A MILP-based clustering strategy for integrating the operational management of crude oil supply. (February 2021) - Assis, Leonardo S.; Camponogara, Eduardo; Grossmann, Ignacio E.
Highlights: An MILP clustering formulation for tackling instances of the operational management of crude oil supply (OMCOS). A decision-support model for defining rules on mixtures of crudes inside storage tanks such that property variations are minimized. Development of a linearization strategy for bilinear terms making use of the bounds on crude properties inferred by clusters. Analysis on the effect of using different clusters when solving instances of the OMCOS. Abstract: In this paper, we present a MILP clustering formulation for tackling the operational management of crude oil supply (OMCOS) proposed by de Assis et al. (2019). The OMCOS consists of defining the scheduling of vessel trips between offshore platforms and a crude oil terminal, combined with the scheduling of operations in a terminal to supply crude oil to distillation columns. The benefits of using the clustering solution as a pre-step before solving the OMCOS are: (a) reduces the number of routes for vessels; (b) simplifies offloading and unloading operations; (c) imposes rules for crude mixtures in clusters of storage tanks that minimize property variations; and (d) produces bounds on crude properties inside storage tanks that are used to linearize bilinear terms in blending constraints. Through the combination of clusters and a MILP-NLP decomposition, near optimal solutions were obtained for a set of representative instances of OMCOS at a reduced computational cost.