STOCHASTIC AND BIOBJECTIVE OPTIMIZATION MODEL IN THE CORN SUPPLY CHAIN

This study presents an optimization model for the corn logistics chain in Brazil, integrating strategic decisions such as cultivation regions, silo location, and intermodality. The research employs a bi-objective, two-stage stochastic approach to minimize both logistical costs and the $CO_2$ emission balance (transportation versus carbon sequestration). The model accounts for uncertainty in corn productivity due to recent climate variations. Based on real-world data, the results generated Pareto solutions that guide infrastructure investments. It was demonstrated that choosing clean routes and strategic regions can significantly increase carbon stocks with a cost increase of only 3% to 5%. This proves the proposal is a viable and sustainable alternative for Brazilian agribusiness, reconciling economic efficiency with environmental responsibility.