Improved Spectral Clustering for Distributed Resources

In addressing the issue of exponential increase in solution time with the growing number of simulations when using mixed-integer linear programming methods to solve stochastic models based on Monte Carlo simulation, this paper proposes employing spectral clustering to determine the best-quality classification scheme. By using the spectral clustering method on random samples generated by Monte Carlo simulation, the optimal classification scheme is identified to group similar stochastic scenarios together. Finally, by selecting the center of each class as a representative, the number of scenarios to be considered is reduced, facilitating the solution of problems such as whole-scenario modeling and lowering the difficulty of solving. Experimental results demonstrate that the spectral clustering method proposed in this paper exhibits the best performance among some commonly used methods.