Logistics optimisation of slab pre-marshalling problem in steel industry

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.

Keywords:

• slab pre-marshalling
• self-training
• hybrid algorithm
• dominance rules

Acknowledgements

The authors would like to thank editors and referees for their valuable suggestions, which have significantly improved our manuscript. This work was supported by the Fund for Innovative Research Groups of the National Natural Science Foundation of China (71621061), the Major International Joint Research Project of the National Natural Science Foundation of China (71520107004), the Major Program of National Natural Science Foundation of China (71790614), and the 111 Project (B16009).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Fund for Innovative Research Groups of the National Natural Science Foundation of China (71621061), the Major International Joint Research Project of the National Natural Science Foundation of China (71520107004), the Major Program of National Natural Science Foundation of China (71790614), and the 111 Project (B16009).