Abstract

Over the last decade, agriculture has evolved from a human-intensive activity to a highly automated process. Multiple technological advances (e.g., harvest machines, sensors, and drones) have been incorporated to collect and transmit information, increasing harvest efficiency and more accurate and timely decisions. These advances have opened new opportunities to apply optimization models during the harvest season. In this context, to apply these models, it is necessary to consider the underlying uncertainty in agricultural operations that comes mainly from weather conditions and the biological characteristics of crops. One of the traditional strategies used to reactively manage these uncertainties in optimization models is the Rolling Horizon (RH) strategy. However, RH is typically myopic about the future, and it can be challenging to implement this approach when commitments with suppliers are signed. This work proposes a non-myopic rolling horizon method to reschedule the agricultural harvest plan. Furthermore, our RH scheme is exemplified by means of olive oil harvesting and production. Our method is based on a baseline plan generation, and after that, an adaptive rescheduling scheme is generated under new conditions. A bi-objective rescheduling problem seeking to maximize production and minimize plan variability is formulated. Computational experiments are conducted to study our methodology's impact in several rescheduling periods. A good performance in two challenging agricultural scenarios is highlighted. This proposal offers the community a framework for reactively managing complex harvest operations.