IMPROVING EFFICIENCY IN MASS PRODUCTION: APPLYING GENETIC ALGORITHMS IN CONJUNCTION WITH OBJECTIVE FUNCTIONS

Mircea TEODORESCU, Florin LUNGU, Radu VLAD

Abstract


In the dynamic context of the manufacturing industry, process optimization becomes essential for maintaining competitiveness. This article introduces an innovative methodology using genetic algorithms along with the objective function to address the specific challenges of mass production. By integrating the principles of genetic algorithms, we examine their capacity to model and optimize complex production processes, highlighting their applicability in the efficient design of assembly lines. Genetic algorithms are the ones that solve the objective function, offering a detailed perspective on how they can improve the accuracy of the final solutions. The case study presented demonstrates the practical application of this theoretical framework in a real production scenario, emphasizing significant improvements in efficiency and cost reduction. The results obtained validate the effectiveness of combining genetic algorithms with the Guinet objective function, proposing a promising direction for future research in the field of production optimization.


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References


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