Science Journal of Applied Mathematics and Statistics
Volume 2, Issue 1, February 2014, Pages: 14-19
Received: Nov. 13, 2013;
Published: Feb. 20, 2014
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Azzabi Lotfi, LASQUO/ISTIA/ University of Angers France
Ayadi Dorra, LASQUO/ISTIA/ University of Angers France
Bachar Kaddour, ESSCA Angers France
Kobi Abdessamad, LASQUO/ISTIA/ University of Angers France
Many present-day problems are multi-objective in nature and their solution requires consideration of conflicting objectives. Usually, they have a number of potentially Pareto-optimal solutions. An extensive knowledge of the problem is required in discriminating between solutions, eliminating the unwanted ones and accepting the required solution(s) by a decision making process. It is well known that multi-objective optimization model had found a lot of important applications in decision making problems such as in economics theory, management science and engineering design. Because of these applications, a lot of literatures have been published to study optimality conditions, duality theories and topological properties of solutions of multi-objective optimization problems. In the case of optimization problems, the idea of regularizing a problem by adding a strongly convex term to the objective function can actually be treated back at least. The regularization technique proved to be an invaluable tool in the solution of ill-posed problems, and an enormous amount of work has been devoted to its study. In this paper, a Multi-objective Optimization Problems formulation based on a Goal Programming Methods solves the multi-objective problem which can tackle relatively large test systems. This method is based on optimization of the most preferred objective and considering the other objectives as constraints.
Fuzzy Goal Programming to Optimization the Multi-Objective Problem, Science Journal of Applied Mathematics and Statistics.
Vol. 2, No. 1,
2014, pp. 14-19.
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