Intelligent optimization for project scheduling of the first mining face in coal mining
In this paper, the intelligent optimization methods including genetic algorithm (GA), 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 Pingan 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 GA and PSO and the optimized NPV is 14,974,000 RMB more than the original plan.
coal mining ga pso net present value process management