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Machines
Volume 12
Issue 6
10.3390/machines12060419
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Open AccessArticle
by Kui Liu SciProfiles Scilit Preprints.org Google Scholar Bin Mei SciProfiles Scilit Preprints.org Google Scholar Qing Li SciProfiles Scilit Preprints.org Google Scholar Shuai Sun SciProfiles Scilit Preprints.org Google Scholar Qingping Zhang SciProfiles Scilit Preprints.org Google Scholar 
Kui LiuBin Mei
, Qing Li, Shuai SunQingping Zhang
1
Department of Automation, Tsinghua University, Beijing 100084, China
2
Sany Group, Changsha 410100, China
*
Authors to whom correspondence should be addressed.
Machines 2024, 12(6), 419; https://doi.org/10.3390/machines12060419
Submission received: 26 April 2024 / Revised: 7 June 2024 / Accepted: 17 June 2024 / Published: 18 June 2024
(This article belongs to the Special Issue Key Technologies in Intelligent Mining Equipment)
Abstract
Open-pit mining is a cornerstone of industrial raw material extraction, yet it is fraught with safety concerns due to rough operating conditions. The advent of Industry 4.0 has introduced advanced technologies such as AI, the IoT, and autonomous systems, setting the stage for a paradigm shift towards unmanned mining operations. With this study, we addressed the urgent need for safe and efficient production based on intelligent unmanned mining systems in open-pit mines. A collaborative production planning model was developed for an intelligent unmanned system comprising multiple excavators and mining trucks. The model is formulated to optimize multiple objectives, such as total output, equipment idle time, and transportation cost. A multi-objective optimization approach based on the genetic algorithm was employed to solve the model, ensuring a balance among conflicting objectives and identifying the best possible solutions. The computational experiments revealed that the collaborative production planning method significantly reduces equipment idle time and enhances output. Moreover, with the proposed method, by optimizing the configuration to include 6 unmanned excavators, 50 unmanned mining trucks, and 4 unloading points, a 92% reduction in excavator idle time and a 44% increase in total output were achieved. These results show the model’s potential to transform open-pit mining operations by using intelligent planning.
Keywords: open-pit mine; unmanned excavator; unmanned mining truck; collaborative production planning; Industry 4.0
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MDPI and ACS Style
Liu, K.; Mei, B.; Li, Q.; Sun, S.; Zhang, Q. Collaborative Production Planning Based on an Intelligent Unmanned Mining System for Open-Pit Mines in the Industry 4.0 Era. Machines 2024, 12, 419. https://doi.org/10.3390/machines12060419
AMA Style
Liu K, Mei B, Li Q, Sun S, Zhang Q. Collaborative Production Planning Based on an Intelligent Unmanned Mining System for Open-Pit Mines in the Industry 4.0 Era. Machines. 2024; 12(6):419. https://doi.org/10.3390/machines12060419
Chicago/Turabian Style
Liu, Kui, Bin Mei, Qing Li, Shuai Sun, and Qingping Zhang. 2024. "Collaborative Production Planning Based on an Intelligent Unmanned Mining System for Open-Pit Mines in the Industry 4.0 Era" Machines 12, no. 6: 419. https://doi.org/10.3390/machines12060419
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
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MDPI and ACS Style
Liu, K.; Mei, B.; Li, Q.; Sun, S.; Zhang, Q. Collaborative Production Planning Based on an Intelligent Unmanned Mining System for Open-Pit Mines in the Industry 4.0 Era. Machines 2024, 12, 419. https://doi.org/10.3390/machines12060419
AMA Style
Liu K, Mei B, Li Q, Sun S, Zhang Q. Collaborative Production Planning Based on an Intelligent Unmanned Mining System for Open-Pit Mines in the Industry 4.0 Era. Machines. 2024; 12(6):419. https://doi.org/10.3390/machines12060419
Chicago/Turabian Style
Liu, Kui, Bin Mei, Qing Li, Shuai Sun, and Qingping Zhang. 2024. "Collaborative Production Planning Based on an Intelligent Unmanned Mining System for Open-Pit Mines in the Industry 4.0 Era" Machines 12, no. 6: 419. https://doi.org/10.3390/machines12060419
Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.
Machines, EISSN 2075-1702, Published by MDPI
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