A Mathematical Model to Maximize the Pre-Processing, Storage, and Transportation Associated with Grain Flow in Brazil

Background: In the grain logistics context, pre-processing operations such as reception, pre-cleaning, drying, storage, and shipping are performed at farm, collecting, intermediate, sub-terminal, and terminal storage units to preserve quality, reduce losses, and add value in the products. However, high transportation costs and limited static storage capacity reduce the selling prices. The objective of this article is to maximize profit associated with pre-processing, storage, and transportation along the grain flow in Brazil. Methods: A generic post-harvest logistics network is represented as a graph connecting producers, multi-level storage units, agribusiness facilities, and ports. A multi-period, multi-level mathematical model is applied in a case study framework explored in three scenarios, covering pre-cleaning, drying, storage, and transportation costs from production areas to commercialization nodes. Results: In all three scenarios, road transport resulted in transportation costs ranging from approximately US 49 million to US 492 million, mainly over long distances. Conclusions: The location and static capacity of collecting and intermediate storage units strongly influenced transport, storage use, CO2 emissions, and post-harvest efficiency. Also, the flow concentration increased heavy-vehicle traffic, reducing overall logistics performance.