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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.


Flow chart of machine sharing algorithm.
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pone.0131003.g012: Flow chart of machine sharing algorithm.

Mentions: When there are more than one machine set, it is quite common to share mobile mining machines in underground mines in case some machines are not available to work for a certain period. This algorithm is designed to share mining machines, aiming to obtain the minimum overall timespan (Fig 12). It will invoke the machine set algorithm to assign the first mining machines which are in the first working procedure, then assign the second mining machines which are in the second working procedure, and so forth until the last mining machines which are in the final working procedure. Fig 13 demonstrates the scheduling result when using the machine sharing algorithm, in the case of three machine sets when one Machine 4 was unavailable.


Intelligent Scheduling for Underground Mobile Mining Equipment.

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

Flow chart of machine sharing algorithm.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0131003.g012: Flow chart of machine sharing algorithm.
Mentions: When there are more than one machine set, it is quite common to share mobile mining machines in underground mines in case some machines are not available to work for a certain period. This algorithm is designed to share mining machines, aiming to obtain the minimum overall timespan (Fig 12). It will invoke the machine set algorithm to assign the first mining machines which are in the first working procedure, then assign the second mining machines which are in the second working procedure, and so forth until the last mining machines which are in the final working procedure. Fig 13 demonstrates the scheduling result when using the machine sharing algorithm, in the case of three machine sets when one Machine 4 was unavailable.

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.