Multi-objective model and optimization algorithm based on column generation for continuous casting production planning

  • Jian YiEmail author
  • Shu-jin Jia
  • Bin Du
  • Qing Liu
Original Article


The optimization of continuous casting production planning (CCPP) in the steel industry was studied. The essence of CCPP is the productive capability balance of back-end facilities over the planning time period. The facilities with the demands of productive capability are called the flows. On the basis of charge plans, the task of CCPP is to produce cast plans according to the rules of cast design in order to meet the flows. Firstly, a multi-objective model was established with considering the demands of flows, the capability requirements of liquid steel refining devices, and the rules of cast design. Secondly, the model was decomposed into a master problem (set partitioning problem model) and a series of subproblem (pricing problem model) by using Dantzig–Wolfe decomposition strategy. Finally, a column generation-based optimization algorithm (CGBOA) was presented. The experimental results on practical production data from Baosteel demonstrate that the proposed algorithm is effective and feasible. Moreover, a new decision support system based on CGBOA has been successfully established and applied to No. 2 Steelmaking Plant at Baosteel.


Steelmaking Continuous casting Production planning Charge Cast Column generation 



This work is supported by the National Key Research and Development Program of China (No. 2017YFB0304100).


  1. [1]
    L.X. Tang, J.Y. Liu, A.Y. Rong, Z.H. Yang, Eur. J. Oper. Res. 133 (2001) 1–20.CrossRefGoogle Scholar
  2. [2]
    J. Mori, V. Mahalec, Comput. Chem. Eng. 101 (2017) 312–325CrossRefGoogle Scholar
  3. [3]
    A. Bellabdaoui, J. Teghem, Int. J. Prod. Econ. 104 (2006) 260–270.CrossRefGoogle Scholar
  4. [4]
    D.F. Zhu, Z. Zheng, X.Q. Gao, J. Iron Steel Res. Int. 17 (2010) No. 9, 19–24.CrossRefGoogle Scholar
  5. [5]
    A. Atighehchian, M. Bijari, H. Tarkesh, Comput. Oper. Res. 36 (2009) 2450–2461.CrossRefGoogle Scholar
  6. [6]
    W. Liu, L.L. Sun, J. Iron Steel Res. Int. 19 (2012) No. 2, 17–21.CrossRefGoogle Scholar
  7. [7]
    L.X. Tang, Y. Zhao, J.Y. Liu, IEEE Trans. Evol. Comput. 18 (2014) 209–225.CrossRefGoogle Scholar
  8. [8]
    J.Q. Li, Q.K. Pan, K. Mao, P.N. Suganthan, Knowl-based. Syst. 72 (2014) 28–36.CrossRefGoogle Scholar
  9. [9]
    J.Y. Long, Z. Zheng, X.Q. Gao, Y.M. Gong, J. Iron Steel Res. Int. 21 (2014) S1, 44–50.CrossRefGoogle Scholar
  10. [10]
    Q.K. Pan, Eur. J. Oper. Res. 250 (2016) 702–714.CrossRefGoogle Scholar
  11. [11]
    H.J. Cui, X.C. Luo, Comput. Chem. Eng. 106 (2017) 133–146.CrossRefGoogle Scholar
  12. [12]
    Z. Zheng, J.Y. Long, X.Q. Gao, J. Iron Steel Res. Int. 24 (2017) 586–594.CrossRefGoogle Scholar
  13. [13]
    L.X. Tang, G.S. Wang, Omega 36 (2008) 976–991.CrossRefGoogle Scholar
  14. [14]
    J.H. Lin, M. Liu, J.H. Hao, S.L. Jiang, Comput. Oper. Res. 72 (2016) 189–203.CrossRefGoogle Scholar
  15. [15]
    S.H. Song, Int. J. Prod. Res. 52 (2014) 1–12.CrossRefGoogle Scholar
  16. [16]
    L. Wang, M.G. Wang, Systems Engineering-Theory Methodology Applications 6 (1997) 39–43.Google Scholar
  17. [17]
    T.M. Ma, T.Y. Chai, B.L. Zheng, Journal of System Simulation 23 (2011) 1054–1058.Google Scholar
  18. [18]
    Y.C. Xue, D.L. Zheng, Q.W. Yang, Control Theory & Applications 27(2010) 273–277.Google Scholar
  19. [19]
    S.S. Ning, W. Wang, X.J. Pan, Control Theory & Applications 24 (2007) 374–379.Google Scholar
  20. [20]
    L.L. Sun, F.J. Luan, T.M. Ma, IFAC 48 (2015) 144–149.Google Scholar
  21. [21]
    C. Barnhart, E.L. Johnson, G.L. Nemhauser, P.H. Vance, Oper. Res. 46 (1998) 316–329.MathSciNetCrossRefGoogle Scholar

Copyright information

© China Iron and Steel Research Institute Group 2018

Authors and Affiliations

  1. 1.Institute of Intelligent ManufacturingBaosteel Central Research InstituteShanghaiChina
  2. 2.College of Information Science and EngineeringNortheastern UniversityShenyangChina
  3. 3.State Key Laboratory of Advanced MetallurgyUniversity of Science and Technology BeijingBeijingChina

Personalised recommendations