This paper presents an integrated approach for the solution of complex optimization problems in thermoscience research. The cited approach is based on the design of computational experiments (DOE), surrogate modeling, and optimization. The DOE/Surrogate modeling techniques under consideration include: A-Optimal/Classical Linear Regression, Latin Hypercube/Artificial Neural Networks, and Latin Hypercube/Sugeno-type Fuzzy Models. These techniques are coupled with both local (modified Newton’s method) and global (Genetic Algorithms) optimization methods. The proposed approach showed to be an effective, efficient, and robust, modeling and optimization tool in the context of a case study, and holds promise to be useful in larger scale optimization problems in thermoscience research.

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