Resilient supplier selection in complex products and their subsystem supply chains under uncertainty and risk disruption: A case study for satellite components

Document Type : Article

Authors

1 School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

2 Management and Industrial Engineering Department, Malek Ashtar University of Technology, Tehran, Iran

3 - School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran. - National Elites Foundation of Iran, Tehran, Iran

4 School of Industrial Engineering, Islamic Azad University, Science and Research Branch, Tehran, Iran

Abstract

Recently, the manufactures of complex product and its subsystems have faced a series of disruptions and troublesome behaviors in supplying goods and items. Likewise, suppliers in this area are more likely to be affected by external risks, in turn eventuating in disturbances. Selecting resilient and expedient suppliers dramatically decreases the delay time and costs and contributes to the competitiveness and development of the companies and organizations in this field. In this regard, this paper aims at proposing a bi-objective robust mathematical model to provide resilience supplier selection and order allocation for complex products and its subsystems in response to uncertainty and disruption risks. In the proposed model, a robust optimization approach is deployed, providing stable decisions for the proposed problem. Also, different resilience strategies including restoring supply from occurred disruptions, fortification of the suppliers, using backup suppliers, and utilizing the extra production capacity for suppliers have been devised to tolerate disruptions. Meanwhile, the augmented ε-constraint method is used, ensuring the optimal strong Pareto solutions and preventing the weak ones for the proposed bi-objective model. The evaluation of the effectiveness and desirability of the developed model is explored by discussing a real case study, via which helpful managerial insights are gained.

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Main Subjects

References

References:
1. Kiamehr, M., M. Hobday, and M. Hamedi, "Latecomer firm strategies in complex product systems (CoPS): The case of Iran’s thermal electricity generation systems", Research Policy, 44(6): p. 1240-1251 (2015).
2. Davies, A. and M. Hobday, "The business of projects: managing innovation in complex products and systems", Cambridge University Press (2005).
3. Acha, V., et al., "Exploring the capital goods economy: complex product systems in the UK", Industrial and Corporate Change, 13(3): p. 505-529 (2004)
4. Davies ,A. and T. Brady, "Policies for a complex product system", Futures, 30(4): p. 293- 304 (1998).
5. Özdemir, E.D., et al., "A confusion of tongues or the art of aggregating indicators— Reflections on four projective methodologies on sustainability measurement", Renewable and Sustainable Energy Reviews, 15(5): p. 2385-2396 (2011).
6. Hansen, K.L. and H. Rush, "Hotspots in complex product systems: emerging issues in innovation management", Technovation, 18(8-9): p. 555-590 (1998).
7. Du, B., et al., "A Pareto supplier selection algorithm for minimum the life cycle cost of complex product system", Expert Systems with Applications, 42(9): p. 4253-4264 (2015).
8. Solgi, O., et al., "Implementing an efficient data envelopment analysis method for assessing suppliers of complex product systems", Journal of Industrial and Systems Engineering, 12(2): p. 0-0 (2019).
9. Hongzhuan, C., et al. "The optimal cost-sharing incentive model of main manufacturer-suppliers for complex equipment under grey information. in Grey Systems and Intelligent Services", IEEE International Conference on. IEEE (2013).
10. Waters, D., "Supply chain risk management: vulnerability and resilience in logistics", Kogan Page Publishers, (2011).
11. Tate, W.L., K.J. Dooley, and L.M. Ellram, "Transaction cost and institutional drivers of supplier adoption of environmental practices", Journal of Business Logistics, 32(1): p. 6-16 (2011).
12. Sheffi, Y. and J.B. Rice Jr, "A supply chain view of the resilient enterprise", MIT Sloan management review, 47(1): p. 41 (2005).
13. Zhang, Y., et al., "A metaheuristic approach to the reliable location routing problem under disruptions", Transportation Research Part E: Logistics and Transportation Review, 83: p. 90-110 (2015).
14. Sadghiani, N.S., S. Torabi, and N. Sahebjamnia, "Retail supply chain network design under operational and disruption risks", Transportation Research Part E: Logistics and Transportation Review, 75: p. 95-114 (2015).
15. Torabi, S., M. Baghersad, and S. Mansouri, "Resilient supplier selection and order allocation under operational and disruption risks", Transportation Research Part E: Logistics and Transportation Review, 79: p. 22-48 (2015).
16. Jabbarzadeh, A., B. Fahimnia, and F. Sabouhi, "Resilient and sustainable supply chain design: sustainability analysis under disruption risks", International Journal of Production Research, p. 1-24 (2018).
17. Hasani, A. and A. Khosrojerdi, "Robust global supply chain network design under disruption and uncertainty considering resilience strategies: A parallel memetic algorithm for a real-life case study", Transportation Research Part E: Logistics and Transportation Review, 87: p. 20-52 (2016).
18. Garcia-Herreros, P., J.M. Wassick, and I.E. Grossmann, "Design of resilient supply chains with risk of facility disruptions", Industrial & Engineering Chemistry Research, 53(44): p. 17240-17251 (2014).
19. Ivanov, S.V. and M. Morozova, "Stochastic problem of competitive location of facilities with quantile criterion", Automation and Remote Control, 77(3): p. 451-461 (2016).
20. Torabi, S., H.R. Soufi, and N. Sahebjamnia, "A new framework for business impact analysis in business continuity management (with a case study)", Safety Science, 68: p. 309-323 (2014).
21. Zahiri, B., J. Zhuang, and M. Mohammadi, "Toward an integrated sustainable-resilient supply chain: A pharmaceutical case study", Transportation Research Part E: Logistics and Transportation Review, 103: p. 109-142 (2017).
22. Hosseini, S., et al., "Resilient Supplier Selection and Optimal Order Allocation Under Disruption Risks", International Journal of Production Economics, (2019).
23. Esmaeili-Najafabadi, E., et al., "A joint supplier selection and order allocation model with disruption risks in centralized supply chain", Computers & Industrial Engineering, 127: p. 734-748 (2019).
24. Hosseini, S ,.D. Ivanov, and A. Dolgui, "Review of quantitative methods for supply chain resilience analysis", Transportation Research Part E: Logistics and Transportation Review, 125: p. 285-307 (2019).
25. Parkouhi, S.V., A.S. Ghadikolaei, and H.F. Lajimi, "Resilient supplier selection and segmentation in grey environment", Journal of Cleaner Production, 207: p. 1123-1137 (2019).
26. Dehghani, E., et al., "Resilient solar photovoltaic supply chain network design under business-as-usual and hazard uncertainties", Computers & Chemical Engineering, 111: p. 288-310 (2018).
27. Meena, P. and S. Sarmah, "Multiple sourcing under supplier failure risk and quantity discount: A genetic algorithm approach", Transportation Research Part E: Logistics and Transportation Review, 50: p 84 - 97 . (2013).
28. Kamalahmadi, M. and M. Mellat-Parast, "Developing a resilient supply chain through supplier flexibility and reliability assessment", International Journal of Production Research, 54(1): p. 302-321 (2016).
29. Namdar, J., et al., "Supply chain resilience for single and multiple sourcing in the presence of disruption risks", International Journal of Production Research, 56(6): p. 2339-2360 (2018).
30. Leung, S.C., et al., "A robust optimization model for multi-site production planning problem in an uncertain environment", European journal of operational research, 181(1): p. 224-238 (2007).
31. Yu, C.-S. and H.-L. Li, "A robust optimization model for stochastic logistic problems", International journal of production economics, 64(1-3): p. 385-397 (2000).
32. De Rosa, V., et al., "Robust sustainable bi-directional logistics network design under uncertainty", International Journal of Production Economics, 145(1): p. 184-198 (2013).
33. Jabbarzadeh, A., B. Fahimnia, and S. Seuring, "Dynamic supply chain network design for the supply of blood in disasters: a robust model with real world application", Transportation Research Part E: Logistics and Transportation Review, 70: p. 225-244 (2014).
34. Mulvey, J.M., R.J. Vanderbei, and S.A. Zenios, "Robust optimization of large-scale systems", Operations research, 43(2): p. 264-281 (1995).
35. Hwang, C.-L. and A.S.M. Masud, "Multiple objective decision making—methods and applications: a state-of-the-art survey", Springer Science & Business Media, Vol. 164, (2012).
36. Mavrotas, G ,.Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Applied mathematics and computation, 213(2): p. 455-465 (2009).
37. Chowdhury, M.M.H. and M.A. Quaddus, A multiple objective optimization based QFD approach for efficient resilient strategies to mitigate supply chain vulnerabilities: The case of garment industry of Bangladesh☆,☆. Omega, 57: p. 5-21 (2015).
38. Azadeh, A., et al., Improved design of CMS by considering operators decision-making styles. International Journal of Production Research, 53(11): p. 3276-3287 (2015).
39. Esmaili, M., N. Amjady, and H.A. Shayanfar, Multi-objective congestion management by modified augmented ε-constraint method. Applied Energy, 88(3): p. 755-766 (2011).
Scientia Iranica
Volume 28, Issue 3 - Serial Number 3
Transactions on Industrial Engineering (E)
May and June 2021
Pages 1802-1816

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History

  • Receive Date: 28 December 2018
  • Revise Date: 11 May 2019
  • Accept Date: 02 September 2019

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