Optimizing E-logistics and Transportation Networks Using Gray Prediction Models: A Green Biotechnological Approach

Abstract

This study delves into the characteristics of the e-logistics market and transportation network planning from a green environmental perspective. Utilizing a combination of gray prediction models and other forecasting techniques, this paper predicts the demand dynamics of material flow within the e-logistics sector, assessing the predictive accuracy through empirical validation. The focus is on analyzing how these models can effectively forecast market behaviors in an ecologically conscious framework. Subsequently, the paper employs a bio-inspired genetic algorithm to design an optimized transportation network model that minimizes both operational costs and carbon dioxide emissions. This model addresses the specific demands of material flow while adhering to principles of environmental sustainability. The analysis of the model's performance reveals a significant enhancement in prediction accuracy and environmental impact. The gray prediction model demonstrated an error ratio for material flow rate predictions within 0.1, while the combined forecasting approach refined this further to an error ratio of approximately 0.02. In practical terms, the application of this advanced planning model has led to substantial reductions in CO2 emissions, with a decrease noted at 0.28 following a minor cost increase. These results underline the efficacy of integrating advanced biotechnological algorithms in e-logistics to foster a transportation network that not only meets the high standards of operational efficiency but also contributes positively to environmental sustainability. The findings affirm that the proposed transportation network aligns with the goals of green environmental protection, suggesting a viable pathway for e-logistics operations to reduce their ecological footprint effectively.