Técnicas evolutivas en problemas multi-objetivos en el proceso de planificación de la producción

planning in production environments takes care of designing, coordinating, managing and controlling all the operations existing in the use of productive systems. there are, in the framework analyzed within this work, several relevant multi-objective optimization problems (mops). they consist of several functions which tend to be complex and expensive to evaluate. multi-objective optimization is the discipline developed to provide solutions, called pareto optimal, for the simultaneous optimization of those functions. the costs of solving mops is due to the dimension of the problems, the combinatorial nature of the algorithms and the kind of objectives represented, linked to the efficiency of the system.. in the last decades several production-related mops have been handled successfully by means of genetic algorithms. here we will evaluate the performance of some particular genetic-based algorithms like nsgaii (non-dominated sorting genetic algorithm ii), speaii (strength pareto evolutionary algorithm ii) and their predecessors, nsga and spea, in the process of planning non-standardized production activities. after the experiment was carried out, the nsgaii algorithm proved to be more efficient.