Developing sustainable, resilient, and responsive biofuel production and distribution management system: A neutrosophic fuzzy optimization approach based on artificial intelligence and geographic information systems.

Amidst the surging energy demand, contemporary biofuel production and distribution systems face the challenge of being sustainable, resilient, and responsive to mitigate environmental impact, withstand disruption, and meet regulatory requirements while remaining competitive in the market. However, existing literature predominantly focuses on sustainability alone, leaving a research gap in understanding the complex relationship among sustainability, resilience, and responsiveness within biofuel production systems. This study aims to bridge this gap by proposing a novel approach that integrates sustainability, resilience, and responsiveness within the (lean, agile, responsive, green) LARG framework through the development of a decision-making system. To mitigate operational uncertainty in decision-making, the study employs neutrosophic fuzzy optimization (NFO), support vector regression (SVR) for biomass supply prediction, and geographic information systems (GIS) for potential biorefinery site identification. The proposed model and solution approach undergo validation in a real case study, accompanied by sensitivity analyses. Key findings reveal a substantial influence of the decision maker's risk attitude on strategic decisions in the biofuel production system, with risk-averse approaches resulting in lower objective attainment levels. Additionally, strategic decisions concerning biorefinery locations emerge as critical factors in the biofuel production and distribution system, significantly affecting both upstream and downstream operations and the attainment levels of sustainability, resilience, and responsiveness dimensions. Furthermore, the study observes that introducing redundancy to the biodiesel production network without adequate planning may not necessarily enhance resilience. However, reinforcing critical nodes with redundancy proves to be a significant factor in improving overall network resilience, hence withstanding disruptive events. Biofuel producers, along with policymakers, regulatory bodies, and related stakeholders, stand to potentially benefit from this research. • Developing a Sustainable, Resilient, and Responsive Biodiesel production system. • Enhancing system resilience with lateral supplies and node analysis metrics. • Applying SVR for biomass forecasting and GIS for the analysis of spatial data. • Proposing a modified neutrosophic fuzzy approach to deal with uncertainty. [ABSTRACT FROM AUTHOR]