Grain Harvest Logistics Modeling and Optimization of Single Harvester / Grain Cart Operations

Grain harvesting machinery represents one of the largest costs to producers, and harvesting machinery selection is one of the most difficult and important decisions grain producers make. For most large grain producers in the Midwestern United States, in-field harvest machinery may consist of one or more harvesters and service units (usually a grain cart). In this research, optimization models for the harvester and grain cart were developed. The models allowed for direct comparison between machines of different capacities based on the most efficient route, thereby eliminating the operator affect. The harvester optimization model utilized a genetic algorithm to determine the optimal pass order, which minimized the non-working in-field time. Compared to past harvest data of three row crop production fields, the model was able to reduce the non-working in-field travel by an average of 29.1% with a standard deviation of 2.7%. A route optimization model was also developed for the grain cart, which reduced the in-field travel of the grain cart by an average of 25.2% with a standard deviation of 5.4% on five test fields. The models were integrated into a Decision Support Tool (DST) developed in MATLAB. The DST allowed users to upload past spatial yield data and compare machinery performance in their specific fields