Abstract
This paper presents a hybrid of genetic programming (GP) and reference-point-based non-dominated sorting genetic algorithm (R-NSGA-II) for the multi-objective optimization of an aluminum torch brazing process for the fabrication of condensers for the automotive industry. The objectives to be optimized are a vacuum leakage test (quality of product), cycle time, and energy consumption (production cost). GP is used to find a mathematical model that describes the relationship between input and output process parameters. Thereafter, reference-point-based NSGA-II procedure is employed to provide the decision maker with a set of solutions close to her/his preferences. Results show that this approach may support the decision makers of the process to set the optimal input process parameters in order to achieve competitive advantages in quality and production costs.
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