This paper presents a hybrid approach, composed of a genetic algorithm and a linear programming method, to achieve an efficient pipeline network operation. The pipeline network optimization consists of the determination of pump scheduling over a short-term horizon, usually one or more days ahead. The resulting mathematical problem has a dynamic and combinatorial characteristic, in which a sub-optimal solution was obtained through these two mathematical tools in a short computational time. The approach was applied in a Pipeline Network to a study case based on the Patagonia Argentina, which is comprised of 16 tanks and linked pumps, with 66 kilometers of pipelines, that transport the production of more than 100 wells to a pre-processing plant. The goal was to obtain a constant input flow rate at the plant respecting physical and chemical processes requirements.

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