3145.

    Google Scholar 

  13. Ramezani, F., Lu, J., Taheri, J., & Hussain, F. K. (2015). Evolutionary algorithm-based multi-objective task scheduling optimization model in cloud environments. World Wide Web-internet and Web Information Systems, 18(6), 1737–1757.

    Google Scholar 

  14. Janani, N., Jegan, S. R. D., & Prakash, P. (2015). Optimization of virtual machine placement in cloud environment using genetic algorithm. Research Journal of Applied Sciences Engineering and Technology, 10(3), 274–287.

    Article  Google Scholar 

  15. Zhang, C., & Guoli, X. U. (2014). Prediction for traffic flow of RBF neural network based on cloud genetic algorithm. Computer Engineering and Applications, 43(1), 91–116.

    Google Scholar 

Download references

Acknowledgements

The author acknowledged the follow projects: (1) Statistics and Scientific Research Project of China in 2016: The Measurement and Evaluation on the Regional logistics capability of Silk Road economic belt (2016334); (2) International Science and Technology Cooperation and Exchange Project in Shaanxi Province: Food Safety Supervision and System Development Based on Internet of Things (2016kw_045); (3) 2018 Scientific Research Project of Shaanxi Provincial Department of Education: Food safety traceability platform Programming based on Internet of Things.

Author information

Authors and Affiliations

  1. Northwestern Polytechnical University, Xi’an, 710072, China

    Ying Xue

  2. Xi’an University of Finance and Economics, Xi’an, 710100, China

    Ying Xue

  3. Weinan Normal University, Weinan, 714099, China

    Li Ge

Authors
  1. Ying Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Li Ge
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ying Xue.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xue, Y., Ge, L. Cost Optimization Control of Logistics Service Supply Chain Based on Cloud Genetic Algorithm. Wireless Pers Commun 102, 3171–3186 (2018). https://doi.org/10.1007/s11277-018-5335-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-018-5335-z

Keywords

Search

Navigation