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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 grouping algorithm.
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pone.0131003.g007: Flow chart of grouping algorithm.

Mentions: In underground mines, there are normally many working faces required to be processed within a short period, e.g. one week. This can lead to a great number of permutations of sequences of the working faces. The computing workload can be too heavy for computers to rapidly obtain the result, or they may fail to solve the problem due to stack overflow in RAM. In the case of 10 working faces, there are 10! (= 3,628,800) permutations. One solution for this is to increase hardware capacity and/or have cache on hard disk. Another solution is to process fewer permutations. In the case of 10 working faces, if they can be divided into two groups, there are 5!+5! (= 240) or 6!+4! (= 744) permutations, which significantly reduces the computing time and workload. Therefore, grouping algorithm is used to divide the working faces into a number of groups. It can help to reduce the computing workload of the sequencing algorithm and the computing time. The basic principle of grouping is to group the faces which are relatively close, based on their distance dij (di,j is the path length between two working faces i and j). In view of the computing capacity of permutation, the maximum number of group, faces and sub-groups in one group, and faces in one sub-group is empirically set as five which can certainly be changed according to other users’ experience and hardware. After the first-round grouping, if there are more than five groups and faces left, the faces and groups will be grouped further, with a maximum of five faces or sub-groups in one group. This process will continue until there are not more than five groups. The algorithm is briefly described in Fig 7.


Intelligent Scheduling for Underground Mobile Mining Equipment.

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

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

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

pone.0131003.g007: Flow chart of grouping algorithm.
Mentions: In underground mines, there are normally many working faces required to be processed within a short period, e.g. one week. This can lead to a great number of permutations of sequences of the working faces. The computing workload can be too heavy for computers to rapidly obtain the result, or they may fail to solve the problem due to stack overflow in RAM. In the case of 10 working faces, there are 10! (= 3,628,800) permutations. One solution for this is to increase hardware capacity and/or have cache on hard disk. Another solution is to process fewer permutations. In the case of 10 working faces, if they can be divided into two groups, there are 5!+5! (= 240) or 6!+4! (= 744) permutations, which significantly reduces the computing time and workload. Therefore, grouping algorithm is used to divide the working faces into a number of groups. It can help to reduce the computing workload of the sequencing algorithm and the computing time. The basic principle of grouping is to group the faces which are relatively close, based on their distance dij (di,j is the path length between two working faces i and j). In view of the computing capacity of permutation, the maximum number of group, faces and sub-groups in one group, and faces in one sub-group is empirically set as five which can certainly be changed according to other users’ experience and hardware. After the first-round grouping, if there are more than five groups and faces left, the faces and groups will be grouped further, with a maximum of five faces or sub-groups in one group. This process will continue until there are not more than five groups. The algorithm is briefly described in Fig 7.

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.