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Intelligent Scheduling for Underground Mobile Mining Equipment.

Song Z, Schunnesson H, Rinne M, Sturgul J - PLoS ONE (2015)

Bottom Line: This investigation first introduces the motivation, the technical background, and then the objective of the study.A decision support instrument (i.e. schedule optimizer for mobile mining equipment) is proposed and described to address this issue.The method and related algorithms which are used in this instrument are presented and discussed.

View Article: PubMed Central - PubMed

Affiliation: Department of Civil and Environmental Engineering, School of Engineering, Aalto University, Espoo, Finland.

ABSTRACT
Many studies have been carried out and many commercial software applications have been developed to improve the performances of surface mining operations, especially for the loader-trucks cycle of surface mining. However, there have been quite few studies aiming to improve the mining process of underground mines. In underground mines, mobile mining equipment is mostly scheduled instinctively, without theoretical support for these decisions. Furthermore, in case of unexpected events, it is hard for miners to rapidly find solutions to reschedule and to adapt the changes. This investigation first introduces the motivation, the technical background, and then the objective of the study. A decision support instrument (i.e. schedule optimizer for mobile mining equipment) is proposed and described to address this issue. The method and related algorithms which are used in this instrument are presented and discussed. The proposed method was tested by using a real case of Kittilä mine located in Finland. The result suggests that the proposed method can considerably improve the working efficiency and reduce the working time of the underground mine.

No MeSH data available.


Layout of Kittilä mine with the locations of the working face used in the case.
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pone.0131003.g015: Layout of Kittilä mine with the locations of the working face used in the case.

Mentions: The weekly plan of Kittilä during 4–10 September 2013 and the operating data of mobile mining equipment were used as the input data for the schedule optimizer. There were 35 working faces, 3 machine sets, and 7 types of machine (i.e. 7 working procedures at each working face). The workload at each working face was acquired from the Kittilä’s weekly plan, and the machine operating data were acquired from manufacturers’ manuals and experienced operators’ estimations. The locations of those 35 working faces are shown in the schematic layout of Kittilä mine in Fig 15. After the data were inputted into the schedule optimizer, the program was first run to obtain the scheduling based on the priority of each working face, and then followed by the lower prioritized working faces. The priority of each working face was set to the value of “1” in this case study.


Intelligent Scheduling for Underground Mobile Mining Equipment.

Song Z, Schunnesson H, Rinne M, Sturgul J - PLoS ONE (2015)

Layout of Kittilä mine with the locations of the working face used in the case.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4476722&req=5

pone.0131003.g015: Layout of Kittilä mine with the locations of the working face used in the case.
Mentions: The weekly plan of Kittilä during 4–10 September 2013 and the operating data of mobile mining equipment were used as the input data for the schedule optimizer. There were 35 working faces, 3 machine sets, and 7 types of machine (i.e. 7 working procedures at each working face). The workload at each working face was acquired from the Kittilä’s weekly plan, and the machine operating data were acquired from manufacturers’ manuals and experienced operators’ estimations. The locations of those 35 working faces are shown in the schematic layout of Kittilä mine in Fig 15. After the data were inputted into the schedule optimizer, the program was first run to obtain the scheduling based on the priority of each working face, and then followed by the lower prioritized working faces. The priority of each working face was set to the value of “1” in this case study.

Bottom Line: This investigation first introduces the motivation, the technical background, and then the objective of the study.A decision support instrument (i.e. schedule optimizer for mobile mining equipment) is proposed and described to address this issue.The method and related algorithms which are used in this instrument are presented and discussed.

View Article: PubMed Central - PubMed

Affiliation: Department of Civil and Environmental Engineering, School of Engineering, Aalto University, Espoo, Finland.

ABSTRACT
Many studies have been carried out and many commercial software applications have been developed to improve the performances of surface mining operations, especially for the loader-trucks cycle of surface mining. However, there have been quite few studies aiming to improve the mining process of underground mines. In underground mines, mobile mining equipment is mostly scheduled instinctively, without theoretical support for these decisions. Furthermore, in case of unexpected events, it is hard for miners to rapidly find solutions to reschedule and to adapt the changes. This investigation first introduces the motivation, the technical background, and then the objective of the study. A decision support instrument (i.e. schedule optimizer for mobile mining equipment) is proposed and described to address this issue. The method and related algorithms which are used in this instrument are presented and discussed. The proposed method was tested by using a real case of Kittilä mine located in Finland. The result suggests that the proposed method can considerably improve the working efficiency and reduce the working time of the underground mine.

No MeSH data available.