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Dynamic-programming-based inequalities for the capacitated lot-sizing problem

Author

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  • Joseph Hartman
  • İ. Büyüktahtakin
  • J. Smith

Abstract

Iterative solutions of forward dynamic programming formulations for the capacitated lot sizing problem are used to generate inequalities for an equivalent integer programming formulation. The inequalities capture convex and concave envelopes of intermediate-stage value functions and can be lifted by examining potential state information at future stages. Several possible implementations that employ these inequalities are tested and it is demonstrated that the proposed approach is more efficient than alternative integer programming–based algorithms. For certain datasets, the proposed algorithm also outperforms a pure dynamic programming algorithm for the problem.

Suggested Citation

  • Joseph Hartman & İ. Büyüktahtakin & J. Smith, 2010. "Dynamic-programming-based inequalities for the capacitated lot-sizing problem," IISE Transactions, Taylor & Francis Journals, vol. 42(12), pages 915-930.
    • Handle: RePEc:taf:uiiexx:v:42:y:2010:i:12:p:915-930
    DOI: 10.1080/0740817X.2010.504683
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    Cited by:

    1. Dogacan Yilmaz & İ. Esra Büyüktahtakın, 2023. "Learning Optimal Solutions via an LSTM-Optimization Framework," SN Operations Research Forum, Springer, vol. 4(2), pages 1-40, June.
    2. Yilmaz, Dogacan & Büyüktahtakın, İ. Esra, 2024. "An expandable machine learning-optimization framework to sequential decision-making," European Journal of Operational Research, Elsevier, vol. 314(1), pages 280-296.

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