  The global agrochemical market is highly consolidated, with large
multinational companies accounting for a major share of the market. Thus, even
for a single agrochemical product, its global supply chain typically involves
numerous paths connecting the raw material sources to the final customers.
Besides structural complexity, agrochemical supply chains are also subject to
seasonality and other unique uncertainties, thereby posing a need for risk
management tools and strategies. In this study, we model and optimize an
agrochemcial supply chain by developing and solving a stochastic mixed-integer
quadratic constrained program (MIQCP). We model and control the demand
uncertainty in this scenario-based MIQCP using variance. We also apply
perspective reformulation techniques to convert the MIQCP to a mixed-integer
linear program (MILP). Computational experiment results from an illustrative
example show that, successively introducing perspective cuts to the
reformulated MILP not only leads to a tight approximation of the original MIQCP
model, but is also more computationally efficient than directly solving the
MIQCP.
