Designing and planning the downstream oil supply chain under uncertainty using a fuzzy programming approach. (August 2021) - Lima, Camilo; Relvas, Susana; Barbosa-Póvoa, Ana
Highlights: We develop a mixed-integer linear programming model to design and plan the downstream sector of the oil supply chain. We use fuzzy mathematical programming to represent uncertainty in the optimization problem. We consider uncertainty in logistics costs and demand for liquid fuels. We establish a new real-life case study to validate the proposed model. Abstract: This paper addresses the strategic and tactical planning of a downstream oil supply chain (DOSC) subject to different sources of uncertainty. This problem is formulated as a mixed-integer linear programming (MILP) model, whereas uncertainty is tackled using chance constrained programming with fuzzy parameters. The MILP model aims at determining the network design and the products distribution plan in a cost-effective way. A real case study on the Brazilian oil industry is used to validate the model. The proposed model shows to be a valuable decision-support tool in order to aid the decision-making process in the strategic and tactical planning of real-life problems.