A Multi-Objective Programming Model for Supplier Evaluation and Selection in the Steel Industry Supply Chain (Case Study: Khouzestan Steel Company)

Authors

  • Shohreh Zangeneh Nejad Department of Industrial Engineering, Zand Institute of Higher Education, Shiraz, Iran.
  • Mehdi Abtahi * Department of Management, Marv.C., Islamic Azad University, Marvdasht.

https://doi.org/10.48314/ijorai.v1i2.68

Abstract

This research focuses on quantitative models for the selection and evaluation of suppliers in the supply chain. It is applied in nature, with the statistical population consisting of 12 experts from Khouzestan Steel Company. Based on this, a fuzzy mathematical model has been proposed for the selection and evaluation of suppliers, aiming to minimize returned goods, late transportation rates, order production costs, and raw material costs. Given the uncertainties present in real-world issues, the demand and capacity parameters, which may not have available or precise values, are considered as fuzzy trapezoidal numbers. Two methods have been employed: the fuzzy ranking method (Jiménez's method) for converting the fuzzy model into a deterministic model, and the  Linear Programming (LP)-metric method due to the multi-objective nature of the problem. The computational results obtained from solving the model show that, in the fuzzy model, due to the consideration of flexibility in the model's constraints using various α-cuts (in Jiménez's range method), the model becomes more flexible compared to the deterministic model, resulting in a better objective function value. Additionally, the results of the proposed model provide optimal values for returned goods, late transportation rates, order production costs, and raw materials, enabling managers to select the most suitable supplier. Furthermore, the calculations indicate that the model's fuzziness does not significantly increase computational complexity or problem-solving time.

Keywords:

Suppliers, Supply chain, Linear programming-metric, Fuzzy logic

References

  1. [1] Ganeshan, R., & Harrison, T. P. (1995). An introduction to supply chain management. 303 Beam Business Building Penn State University University Park. http://www.worms.ms.unimelb.edu.au/logo.html
  2. [2] Jain, V., Tiwari, M. K., & Chan, F. T. S. (2004). Evaluation of the supplier performance using an evolutionary fuzzy-based approach. Journal of manufacturing technology management, 15(8), 735–744. https://doi.org/10.1108/17410380410565320
  3. [3] Der Rhee, B., Verma, R., & Plaschka, G. (2009). Understanding trade-offs in the supplier selection process: The role of flexibility, delivery, and value-added services/support. International journal of production economics, 120(1), 30–41. https://doi.org/10.1016/j.ijpe.2008.07.024
  4. [4] Gottfredson, M., Puryear, R., & Phillips, S. (2005). Strategic sourcing: From periphery to the core. Harvard business review, 83(2), 132–139. https://europepmc.org/article/med/15724581
  5. [5] Wu, D. (2009). Supplier selection: A hybrid model using DEA, decision tree and neural network. Expert systems with applications, 36(5), 9105–9112. https://doi.org/10.1016/j.eswa.2008.12.039
  6. [6] Ghodsypour, S. H., & O’brien, C. (2001). The total cost of logistics in supplier selection, under conditions of multiple sourcing, multiple criteria and capacity constraint. International journal of production economics, 73(1), 15–27.
  7. [7] Wadhwa, V., & Ravindran, A. R. (2007). Vendor selection in outsourcing. Computers & operations research, 34(12), 3725–3737. https://doi.org/10.1016/j.cor.2006.01.009
  8. [8] Weber, C. A., Current, J. R., & Benton, W. C. (1991). Vendor selection criteria and methods. European journal of operational research, 50(1), 2–18. https://doi.org/10.1016/0377-2217(91)90033-R
  9. [9] Chen, C. T., Lin, C. T., & Huang, S. F. (2006). A fuzzy approach for supplier evaluation and selection in supply chain management. International journal of production economics, 102(2), 289–301. https://doi.org/10.1016/j.ijpe.2005.03.009
  10. [10] Çebi, F., & Bayraktar, D. (2003). An integrated approach for supplier selection. Logistics information management, 16(6), 395–400. https://doi.org/10.1108/09576050310503376
  11. [11] Goffin, K., Szwejczewski, M., & New, C. (1997). Managing suppliers: When fewer can mean more. International journal of physical distribution & logistics management, 27(7), 422–436. https://doi.org/10.1108/09600039710188486
  12. [12] Amid, A., Ghodsypour, S. H., & O’Brien, C. (2006). Fuzzy multiobjective linear model for supplier selection in a supply chain. International journal of production economics, 104(2), 394–407. https://doi.org/10.1016/j.ijpe.2005.04.012
  13. [13] Moghaddam, K. S. (2015). Fuzzy multi-objective model for supplier selection and order allocation in reverse logistics systems under supply and demand uncertainty. Expert systems with applications, 42(15–16), 6237–6254. https://doi.org/10.1016/j.eswa.2015.02.010
  14. [14] Beikkhakhian, Y., Javanmardi, M., Karbasian, M., & Khayambashi, B. (2015). The application of ISM model in evaluating agile suppliers selection criteria and ranking suppliers using fuzzy TOPSIS-AHP methods. Expert systems with applications, 42(15–16), 6224–6236. https://doi.org/10.1016/j.eswa.2015.02.035
  15. [15] Alireza, P. R., Reza, T. M., & Sadollah, E. N. (2012). Multi-product and multi-cycle multidimensional planning model-purchase-supply chain with fuzzy parameters. Journal of advances in industrial engineering, 46(2), 147–158.
  16. [16] Zeydan, M., Çolpan, C., & Çobanoğlu, C. (2011). A combined methodology for supplier selection and performance evaluation. Expert systems with applications, 38(3), 2741–2751. https://doi.org/10.1016/j.eswa.2010.08.064
  17. [17] Jolai, F., Yazdian, S. A., Shahanaghi, K., & Khojasteh, M. A. (2011). Integrating fuzzy TOPSIS and multi-period goal programming for purchasing multiple products from multiple suppliers. Journal of purchasing and supply management, 17(1), 42–53. https://doi.org/10.1016/j.pursup.2010.06.004
  18. [18] Shadkam, E., & Ghavidel, F. (2021). Presentation and implementation multi-objective mathematical models to balance the assembly line. International journal of research in industrial engineering, 10(1), 56–66. https://doi.org/10.22105/riej.2021.269298.1184
  19. [19] Kabadayi, N., & Dehghanimohammadabadi, M. (2022). Multi-objective supplier selection process: A simulation--optimization framework integrated with MCDM. Annals of operations research, 319(2), 1607–1629. https://doi.org/10.1007/s10479-021-04424-2
  20. [20] Shadkam, E. (2023). Investigating the multi-objective optimisation problem of supplier selection using the new COTOP hybrid method. International journal of business performance and supply chain modelling, 14(1), 79–105. https://doi.org/10.1504/IJBPSCM.2023.130468
  21. [21] Miyangaskary, M. K., Keivanpour, S., & Safari, H. (2025). A multi-objective optimization model of a closed-loop supply chain for supplier selection and order allocation under uncertainty: A case study of retail stores for protein products. Transportation research procedia, 82, 323–341. https://doi.org/10.1016/j.trpro.2024.12.046
  22. [22] Seifbarghy, M., Yargholi, F., & Hamidi, M. (2024). Modeling a stochastic multi-objective, multi-site, multi-mode, and multi-item supplier selection and order allocation problem. Journal of industrial and management optimization, 20(9), 2980–3003. https://doi.org/10.3934/jimo.2024037
  23. [23] Boran, F. E., Genç, S., Kurt, M., & Akay, D. (2009). A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method. Expert systems with applications, 36(8), 11363–11368. https://doi.org/10.1016/j.eswa.2009.03.039

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Published

2025-06-07

Issue

Vol. 1 No. 2 (2025): International Journal of Operations Research and Artificial Intelligence (IJORAI)

Section

Articles

How to Cite

Zangeneh Nejad, S. ., & Abtahi, M. . (2025). A Multi-Objective Programming Model for Supplier Evaluation and Selection in the Steel Industry Supply Chain (Case Study: Khouzestan Steel Company). International Journal of Operations Research and Artificial Intelligence , 1(2), 61-73. https://doi.org/10.48314/ijorai.v1i2.68