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A multi-criteria supplier evaluation and selection model without reducing the level of optimality

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Abstract

Traditional mathematical models for supplier evaluation and selection have primarily focused on cost-oriented perspectives, neglecting non-cost factors such as strategic and relational measures. However, such models have often compromised the optimality of solutions. Additionally, the use of genetic algorithms in these models has resulted in similar objective function outputs with minimal differences, making it challenging to differentiate between potential suppliers. To address these limitations, this study proposes a novel approach that employs multi-criteria decision-making (MCDM) techniques to evaluate suppliers without sacrificing optimality or imposing additional constraints. The proposed model utilizes hybrid algorithms and incorporates the weighted criteria obtained through the Best–Worst Method and the ranking and selection of alternatives using the Technique for Order of Preference by Similarity to Ideal Solution. By applying MCDM methods in the post-evaluation phase, this research introduces a unique approach that preserves solution optimality. The findings highlight that while the objective function outputs from the genetic algorithm implementations may be nearly identical, they can vary significantly across other criteria. This study demonstrates the strength of multi-criteria evaluation in overcoming the limitations of genetic algorithms without compromising solution optimality.

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Acknowledgements

We would like to express our heartfelt acknowledgement to the late Professor Adel Azar for his invaluable contributions to this paper. Professor Azar's expertise, guidance, and mentorship have greatly influenced the development and quality of our research. His profound knowledge, dedication, and passion for science, have been instrumental in shaping the ideas and direction of this study. Professor Adel Azar's untimely passing in August 2022 has left a void in the academic community, and we deeply mourn the loss of such a remarkable scholar and mentor. We will always cherish the memories of working with him, his insightful discussions, and his unwavering support throughout the research process. We extend our sincere gratitude to Professor Adel Azar for his significant contributions and lasting impact on this paper. His passion for knowledge and commitment to excellence will continue to inspire us in our future research endeavors. May his soul rest in peace.

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Authors and Affiliations

  1. M.Sc Industrial Management, Tarbiat Modares University, Tehran, Iran

    Moein Khazaei, Mohammad Ramezani & Arvin Fooladvand

  2. Tecnologico de Monterrey, Escuela de Ingeniería Y Ciencias, Puebla, Mexico

    Mostafa Hajiaghaei-Keshteli

  3. Faculty of Industrial Management, Tarbiat Modares University, Tehran, Iran

    Ali Rajabzadeh Ghatari & Adel Azar

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  1. Moein Khazaei
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Contributions

MK and AA contributed to conceptualization; MK and MH-K helped in methodology; MK done software; MR, ARG, and AA helped in validation; MH-K and MR contributed to formal analysis; AA and MK helped in investigation; MK, MH-K, and AA contributed to resources; MK helped in data curation; AA and MK contributed to writing—original draft preparation; MK, MH-K, AF, and MR contributed to writing; MK, AF, and MR helped in visualization; AA helped in supervision; AF and MK contributed to project administration and ARG revision. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Mostafa Hajiaghaei-Keshteli.

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Khazaei, M., Hajiaghaei-Keshteli, M., Rajabzadeh Ghatari, A. et al. A multi-criteria supplier evaluation and selection model without reducing the level of optimality. Soft Comput 27, 17175–17188 (2023). https://doi.org/10.1007/s00500-023-08954-8

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