We present a stochastic tactical planning model for the production and distribution of fresh agricultural
products. The model incorporates the uncertainties encountered in the fresh produce industry when
developing growing and distribution plans due to the variability of weather and demand. The main motivation
for building this model is to make tools available for producers to develop robust growing plans,
while allowing the flexibility to choose different levels of exposure to risk.
The modeling approach selected is a two-stage stochastic program in which the decisions in a first
stage are designed to meet the uncertain outcomes in a second stage. The model developed is applied
to a case study of growers of fresh produce in Mexico and in a simulation of various scenarios to test
the robustness of the planning decisions. The results show that significant improvements are obtained
in the planning recommendations when using the proposed stochastic approach as compared to those
rendered by deterministic models. For instance, for the same level of risk experimented by the producer,
planning based on the proposed stochastic models rendered increases of expected profit of over 50%. At
the same time when risk aversion policies were implemented, the expected losses decreased significantly
over those recommended by deterministic planning models