Optimizing the production scheduling of a petroleum refinery through genetic algorthims

This paper presents a method, based on Genetic Algorithms, to optimize the production scheduling of the fuel oil and asphalt area in a petroleum refinery. The fuel oil and asphalt area is a multi-product plant, with two machine stages – one mixer and a set of tanks – with no setup time and with resource constraints in continuous operation. Two genetic algorithm models were developed to establish the sequence and size of all production shares. A special mutation operator – Neighborhood Mutation – is proposed to minimize the number of changes in the production. A multi-objective fitness evaluation technique, based on an energy minimization method, was also incorporated to the Genetic Algorithm models. The results obtained confirm that the proposed Genetic Algorithm models, associated with the multi-objective energy minimization method, are able to solve the scheduling problem, optimizing the refinery operational objectives.