REA Press Null REA Press3042-30663042-3066 REA Press https://doi.org/10.48313/scodm.v2i3.40 Research Article Supplier selection, Automotive industry, Multi-objective genetic algorithm, Non-dominated sorting genetic algorithm II. Applying A Multi-Objective Genetic Optimization Algorithm to Select Automotive Parts SuppliersApplying A Multi-Objective Genetic Optimization Algorithm to Select Automotive Parts Suppliers Kazemi Mina Department of Computer Engineering, Mala. C., Islamic Azad University, Malard, Iran. Mehrabi Abolghasem Department of Mechanical Engineering, Head of the Body Engineering Department, SAIPA, Tehran, Iran. Anbarzadeh Amin Department of Mechanical Engineering, Technical and Vocational University (TVU), Tehran, Iran. Farahani Zahra Department of Industrial Engineering, Engineering Expert of the Department of Body Engineering, SAIPA, Tehran, Iran. 09 2025 23 09 2025 2 3 © 2025 REA Press 2025

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 Applying A Multi-Objective Genetic Optimization Algorithm to Select Automotive Parts Suppliers

This paper proposes a multi-objective mathematical model to select the best suppliers of parts and products to improve vehicle quality and reduce costs. The results are presented in two sizes, and a sensitivity analysis of the demand parameter has been performed. For each of the medium and large sizes, the indices of the undefeated Non-Dominated Sorting Genetic Algorithm II (NSGA-II, including computational time, Maximum Spread Index (MSI), metric distance index, and the number of efficient solutions, have been calculated. The results show that the number of efficient solutions increases with problem size, indicating the high efficiency of the undefeated NSGA-II in finding efficient solutions for the supplier selection problem.

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