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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.


Grouping result of a conceptual vertical long-side section of an underground mine (number: Face ID; black line: path; blue circle: group and sub-group).
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pone.0131003.g008: Grouping result of a conceptual vertical long-side section of an underground mine (number: Face ID; black line: path; blue circle: group and sub-group).

Mentions: Fig 8 gives the result of this case by using the above grouping algorithm. It illustrates a conceptual vertical long-side section of an underground mine. The black lines represent underground paths; the numbers represent different working faces; the blue circles represent different groups. In each group, there are less than five faces or sub-groups. The faces and sub-groups are grouped according to the distances from each other. Combining the grouping algorithm and the sequencing algorithm can significantly reduce the computing work load. In Fig 8, the faces are first grouped into six groups, and there are two groups (15, 16; and 17, 18, 19, 20) being grouped in one group. Then the sequencing algorithm will find the optimized sequence of faces in each group and sub-group. Next the sequencing algorithm will find the optimized sequence of sub-groups within one group, and, finally, find the optimized sequence of groups in order to obtain the minimum timespan.


Intelligent Scheduling for Underground Mobile Mining Equipment.

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

Grouping result of a conceptual vertical long-side section of an underground mine (number: Face ID; black line: path; blue circle: group and sub-group).
© Copyright Policy
Related In: Results  -  Collection

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

pone.0131003.g008: Grouping result of a conceptual vertical long-side section of an underground mine (number: Face ID; black line: path; blue circle: group and sub-group).
Mentions: Fig 8 gives the result of this case by using the above grouping algorithm. It illustrates a conceptual vertical long-side section of an underground mine. The black lines represent underground paths; the numbers represent different working faces; the blue circles represent different groups. In each group, there are less than five faces or sub-groups. The faces and sub-groups are grouped according to the distances from each other. Combining the grouping algorithm and the sequencing algorithm can significantly reduce the computing work load. In Fig 8, the faces are first grouped into six groups, and there are two groups (15, 16; and 17, 18, 19, 20) being grouped in one group. Then the sequencing algorithm will find the optimized sequence of faces in each group and sub-group. Next the sequencing algorithm will find the optimized sequence of sub-groups within one group, and, finally, find the optimized sequence of groups in order to obtain the minimum timespan.

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.