Multimodal optimization model for soybean flow in the Brazilian Midwest region

Soybean outflow in Brazil’s Central-West region still relies heavily on road transport, driving up logistics costs and greenhouse gas emissions. This paper proposes an optimization model for multimodal soybean transport at the municipal level, considering production, storage, and consumption in the region. The logistics network is represented as a graph with different transport modes, and the problem is formulated to simultaneously minimize costs and CO2 emissions, incorporating
penalties for early and late delivery relative to the aggregate mean flow time, with soft and hard penalties for deviation from reference limits. The model was implemented in Python using the Gurobi solver. The results indicate that the multimodal strategy reduces costs and emissions compared to exclusively road-based transport, producing routes consistent with the available infrastructure and contributing to more efficient and sustainable logistics planning for the region’s agribusiness.