Biomass supply chain resilience: integrating demand and availability predictions into routing decisions using machine learning

Biomass sources have the potential to mitigate carbon emissions as a renewable source while reducing waste and residues. Seasonality disruption risks are some of disadvantages biomass resources requiring that supply chains be managed such withstand disruptions. There has been very limited research on integrating predictions for smart management or demand sides chains. In this study, number predictive models investigated building energy stock availability subject forecasts weather conditions. On basis, an allocation algorithm is proposed optimal collection logistics from land depots. Accordingly, Google Maps API will used identify best distribution routes delivering depots end-users. A case study with real (supply demand) data considered. The integrated data-driven approach aims at improving accuracy coordinating these enhance resiliency bioenergy chain routing decisions.