Fire Risk Reduction and Recover Energy Potential: A Disruptive Theoretical Optimization Model to the Residual Biomass Supply Chain

Rural fires have been a constant concern, with most being associated with land abandonment. However, some fires occur due to negligent attitudes towards fire, which is often used to remove agroforestry leftovers. In addition to the fire risk, this burning also represents a waste of the energy present in this residual biomass. Both rural fires and energy waste affect the three dimensions of sustainability. The ideal solution seems to be to use this biomass, avoiding the need for burning and recovering the energy potential. However, this process is strongly affected by logistical costs, making this recovery unfeasible. In this context, this study aims to propose an optimization model for this chain, focusing on the three dimensions of sustainability. The results of the present study comprise a summary of the current state of the art in supply-chain optimization, as well as a disruptive mathematical model to optimize the residual biomass supply chain. To achieve this objective, a literature review was carried out in the first phase, incorporating the specificities of the context under study to arrive at the final model. To conclude, this study provides a review covering several metaheuristics, including ant colony optimization, genetic algorithms, particle swarm optimization, and simulated annealing, which can be used in this context, adding another valuable input to the final discussion.