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Logistics optimisation of slab pre-marshalling problem in steel industry

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  • Peixin Ge
  • Ying Meng
  • Jiyin Liu
  • Lixin Tang
  • Ren Zhao

Abstract

We study the slab pre-marshalling problem to re-position slabs in a way that the slabs are stored in the least number of stacks and each stack contains only the slabs of the same group, which can be utilised interchangeably. In this way, when a slab of any group is required, the topmost slab can always be picked up without shuffling. During pre-marshalling, however, at most two slabs can be moved by one operation. In this paper, we present a network model with three valid inequalities to solve this problem. With a small amount of labelled data from the model approach, a self-training technique is applied to train a function for predicting the best next move. Then, a new hybrid algorithm is developed to solve the practical problems by combining the self-training technique, heuristics, and the branch-and-bound algorithm with five dominance rules. The experimental results demonstrate the effectiveness of this network model and valid inequalities, and the performances of different components of this algorithm. The new algorithm produces high-quality solutions within seconds.

Suggested Citation

  • Peixin Ge & Ying Meng & Jiyin Liu & Lixin Tang & Ren Zhao, 2020. "Logistics optimisation of slab pre-marshalling problem in steel industry," International Journal of Production Research, Taylor & Francis Journals, vol. 58(13), pages 4050-4070, July.
    • Handle: RePEc:taf:tprsxx:v:58:y:2020:i:13:p:4050-4070
    DOI: 10.1080/00207543.2019.1641238
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    Cited by:

    1. Parreño-Torres, Consuelo & Alvarez-Valdes, Ramon & Parreño, Francisco, 2022. "A beam search algorithm for minimizing crane times in premarshalling problems," European Journal of Operational Research, Elsevier, vol. 302(3), pages 1063-1078.
    2. Pfrommer, Jakob & Meyer, Anne & Tierney, Kevin, 2024. "Solving the unit-load pre-marshalling problem in block stacking storage systems with multiple access directions," European Journal of Operational Research, Elsevier, vol. 313(3), pages 1054-1071.

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