A Machine Learning Based Modeling and Analytical Framework For Sustainable Biomass Supply Chain Management

The conversion of forest biomass and energy crops into sustainable value-added bioenergy and bioproducts can effectively mitigate climate change and promote the prosperity of the bioeconomy. Efficient supply chain management is crucial for optimizing the utilization of diverse biomass feedstocks and minimizing environmental impacts. Biomass supply chain management involves planning, coordinating, and optimizing various system processes, encompassing feedstock cultivation and establishment, harvesting, processing, storage, transportation, and conversion. A modeling and analysis framework integrating machine learning (ML), spatial analysis, and optimization algorithms was developed to provide strategic and operational decision support. Combining multi-criteria decision analysis (MCDA), geographic information system (GIS) identified areas suitable for establishing biomass facilities based on economic, environmental, and socio-economic criteria. ML technique, as a significant branch of artificial intelligence, automatically processes and trains models based on historical, empirical data from various sources to predict key parameters in the biomass supply chain. Mixed integer linear programming (MILP) was employed to minimize the total cost of biomass utilization under constraints of biomass supply, demand, and material balance. This study has promising implications for sustainable practices, bioenergy management, and regional bioeconomy development.