Document Type : Original Article


1 M.A., Department of Industrial Management, Najafabad Branch, Islamic Azad University, Najafabad, Iran.

2 Assistant Professor, Department of Industrial Management, Najafabad Branch, Islamic Azad University, Najafabad, Iran.


The essential risks of suppliers increase complexity and vulnerability of supply chain and sometimes cause the disruptions. These risks should be predicted and to coping with them some solutions must be provided beforehand. It is aimed at identifying the disruptive risks in the supply chain of Foolad steel and then decreasing their potential effects for the next four periods. According to experts of the company, the graphite electrode is strategically the most important material as its risks disrupt the supply chain. The weighs of these risks were determined by using ANP and accordingly the most important risk was the risk of non-flexibility of suppliers with a weight of 0.5436. Other risks, the risk of long delivery orders, the risk of low quality and the risk of price increases, have the weights of 0.1911,0.1716 and 0.0937, respectively. Then, a multi-objective function model was developed that each objective function aims to minimize one risk. This model was solved by two methods, absolute priority method and goal programming, and finally the results were compared to each other.


1. Adeli, M., Zandieh, M. (2013). Presenting a simulation of optimization multi-objective approach for sourcing model and decision of integrating inventories. Journal of Industrial Management Perspective, 11, 89-110 (In Persian).
2. Aghajani, H., Samadi Miarkolaei, H., Farmanzadeh, M. (2014). Investigation and Evaluation of Supply Chain Resilience with Fuzzy Logic Approach (Empirical Evidence: Automobile Parts Manufacturing Industry in Mazandaran Province). Journal of Industrial Management Faculty of Humanities, 27, 83-94 (In Persian).
3. Azhmyakov, V., Fernández-Gutiérrez, J. P., & Gadi, S. K. (2016). A Novel Numerical Approach to the MCLP Based Resilient Supply Chain Optimization. IFAC-PapersonLine, 49(31), 137-142.
4. Barroso, A. P., Machado, V. H., & Machado, V. C. (2011). Supply chain resilience using the mapping approach. In Supply chain management. InTech.
5. Bittante, A., Pettersson, F., & Saxén, H. (2018). Optimization of a small-scale LNG supply chain. Energy, 148, 79-89.
6. Brusset, X., & Teller, C. (2017). Supply chain capabilities, risks, and resilience. International Journal of Production Economics, 184, 59-68.
7. Bunderson, J.S., & Sutcliffe, K.M. (2002). Comparing alternative conceptualization of functional diversity in management teams: process and performance effects. Academy of Management Journal, 45, 847-93.
8. Carvalho, H., & Machado, V. C. (2007). Designing principles to create resilient supply chains. In IIE Annual Conference. Proceedings (p. 186). Institute of Industrial and Systems Engineers (IISE).
9. Christopher, M., & Peck, H. (2004). Building the resilient supply chain. The international journal of logistics management, 15(2), 1-14.
10. Christopher, M. (2005). Managing risk in the supply chain. Supply Chain Practice, 7(2), 4.
11. Das, K. (2018). Integrating resilience in a supply chain planning model. International Journal of Quality & Reliability Management, 35(3), 570-595.
12. Datta, P. P., Christopher, M., & Allen, P. (2007). Agent-based modelling of complex production/distribution systems to improve resilience. International Journal of Logistics Research and Applications, 10(3), 187-203.
13. De Jong, S., Hoefnagels, R., Wetterlund, E., Pettersson, K., Faaij, A., & Junginger, M. (2017). Cost optimization of biofuel production–The impact of scale, integration, transport and supply chain configurations. Applied energy, 195, 1055-1070.
14. Falasca, M., Zobel, C. W., & Cook, D. (2008, May). A decision support framework to assess supply chain resilience. In Proceedings of the 5th International ISCRAM Conference (pp. 596-605).
15. Gaonkar, R. S., & Viswanadham, N. (2007). Analytical framework for the management of risk in supply chains. IEEE Transactions on automation science and engineering, 4(2), 265-273.
16. Ghazanfari, M., Riazi, A., Kazemi, M. (2001). Supply Chain Management. Journal of Tadbir, 117, 20-27 (In Persian).
17. Jabbarzadeh, A., Fahimnia, B., Sheu, J. B., & Moghadam, H. S. (2016). Designing a supply chain resilient to major disruptions and supply/demand interruptions. Transportation Research Part B: Methodological, 94, 121-149.
18. Ji, G., & Zhu, C. (2008, June). Study on supply chain disruption risk management strategies and model. In Service Systems and Service Management, 2008 International Conference on (pp. 1-6). IEEE.
19. Jüttner, U., & Maklan, S. (2011). Supply chain resilience in the global financial crisis: an empirical study. Supply Chain Management: An International Journal, 16(4), 246-259.
20. Kamalahmadi, M., & Parast, M. M. (2016). A review of the literature on the principles of enterprise and supply chain resilience: Major findings and directions for future research. International Journal of Production Economics, 171, 116-133.
21. Kim, Y., Chen, Y. S., & Linderman, K. (2015). Supply network disruption and resilience: A network structural perspective. Journal of operations Management, 33, 43-59.
22. Maheshwari, P., Singla, S., & Shastri, Y. (2017). Resiliency optimization of biomass to biofuel supply chain incorporating regional biomass pre-processing depots. Biomass and bioenergy, 97, 116-131.
23. Margolis, J. T., Sullivan, K. M., Mason, S. J., & Magagnotti, M. (2018). A multi-objective optimization model for designing resilient supply chain networks. International Journal of Production Economics, 204, 174-185.
24. Mavi, R. K., Goh, M., & Mavi, N. K. (2016). Supplier selection with Shannon entropy and fuzzy TOPSIS in the context of supply chain risk management. Procedia-Social and Behavioral Sciences, 235, 216-225.
25. Melnyk, S. A., Closs, D. J., Griffis, S. E., Zobel, C. W., and Macdonald, J. R. (2014). Understanding supply chain resilience. Supply Chain Management Review, 18(1), 34-41.
26. Mori, M., Kobayashi, R., Samejima, M., & Komoda, N. (2017). Risk-cost optimization for procurement planning in multi-tier supply chain by Pareto Local Search with relaxed acceptance criterion. European Journal of Operational Research, 261(1), 88-96.
27. Nguyen, D. H., & Chen, H. (2018). Supplier selection and operation planning in biomass supply chains with supply uncertainty. Computers & Chemical Engineering, 118, 103-117.
28. Pettit, T.J., Fiksel, J., Croxton, K.L. (2008). Ensuring supply chain resilience: Development of a conceptual framework. Journal of Business Logistics banner, 31(1), 1-21.
29. Pickett, C. (2006). Prepare for supply chain disruptions before they hit. Logistics Today, 47(6), 22-25.
30. Ponomarov, S. Y., & Holcomb, M. C. (2009). Understanding the concept of supply chain resilience. The international journal of logistics management, 20(1), 124-143.
31. Ponis, S. T., & Koronis, E. (2012). Supply chain resilience: definition of concept and its formative elements. Journal of Applied Business Research, 28(5), 921.
32. Rabieh, M., Azar, A., Modarres Yazdi, M., Fetanatfard Haghighi, M. (2011). Designing a robust multi–objective mathematical model of sourcing: an approach to reducing supply chain risks (Case study: Iran khodro supply chain). Journal of Industrial Management Perspective, 1, 57-77 (In Persian).
33. Rice, J. B., & Caniato, F. (2003). Building a secure and resilient supply network. Supply Chain Management Review, 7(5), 22-30.
34. Roberta Pereira, C., Christopher, M., & Lago Da Silva, A. (2014). Achieving supply chain resilience: the role of procurement. Supply Chain Management: an international journal, 19(5/6), 626-642.
35. Sampat, A. M., Martin, E., Martin, M., & Zavala, V. M. (2017). Optimization formulations for multi-product supply chain networks. Computers & Chemical Engineering, 104, 296-310.
36. Sheffi, Y. (2005). The resilient enterprise: overcoming vulnerability for competitive advantage. MIT Press Books, 1.
37. Soren, A., & Shastri, Y. (2018). Optimization based design of a resilient biomass to energy system. In Computer Aided Chemical Engineering, 43, 797-802.
38. Talebi, D., Ayron, F. (2015). Identifying supply chain risks and selecting suppliers using the network analysis process (The case study: Automobile industry). Journal of Industrial Management Perspective, 17, 31-43 (In Persian).
39. Tang, C. S. (2006). Robust strategies for mitigating supply chain disruptions. International Journal of Logistics: Research and Applications, 9(1), 33-45.
40. Zhang, C., Nemhauser, G., Sokol, J., Cheon, M. S., & Keha, A. (2018). Flexible solutions to maritime inventory routing problems with delivery time windows. Computers & Operations Research, 89, 153-162.
41. Zhao, S., Liu, X., & Zhuo, Y. (2017). Hybrid Hidden Markov Models for resilience metrics in a dynamic infrastructure system. Reliability Engineering & System Safety, 164, 84-97.