An Intelligent Platform for Green Forecasting Optimization

In this research, a technical platform has been proposed to modify the traditional algorithms by two new forecast algorithms for green supply chain management (GSCM). This platform also designed a dynamic predicting controller to improve the forecast efficiency of GSCM. This grey genetic algorithm (GGA) controller can optimize the parameters of adaptive exponential grey models (AEGM) to find the better solution for their prediction efficiencies. The contributions of this GGA are essentially from the two features: (1) the crossover and mutation rate controller of GA parameter optimization. (2) the variable controller of GM background value optimization. This research is simulated and verified by a GGA to reach an optimal solution by the 5 research cases and 2 industrial cases of GM experiments. The experimental results are also revealed that the better forecast precision will reduce the inventory level and total green cost of GSCM.