Operational planning of oil refineries under uncertainty Special issue: Applied Stochastic Optimization

In this paper, we describe a non-linear programming model for operational planning of oil refineries, considering exogenous (external) and endogenous (internal) uncertainties. Three mathematical models based on stochastic programming (two-stage stochastic model) and robust programming (min–max regret model and max–min model) are developed to address these uncertainties. The main purpose of this paper is to address the impact of uncertainty on the operational planning of oil refineries by using different risk profiles. The stochastic approach corresponds to a risk-neutral attitude and optimizes the expected value of the objective function. The robust approach, on the other hand, corresponds to a risk-averse attitude and hedges the decision-maker against the worst values of all possible scenarios, although it does not require the estimation of scenario probabilities. A study based on real data from a Brazilian refinery demonstrates the performance of various approaches. After analysing the oil purchase decisions, we identify a clear relationship between the adopted risk attitude and the quantity and quality of the purchased oil. We also show the strong influence of the product specification constraints on the model decisions.