Title :
An Intelligent Platform for Green Forecasting Optimization
Author :
Chen-Fang Tsai ; Shin-Li Lu
Author_Institution :
Dept. of Ind. Manage. & Enterprise Inf., Aletheia Univ., New Taipei, Taiwan
Abstract :
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.
Keywords :
genetic algorithms; supply chain management; AEGM; GA parameter optimization; GGA controller; GM background value optimization; GSCM; adaptive exponential grey models; forecast algorithms; forecast efficiency; forecast precision; green forecasting optimization; green supply chain management; grey genetic algorithm; intelligent platform; inventory level; mutation rate controller; optimal solution; technical platform; total green cost; Accuracy; Algorithm design and analysis; Forecasting; Integrated circuit modeling; Optimization; Prediction algorithms; Predictive models; GM test-beds; Green Prediction Model; Grey Genetic Algorithms;
Conference_Titel :
e-Business Engineering (ICEBE), 2013 IEEE 10th International Conference on
Conference_Location :
Coventry
DOI :
10.1109/ICEBE.2013.22
