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

Authors

  • Mina Kazemi Department of Computer Engineering, Mala. C., Islamic Azad University, Malard, Iran.
  • Abolghasem Mehrabi * Department of Mechanical Engineering, Head of the Body Engineering Department, SAIPA, Tehran, Iran.
  • Amin Anbarzadeh Department of Mechanical Engineering, Technical and Vocational University (TVU), Tehran, Iran.
  • Zahra Farahani Department of Industrial Engineering, Engineering Expert of the Department of Body Engineering, SAIPA, Tehran, Iran.

https://doi.org/10.48313/scodm.v2i3.40

Abstract

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.

Keywords:

Supplier selection, Automotive industry, Multi-objective genetic algorithm, Non-dominated sorting genetic algorithm II

Published

2025-05-19

Issue

Vol. 2 No. 3 (2025): Supply Chain and Operations Decision Making

Section

Articles

How to Cite

Kazemi, M. ., Mehrabi, A. ., Anbarzadeh, A. ., & Farahani, Z. . (2025). Applying A Multi-Objective Genetic Optimization Algorithm to Select Automotive Parts Suppliers. Supply Chain and Operations Decision Making, 2(3), 147-155. https://doi.org/10.48313/scodm.v2i3.40

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