Proceedings of the 2015 5th International Conference on Computer Sciences and Automation Engineering

PSO Algorithm for Block Sequencing Problem in Open Pit Mining

Authors
Wenbo Liu, Hongyi Zhang
Corresponding Author
Wenbo Liu
Available Online February 2016.
DOI
10.2991/iccsae-15.2016.83How to use a DOI?
Keywords
Mining, Particle Swarm Optimization algorithm, CPLEX
Abstract

Iron ore is the main raw material of the production in iron and steel enterprises in China. It is a non-renewable resource and with limited reserves. In this paper, in combination with the practical situation of the mine, an integer linear programming model of a strip mining block scheduling problem is established with the scientific, reasonable, and economical principle. The maximization of mining profit is set as the goal under the precedence constraints and production capacity constraints. This paper designs an improved particle swarm optimization algorithm to solve the problem and compares with results with CPLEX optimizing software. According to the results of different scale experiment, the improved particle swarm optimization algorithm has better performance.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2015 5th International Conference on Computer Sciences and Automation Engineering
Series
Advances in Computer Science Research
Publication Date
February 2016
ISBN
10.2991/iccsae-15.2016.83
ISSN
2352-538X
DOI
10.2991/iccsae-15.2016.83How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Wenbo Liu
AU  - Hongyi Zhang
PY  - 2016/02
DA  - 2016/02
TI  - PSO Algorithm for Block Sequencing Problem in Open Pit Mining
BT  - Proceedings of the 2015 5th International Conference on Computer Sciences and Automation Engineering
PB  - Atlantis Press
SP  - 434
EP  - 441
SN  - 2352-538X
UR  - https://doi.org/10.2991/iccsae-15.2016.83
DO  - 10.2991/iccsae-15.2016.83
ID  - Liu2016/02
ER  -