Constrained GA applied to production and energy management of a pulp and paper mill

Abstract

Optimization tasks issued from real industrial problems are often characterized by being multicriteria, mixed, nonconvex, large scale, ill-defined [2][9][26]. In this work such a problem is obtained from the optimization of production scheduling and energy management in industrial complexes (in the case of a kraft pulp and paper mill). Consider two criteria, one of real variables issued from the energy optimization, and another of integer (logical) variables issued from production scheduling optimization, submitted to a high number of equality and inequality constraints [19][20]. To solve this problem it is proposed a strategy based on genetic algorithms. Computational results are presented to support discussion of the several developed techniques, namely selection methods, crossover and mutation operators, and diversification techniques. Results about the industrial relevance of the method are also presented, showing that genetic algorithms can solve important industrial problems although they need yet powerful computers to get answers in an interactive way.