This study analyzes the strategic planning of an oil supply chain. To optimize this chain, a two-stage stochastic model with fixed recourse and incorporation of risk management was developed. The model took a scenario-based approach and addressed three sources of uncertainty. To deal with these uncertainties, the conditional value-at-risk (CVaR) was adopted as a risk measure, and then the model was applied to the supply chain of six oil refineries. The goal of the study was to maximize the expected net present value, E(NPV), of the supply chain under analysis. The results indicate that the optimization of the several scenarios yielded an E(NPV) variation that reached US$ 36 million. Such a significant difference demonstrates that taking uncertainties into consideration is a fundamental step in decision-making processes.

This paper proposes the development of a strategic planning model for an integrated oil chain considering three sources of uncertainty: crude oil production, demand for refined products and market prices. To deal with these uncertainties, three formulations are proposed: (1) a two-stage stochastic model with a finite number of realizations, (2) a robust min-max regret model and (3) a max-min model. These models were applied to Brazil's oil chain, comprising 17 refineries and three main petrochemical plants, 16 groups of crude oils, 50 intermediate products, 10 final products, 13 terminals and a logistic network composed of 278 transportation arcs relative to the road, water, rail and pipeline modes. The time horizon analyzed covers 10 years. The results indicate significant financial differences between the three formulations, depending on the agent's risk profile.

Oil refining is one of the most complex activities in the chemical industry due to its nonlinear nature, which ruins the convexity properties and prevents any guarantees of the global optimality of solutions obtained by local nonlinear programming (NLP) algorithms. Moreover, using global optimization algorithms is often not feasible because they typically require large computational efforts. This paper proposes the use of convex relaxations based on McCormick envelopes for the Refinery Operations Planning Problem (ROPP) that can be used to generate both initial solutions for the ROPP and to estimate optimality gaps for the solutions obtained. The results obtained using data from a real-world refinery suggest that the proposed approach can provide reasonably good solutions for the ROPP, even for cases in which there was no solution available using traditional local NLP algorithms. Furthermore, compared with a global optimization solver, the proposed heuristic is capable of obtaining better solutions in less computational time.

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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.

The oil industry is increasingly interested in improving the planning of their operations, because of the dynamic nature of the oil business. This study intends to establish an iterative integration approach for the tactical and operational planning of multisite refining networks. Tactical and operational mathematical models are proposed. Both models are two-stage stochastic linear programs in which uncertainty is incorporated into the dominant random parameters at each decision level. Decisions made in the oil industry differ based on multisite network echelon (spatial integration) and planning horizon (temporal integration). Spatial integration is discussed at the tactical level, whereas temporal integration is discussed with respect to the interaction between the two levels. In the proposed temporal integration approach (iterative approach), there is a cyclic information flow between the two models. An industrial scale study using data from the Brazilian oil industry was conducted to discuss the benefits of integration in a stochastic environment.

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