Operations planning is an important step in any activity as it aligns resources to achieve economic production value. In agriculture operations where uncertainty is present, planners must deal with biological and environmental factors, among others, which add variability and complexity to the production planning process. In this work, we consider operations planning to harvest grapes for wine production where uncertainty in weather conditions will affect the quality of grapes and, consequently, the economic value of the product. In this setting, planners make decisions on labor allocation and harvesting schedules, considering uncertainty of future rain. Weather uncertainty is modeled following a Markov Chain approach, in which rain affects the quality of grapes and labor productivity. We compare an expected value with a multi-stage stochastic optimization approach using standard metrics such as Value of Stochastic Solution and Expected Value of Perfect Information. We analyze the impact of grape quality over time, if they are not harvested on the optimal ripeness day, and also consider differences in ability between workers, which accounts for the impact of rain in their productivity. Results are presented for a small grape harvest instance and we compare the performance of both models under different scenarios of uncertainty, manpower ability, and product qualities. Results indicate that the multi-stage approach produces better results than the expected value approach, especially under high uncertainty and high grape quality scenarios. Worker ability is also a mechanism for dealing with uncertainty, and both models take advantage of this variable.

Chile is among the largest walnut producers and exporters globally, thanks to a favorable nut growth and production environment. Despite an increasingly competitive market, the literature offers little scientific advice regarding decision support systems (DSSs) for the nut sector. In particular, the literature does not present optimization approaches to support decision-making in walnut supply chain management, especially the processing planning. This work provides a DSS that allows the exporter to plan walnut processing decisions taking into account the quality of the raw material, such as size, color, variety, and external and internal defects, in order to maximize the benefits of the business. To formalize the problem, an integer programming model is proposed. The DSS was implemented via a web application for MeliFen, a walnut exporter located near Santiago, Chile. A comparative analysis of the last two years revealed that MeliFen increased its profit by approximately 9.8% using this tool. We also suggest other uses that this DSS provides, besides profit maximization.