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Abstract
Became the theory of optimization (Optimization Theory) represents a basic foundation in solving the problems of decision-making with a single objective and with multiple objectives even at the level of daily life, in light of the tremendous development in the IT (Information Technology) and the play of the provision of information about the problems and the development of generations of computers significant development which saves time and effort has led to the emergence of this development methods artificial intelligence (Artificial Intelligence Techniques) in solving complex problems .
And our belief in the inevitability of the use of scientific method in solving the problems of decision-making, whatever the problem under study, whether small or large size through the formulation of the decision-making problems in the mathematical model. We had the privilege of research and investigation in the field of programming Alhdwih Nazi nonlinear and describe ways to solve these problems using the method of artificial intelligence.
This thesis consists of six main chapters, these chapters can be described in the following manner:
Chapter 1 : This chapter consists of three sections : In section 1.1, an introduction to multi-objective decision making problem is introduced. In section 1.2 the classification of multi-objective methods. While in section 1.3 the classical search and optimization methods are presented.
Chapter 2 : In this chapter, in section 2.2 The different criteria upon which optimization, problems are classified, section 2.3 Describes a brief overview on genetic algorithms and some basic definitions used in the field of genetic algorithms and describes in details the mechanism of it for solving such problem. In section 2.4 Theoretical aspects about multiobjective optimization are presented.. In section 2.5 The presented of multicriteria decision aid methods implemented in genetic algorithms are presented.
Chapter 3 : In this chapter, the concept of goal programming is introduced in section 3.2. In section 3.3. Fuzzy set theory are presented. In section 3.4.The introduce the fuzzy number. While in section 3.5. The presented of nonlinear goal programming formulation are proposed. In section 3.6. The fuzzy goal programming formulation are presented. While in section 3.7. The Concluding Remarks.
Chapter 4 : In this chapter, in section 4.2. The formulation of dynamic Fuzzy Goal programming has been introduced. In section 4.3. The presented the Multi-stage Fuzzy Goal programming, and in section 4.4. The described of the mechanism of genetic algorithms. In section 4.5. The proposed algorithm for solving dynamic fuzzy goal programming. In section 4.6.Illustrative numerical examples are provided to clarify the main results. In section 4.7 the analysis of results are presented. Finally in section 4.8. The conclusion are presented.
Chapter 5: In this chapter in section 5.2. The application problem, while in section 5.3. The Formulation of fuzzy goal programming problem are presented. In section 5.4. An example to clarify the main results. In section 5.5. The analysis of results. Finally, in section 5.6. The conclusion are presented.
Chapter 6: This chapter is organized as follows: In section 6.2, the main conclusions of the research work presented in this dissertation are introduced. In section 6.3, recommendation for further research are offered.