Haonan, Z., Samavati, M. orcid.org/0000-0002-3468-9155 and Hill, A.J. (2021) Heuristics for integrated blending optimisation in a mining supply chain. Omega, 102. 102373. ISSN 0305-0483
Abstract
In a mining supply chain, products from mines are blended at port terminals to ensure that a set of blending targets (such as grade and qualities) are achieved. The production scheduling problem of each individual mine and the blending problem for a network of mines and ports constitute the integrated blending optimisation, which involves modelling of material flows from mine-side pits to port-side stockpiles. Due to the problem scale and the bilinear constraints for blending behaviours, the problem is computationally hard to solve by any available optimisers. This paper extends upon a decomposition-based algorithm in the literature, which was first to solve the blending problem for a network of multiple mines and ports over multiple time periods. In our paper, a prune routine is proposed to progressively update the mixed integer program of the production scheduling problem for each mine during a rolling-horizon heuristic. Experiments have shown that this extension produces solutions of higher quality than the original algorithm. Furthermore, a ranking-based topological sorting heuristic is presented for selecting units of mineral deposits, known as ’blocks’. Experiments have shown that the average computation time can be reduced by 75.97% when this heuristic is implemented. On top of these extensions, an adaptive algorithm is adopted from the decomposition-based algorithm, featuring faster convergence and higher solution quality at the same time. Comparing our results to the literature, our adaptive algorithm, on average, yields an improvement in solution quality by 12.67% while reducing computation time by 65.09%.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 Elsevier Ltd. This is an author produced version of a paper subsequently published in Omega. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Scheduling; Integrated blending optimisation; Open-pit mine optimisation; Decomposition-based algorithm; Topological sorting |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Management School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 07 Dec 2020 11:19 |
Last Modified: | 14 May 2022 00:38 |
Status: | Published |
Publisher: | Elsevier BV |
Refereed: | Yes |
Identification Number: | 10.1016/j.omega.2020.102373 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:168746 |