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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.


Scheduling output for the weekly plan of Kittilä mine.
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pone.0131003.g016: Scheduling output for the weekly plan of Kittilä mine.

Mentions: After inputting and running the schedule optimizer by a CPU 1.7GHz and RAM 8GB laptop, a Gantt chart was produced within 20 seconds (Fig 16). The execution of the scheduling process uses the machine sets algorithm. The different machine sets respectively were assigned in different mining areas, i.e. Working Faces 3~9, Working Faces 1,2, 26~35, and Working Faces 10~25. And then the working faces in the mining areas are divided into smaller groups, by using the grouping algorithm. Next, the minimum timespan was found by invoking the sequencing algorithm. The Gantt chart shows that the entire mining process could be completed in 52 hours. It should be noted that the Gantt chart does not include shift-changing time, ventilation time, coffee and lunch time, which is around 18 hours for the two days. Furthermore, it does not include maintenance time of mobile machines because there were no broken-down machines reported in that week. Therefore, the optimized result gives a shorter period of circa 70 hours, which is less than one week when the mine did. The reasons of such discrepancy of the two working periods are the following: firstly, the schedule optimizer used the optimized sequence to schedule the jobs for each machine; secondly, the working parameters of machines were deterministic which can increase the error of this comparison; thirdly, there were idle times of crew and machines in the underground mining (therefore, there have been many applications of underground tracking and reporting developed). In view of the big difference between the optimized and actual result, it should be a considerable contribution of the schedule optimizing techniques.


Intelligent Scheduling for Underground Mobile Mining Equipment.

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

Scheduling output for the weekly plan of Kittilä mine.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0131003.g016: Scheduling output for the weekly plan of Kittilä mine.
Mentions: After inputting and running the schedule optimizer by a CPU 1.7GHz and RAM 8GB laptop, a Gantt chart was produced within 20 seconds (Fig 16). The execution of the scheduling process uses the machine sets algorithm. The different machine sets respectively were assigned in different mining areas, i.e. Working Faces 3~9, Working Faces 1,2, 26~35, and Working Faces 10~25. And then the working faces in the mining areas are divided into smaller groups, by using the grouping algorithm. Next, the minimum timespan was found by invoking the sequencing algorithm. The Gantt chart shows that the entire mining process could be completed in 52 hours. It should be noted that the Gantt chart does not include shift-changing time, ventilation time, coffee and lunch time, which is around 18 hours for the two days. Furthermore, it does not include maintenance time of mobile machines because there were no broken-down machines reported in that week. Therefore, the optimized result gives a shorter period of circa 70 hours, which is less than one week when the mine did. The reasons of such discrepancy of the two working periods are the following: firstly, the schedule optimizer used the optimized sequence to schedule the jobs for each machine; secondly, the working parameters of machines were deterministic which can increase the error of this comparison; thirdly, there were idle times of crew and machines in the underground mining (therefore, there have been many applications of underground tracking and reporting developed). In view of the big difference between the optimized and actual result, it should be a considerable contribution of the schedule optimizing techniques.

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.