DocumentCode
2707941
Title
Application of PSO and MPSO in project scheduling of the first mining face in coal mining
Author
Guo, Haixiang ; Li, Lanlan ; Zhu, Kejun ; Ding, Chang
Author_Institution
Sch. of Manage. & Econ., China Univ. of Geosci., Wuhan, China
fYear
2009
fDate
14-19 June 2009
Firstpage
1033
Lastpage
1040
Abstract
In this paper, the intelligent optimization methods including particle swarm optimization (PSO) and modified particle swarm optimization (MPSO) are used in optimizing the project scheduling of the first mining face of the second region of the fifth Ping´an coal mine in China. The result of optimization provides essential information of management and decision-making for governors and builder. The process of optimization contains two parts: the first part is obtaining the time parameters of each process and the network graph of the first mining face in the second region by PERT (program evaluation and review technique) method based on the raw data. The other part is the second optimization to maximal NPV (net present value) based on the network graph. The starting dates of all processes are decision-making variables. The process order and time are the constraints. The optimization result shows that MPSO is better than PSO and the optimized NPV is 14974000 RMB more than the original plan.
Keywords
coal; decision making; graph theory; mining; particle swarm optimisation; project management; scheduling; Ping´an coal mine; coal mining; decision-making; intelligent optimization methods; modified particle swarm optimization; network graph; program evaluation and review technique method; project scheduling; Costs; Decision making; Geology; Information management; Intelligent networks; Neural networks; Optimization methods; Particle swarm optimization; Production; Quality management;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2009. IJCNN 2009. International Joint Conference on
Conference_Location
Atlanta, GA
ISSN
1098-7576
Print_ISBN
978-1-4244-3548-7
Electronic_ISBN
1098-7576
Type
conf
DOI
10.1109/IJCNN.2009.5178698
Filename
5178698
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