Logistics optimization of slab pre-marshalling problem in steel industry.doc (1.38 MB)
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Logistics optimisation of slab pre-marshalling problem in steel industry

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journal contribution
posted on 16.09.2019, 13:50 authored by Peixin Ge, Ying Meng, Jiyin LiuJiyin Liu, Lixin Tang, Ren Zhao
© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. 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.

History

School

  • Business and Economics

Department

  • Business

Published in

International Journal of Production Research

Volume

58

Issue

13

Pages

4050-4070

Publisher

Taylor and Francis

Version

AM (Accepted Manuscript)

Rights holder

© Taylor and Francis

Publisher statement

This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 30 Aug 2019 available online: https://doi.org/10.1080/00207543.2019.1641238

Acceptance date

27/06/2019

Publication date

2019-08-30

Copyright date

2019

ISSN

0020-7543

eISSN

1366-588X

Language

en

Depositor

Prof Jiyin Liu