Title
Application of PSO and MPSO in project scheduling of the first mining face in coal mining
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.
Year
DOI
Venue
2009
10.1109/IJCNN.2009.5178698
IJCNN
Keywords
Field
DocType
particle swarm optimization,optimized npv,mining face,decision-making variable,project scheduling,modified particle swarm optimization,network graph,optimization result,coal mining,intelligent optimization method,process order,time parameter,information management,face,net present value,continuous phase modulation,intelligent networks,production,project management,graph theory,mining,program evaluation and review technique,scheduling,neural networks,data mining,maintenance engineering,coal,optimization,quality management,geology
Particle swarm optimization,Graph theory,Data mining,Schedule (project management),Computer science,Scheduling (computing),Multi-swarm optimization,Program evaluation and review technique,Maintenance engineering,Project management
Conference
ISSN
Citations 
PageRank 
1098-7576
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Haixiang Guo17110.66
Lanlan Li220.77
Kejun Zhu317722.96
Chang Ding4123.98