Genetic algorithms applied to scheduling and optimization of refinery operations

This paper presents a Genetic Algorithm-based method to optimize the production schedule of the fuel oil and asphalt section in a petroleum refinery. Two Genetic Algorithm models were developed to establish the sequence and size of all production shares. A special mutation operator was also proposed to minimize the number of changes in the production. A multi-objective fitness evaluation technique was also incorporated to the Genetic Algorithm models. The obtained results 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’s operational objectives.