Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/138033
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Type: Conference paper
Title: Run-of-mine stockyard recovery scheduling and optimisation for multiple reclaimers
Author: Assimi, H.
Koch, B.
Garcia, C.
Wagner, M.
Neumann, F.
Citation: Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing (SAC ’22), 2022 / Hong, J., Bures, M., Park, J.W., Cerný, T. (ed./s), pp.1074-1083
Publisher: Association for Computing Machinery
Publisher Place: New York, N.Y.
Issue Date: 2022
ISBN: 9781450387132
Conference Name: ACM/SIGAPP Symposium on Applied Computing (SAC) (25 Apr 2022 - 29 Apr 2022 : virtual online)
Editor: Hong, J.
Bures, M.
Park, J.W.
Cerný, T.
Statement of
Responsibility: 
Hirad Assimi, Ben Koch, Chris Garcia, Markus Wagner, Frank Neumann
Abstract: Stockpiles are essential in the mining value chain, assisting in maximising value and production. Quality control of taken minerals from the stockpiles is a major concern for stockpile managers where failure to meet some requirements can lead to losing money. This problem was recently investigated using a single reclaimer, and basic assumptions. This study extends the approach to consider multiple reclaimers in preparing for short and long-term deliveries. The engagement of multiple reclaimers complicates the problem in terms of their interaction in preparing a delivery simultaneously and safety distancing of reclaimers. We also consider more realistic settings, such as handling different minerals with different types of reclaimers. We propose methods that construct a solution step by step to meet precedence constraints for all reclaimers in the stockyard. We study various instances of the problem using greedy algorithms, Ant Colony Optimisation (ACO), and propose an integrated local search method determining an efficient schedule. We fine-tune and compare the algorithms and show that the ACO combined with local search can yield efficient solutions.
Keywords: Stockpile; Ant colony optimisation; Greedy algorithm; Parallel processing; Iterative local search
Rights: © 2022 Copyright held by the owner/author(s). Publication rights licensed to ACM.
DOI: 10.1145/3477314.3507130
Grant ID: http://purl.org/au-research/grants/arc/DP200102364
http://purl.org/au-research/grants/arc/IC190100017
Published version: https://dl.acm.org/doi/proceedings/10.1145/3477314
Appears in Collections:Computer Science publications
Electrical and Electronic Engineering publications

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