Optimizing economic and environmental sustainability in biomass-based energy supply chains: A multi-criteria decision-making approach

To address the problems that are caused by traditional energy systems, bioenergy sources have been recognized as a potential alternative. The unpredictability of supply-side externalities, such as collection and transportation, is one of the critical challenges that bioenergy supply chains face. These externalities have the potential to undercut the advantages that are associated with sustainability. Implementing a mixed biomass-based energy supply chain incorporating mixed-mode bio-refineries and mixed-pathway transportation is a viable strategy for addressing these difficulties. A framework for decision-making that considers many criteria has been established to facilitate decision-making that affects both the economic and environmental components of sustainability. When predicting uncertainty parameters, the financial analysis uses a technology known as a support vector machine. On the other hand, stochastic optimization models include these uncertainties in the decision-making process. A genetic algorithm is used in this model to achieve the goal of minimizing the overall yearly cost of the mixed supply chain network. Life cycle assessment analyses the potential for global warming associated with the cost-effective supply chain. This helps to ensure that environmental factors are considered. The use of this technique improves the sustainability advantages of bioenergy infrastructure. The method is shown using a case study in the Pacific Northwest. The research findings suggest that diversified supply chains can enhance sustainability performance by lowering costs by as much as 24 per cent and decreasing environmental effects by 5 per cent.