In many, if not most, optimization problems, industrialists are often confronted with multiobjective decision problems. For example, in manufacturing processes, it may be necessary to optimize several criteria to take into account all the market constraints. So, the purpose is to choose the best tradeoffs among all the defined and conflicting objectives. In multicriteria optimization, after the decision maker has chosen all his objectives, he has to determine the multicriteria optimal zone by using the concept of domination criterion called Pareto domination. Two points in the research domain are compared. If one is better for all attributes, it is a nondominating solution. All the nondominating points form the Pareto's region. In this paper, several multiobjective optimization algorithms are used to obtain this zone. These methods are based on a diploid genetic algorithm and are compared to an industrial application: food granulation. In the optimal zone, the decision maker has to choose the best solution after he has made a ranking with all potential solutions. A partition is made and the decision maker has more information on the process. Finally, a decision support system shell is developed in order to classify all solutions.
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