Advertisement

Cluster Computing

, Volume 22, Supplement 4, pp 8541–8552 | Cite as

Supply chain production and delivery scheduling based on data mining

  • Mengna WuEmail author
  • Ke Liu
  • Hua Yang
Article
  • 260 Downloads

Abstract

Aiming at many problems such as variable and uncertainty in problem modeling, and the phenomenon of big load in the distribution of FMCG, the idea of customer clustering and dynamic customer clustering is put forward. The data mining is used to model the distribution and scheduling problem of multi product and multi item products for the purchase of third party logistics and distribution services. At the same time, the classification method of customer clustering is studied. The general rules of dynamic customer clustering, the scope of application and the rules of application are proposed. In view of the fact that the needs of customers in real operation are dealt with instantaneously, the scheduling method based on qualitative and quantitative heuristic rules is discussed. The suggestion of customer clustering set is provided to supply the order rank of production place. The results show that the idea of customer clustering effectively reduces the difficulty of solving the model. The dynamic customer thought increases the flexibility of customer clustering thinking. It is suitable for the uncertainty of the practical application of enterprises, and further reduces the logistics cost of enterprises. This rule has a high reference value for the scheduling of enterprise products.

Keywords

Fast moving consumer goods Delivery dispatch Supply chain Production 

Notes

Acknowledgements

Dr. Mengna Wu acknowledges the “Fund Project: Youth Project from Ministry of Education, Two Level Automobile Supply Chain Scheduling Model Based on Grade Differentiation under Carbon Emission Restriction (2015YJC630134). Outstanding Young Scientists of Shandong Province, Study on development evaluation and economic performance of Inclusive Finance in Shandong Province (BS2015SF018).

References

  1. 1.
    Chen, G., Sun, X., Hu, H., Hu, Y.: Research on modeling and algorithm of supply chain’s reliability based on ccfsm. J. Coast. Res. 73, 99–103 (2015)CrossRefGoogle Scholar
  2. 2.
    Dev, N.K., Shankar, R., Gunasekaran, A., Thakur, L.S.: A hybrid adaptive decision system for supply chain reconfiguration. Int. J. Prod. Res. 54(23), 7100–7114 (2016)CrossRefGoogle Scholar
  3. 3.
    Frazzon, E.M., Albrecht, A., Pires, M., Israel, E., Kück, M., Freitag, M.: Hybrid approach for the integrated scheduling of production and transport processes along supply chains. Int. J. Prod. Res. (2015).  https://doi.org/10.1080/00207543.2017.1355118 CrossRefGoogle Scholar
  4. 4.
    Yao, J.: Optimisation of one-stop delivery scheduling in online shopping based on the physical internet. Int. J. Prod. Res. 55(2), 358–376 (2016)CrossRefGoogle Scholar
  5. 5.
    Lin, Y.K., Yeh, C.T., Huang, C.F.: A simple algorithm to evaluate supply-chain reliability for brittle commodity logistics under production and delivery constraints. Ann. Oper. Res. 244(1), 67–83 (2016)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Shukla, N., Kiridena, S.: A fuzzy rough sets-based multi-agent analytics framework for dynamic supply chain configuration. Int. J. Prod. Res. 54(23), 6984–6996 (2016)CrossRefGoogle Scholar
  7. 7.
    Choi, T.M., Yeung, W.K., Cheng, T.C.E., Yue, X.: Optimal scheduling, coordination, and the value of rfid technology in garment manufacturing supply chains. IEEE Trans. Eng. Manag. 65(1), 1–13 (2017)Google Scholar
  8. 8.
    Sellitto, M.A., Pereira, G.M., Borchardt, M., Silva, R.I.D., Viegas, C.V.: A scor-based model for supply chain performance measurement: application in the footwear industry. Int. J. Prod. Res. 53(16), 4917–4926 (2015)CrossRefGoogle Scholar
  9. 9.
    Lowe, J.J., Mason, S.J.: Integrated semiconductor supply chain production planning. IEEE Trans. Semicond. Manuf. 29(2), 116–126 (2016)CrossRefGoogle Scholar
  10. 10.
    Pan, A., Choi, T.M.: An agent-based negotiation model on price and delivery date in a fashion supply chain. Ann. Oper. Res. 242(2), 529–557 (2016)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Shretta, R., Johnson, B., Smith, L., Doumbia, S., Savigny, D.D., Anupindi, R., et al.: Costing the supply chain for delivery of act and rdts in the public sector in benin and kenya. Malaria Journal 14(1), 1–14 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Business SchoolShanDong Normal UniversityJinanChina
  2. 2.China National Heavy Duty Truck Group Co., LTDJinanChina
  3. 3.Management SchoolJiLin UniversityChangchunChina

Personalised recommendations