Open Access
Issue
RAIRO-Oper. Res.
Volume 55, Number 5, September-October 2021
Page(s) 2827 - 2860
DOI https://doi.org/10.1051/ro/2021123
Published online 20 September 2021
  • N. Azad, G.K. Saharidis, H. Davoudpour, H. Malekly and S.A. Yektamaram, Strategies for protecting supply chain networks against facility and transportation disruptions: an improved Benders decomposition approach. Ann. Oper. Res. 210 (2013) 125–163. [Google Scholar]
  • D. Bertsimas and M. Sim, The price of robustness. Oper. Res. 52 (2004) 35–53. [Google Scholar]
  • D. Bertsimas, V. Gupta and N. Kallus, Data-driven robust optimization. Math. Program. 167 (2018) 235–292. [Google Scholar]
  • A. Bhattacharya, J. Geraghty, P. Young and P. Byrne, Design of a resilient shock absorber for disrupted supply chain networks: a shock-dampening fortification framework for mitigating excursion events. Prod. Plan. Control. 24 (2013) 721–742. [Google Scholar]
  • S. Chopra and M. Sodhi, Supply-chain breakdown. MIT Sloan Manage. Rev. 46 (2004) 53–61. [Google Scholar]
  • E. Dehghani, M.S. Jabalameli, A. Jabbarzadeh, M.S.J.C. Pishvaee and C. Engineering, Resilient solar photovoltaic supply chain network design under business-as-usual and hazard uncertainties. Comput. Chem. Eng. 111 (2018) 288–310. [Google Scholar]
  • E. Dehghani, M.S. Jabalameli and A. Jabbarzadeh, Robust design and optimization of solar photovoltaic supply chain in an uncertain environment. Energy 142 (2018) 139–156. [Google Scholar]
  • M.H. Dehghani Sadrabadi, R. Ghousi and A. Makui, An enhanced robust possibilistic programming approach for forward distribution network design with the aim of establishing social justice: a real-world application. J. Ind Syst. Eng. 12 (2019) 76–106. [Google Scholar]
  • M.H. Dehghani Sadrabadi, A. Jafari Nodoushan and A. Bozorgi-Amiri, Resilient supply chain under risks: a network and structural perspective. Iran. J. Manage. Stud. (2020). DOI: 10.22059/ijms.2020.306292.674139. [Google Scholar]
  • M. Eskandarpour, P. Dejax and O. Péton, Multi-directional local search for sustainable supply chain network design. Int. J. Prod. Res. 59 (2021) 412–428. [Google Scholar]
  • M. Falasca, C.W. Zobel and D. Cook, A decision support framework to assess supply chain resilience. In: Proceedings of the 5th International ISCRAM Conference (2008). [Google Scholar]
  • M. Fattahi, K. Govindan and E. Keyvanshokooh, Responsive and resilient supply chain network design under operational and disruption risks with delivery lead-time sensitive customers. Transp. Res. Part E: Logistics Transp. Rev. 101 (2017) 176–200. [Google Scholar]
  • P. Garcia-Herreros, J.M. Wassick and I.E. Grossmann, Design of resilient supply chains with risk of facility disruptions. Ind. Eng. Chem. Res. 53 (2014) 17240–17251. [Google Scholar]
  • A. Ghavamifar, A. Makui and A.A. Taleizadeh, Designing a resilient competitive supply chain network under disruption risks: a real-world application. Transp. Res. Part E: Logistics Transp. Rev. 115 (2018) 87–109. [Google Scholar]
  • N. Gunantara, A review of multi-objective optimization: methods and its applications. Cogent Eng. 5 (2018) 1502242. [Google Scholar]
  • M. Hajiaghaei-Keshteli and A.M.F. Fard, Sustainable closed-loop supply chain network design with discount supposition. Neural Comput. App. 31 (2019) 5343–5377. [Google Scholar]
  • B. Hamdan and A. Diabat, Robust design of blood supply chains under risk of disruptions using Lagrangian relaxation. Transp. Res. Part E: Logistics Transp. Rev. 134 (2020) 101764. [Google Scholar]
  • S.-M. Hosseini-Motlagh, M.R.G. Samani and F.A. Saadi, A novel hybrid approach for synchronized development of sustainability and resiliency in the wheat network. Comput. Electron. Agric. 168 (2020) 105095. [Google Scholar]
  • A. Jabbarzadeh, B. Fahimnia and S. Seuring, Dynamic supply chain network design for the supply of blood in disasters: a robust model with real world application. Transp. Res. Part E: Logistics Transp. Rev. 70 (2014) 225–244. [Google Scholar]
  • A. Jabbarzadeh, B. Fahimnia and F. Sabouhi, Resilient and sustainable supply chain design: sustainability analysis under disruption risks. Int. J. Prod. Res. 56 (2018) 5945–5968. [Google Scholar]
  • S.C. Leung, S.O. Tsang, W.-L. Ng and Y. Wu, A robust optimization model for multi-site production planning problem in an uncertain environment.v Eur. J. Oper. Res. 181 (2007) 224–238. [Google Scholar]
  • J.T. Margolis, K.M. Sullivan, S.J. Mason and M. Magagnotti, A multi-objective optimization model for designing resilient supply chain networks. Int. J. Prod. Econ. 204 (2018) 174–185. [Google Scholar]
  • G. Mavrotas, Effective implementation of the ε-constraint method in multi-objective mathematical programming problems. Appl. Math. Comput. 213 (2009) 455–465. [Google Scholar]
  • A. Mohammed, I. Harris, A. Soroka and R. Nujoom, A hybrid MCDM-fuzzy multi-objective programming approach for a G-Resilient supply chain network design. Comput. Ind. Eng. 127 (2019) 297–312. [Google Scholar]
  • J.M. Mulvey, R.J. Vanderbei and S.A. Zenios, Robust optimization of large-scale systems. Oper. Res. 43 (1995) 264–281. [Google Scholar]
  • A. Nikas, A. Fountoulakis, A. Forouli and H. Doukas, A robust augmented ε-constraint method (AUGMECON-R) for finding exact solutions of multi-objective linear programming problems. Oper. Res. (2020) 1–42. DOI: 10.1007/s12351-020-00574-6. [Google Scholar]
  • M. Nili, S.M. Seyedhosseini, M.S. Jabalameli and E. Dehghani, A multi-objective optimization model to sustainable closed-loop solar photovoltaic supply chain network design: a case study in Iran. Renew. Sustainable Energy Rev. 150 (2021) 111428. [Google Scholar]
  • M. Nili, S.M. Seyedhosseini, M.S. Jabalameli and E. Dehghani, An integrated model for designing a bi-objective closed-loop solar photovoltaic supply chain network considering environmental impacts: a case study in Iran. J. Ind. Syst. Eng. 13 (2021) 243–280. [Google Scholar]
  • S.V. Nooraie and M.M. Parast, Mitigating supply chain disruptions through the assessment of trade-offs among risks, costs and investments in capabilities. Int. J. Prod. Econ. 171 (2016) 8–21. [Google Scholar]
  • E. Olivares-Benitez, R.Z. Ros-Mercado and J.L. González-Velarde, A metaheuristic algorithm to solve the selection of transportation channels in supply chain design. Int. J. Prod. Econ. 145 (2013) 161–172. [Google Scholar]
  • A. Pavlov, D. Ivanov, D. Pavlov and A. Slinko, Optimization of network redundancy and contingency planning in sustainable and resilient supply chain resource management under conditions of structural dynamics. Ann. Oper. Res. (2019) 1–30. DOI: 10.1007/s10479-019-03182-6. [Google Scholar]
  • T.J. Pettit, J. Fiksel and K.L. Croxton, Ensuring supply chain resilience: development of a conceptual framework. J. Bus. Logistics 31 (2010) 1–21. [Google Scholar]
  • L. Purvis, S. Spall, M. Naim and V. Spiegler, Developing a resilient supply chain strategy during “boom” and “bust”. Prod. Plan. Control 27 (2016) 579–590. [Google Scholar]
  • S. Radhakrishnan, B. Harris and S. Kamarthi, Supply chain resiliency: a review. In: Supply Chain Risk Management. Springer (2018) 215–235. [Google Scholar]
  • S. Rezapour, R.Z. Farahani and M. Pourakbar, Resilient supply chain network design under competition: a case study. Eur. J. Oper. Res. 259 (2017) 1017–1035. [Google Scholar]
  • F. Sabouhi and M.S. Jabalameli, A stochastic bi-objective multi-product programming model to supply chain network design under disruption risks. J. Ind. Syst. Eng. 12 (2019) 196–209. [Google Scholar]
  • F. Sabouhi, M.S. Pishvaee and M.S. Jabalameli, Resilient supply chain design under operational and disruption risks considering quantity discount: a case study of pharmaceutical supply chain. Comput. Ind. Eng. 126 (2018) 657–672. [Google Scholar]
  • F. Sabouhi, M.S. Jabalameli, A. Jabbarzadeh and B. Fahimnia, A multi-cut L-shaped method for resilient and responsive supply chain network design. Int. J. Prod. Res. 58 (2020) 7353–7381. [Google Scholar]
  • T. Sawik, Selection of resilient supply portfolio under disruption risks. Omega 41 (2013) 259–269. [Google Scholar]
  • Y. Sheffi and J.B. Rice Jr, A supply chain view of the resilient enterprise. MIT Sloan Manage. Rev. 47 (2005) 41. [Google Scholar]
  • D. Shi, A review of enterprise supply chain risk management. J. Syst. Sci. Syst. Eng. 13 (2004) 219–244. [Google Scholar]
  • L. Silbermayr and S. Minner, Dual sourcing under disruption risk and cost improvement through learning. Eur. J. Oper. Res. 250 (2016) 226–238. [Google Scholar]
  • R. Sreedevi and H. Saranga, Uncertainty and supply chain risk: the moderating role of supply chain flexibility in risk mitigation. Int. J. Prod. Econ. 193 (2017) 332–342. [Google Scholar]
  • S. Torabi, M. Baghersad and S. Mansouri, Resilient supplier selection and order allocation under operational and disruption risks. Transp. Res. Part E: Logistics Transp. Rev. 79 (2015) 22–48. [Google Scholar]
  • S.A. Torabi, R. Giahi and N. Sahebjamnia, An enhanced risk assessment framework for business continuity management systems. Saf. Sci. 89 (2016) 201–218. [Google Scholar]
  • P. Vaez, F. Sabouhi and M.S. Jabalameli, Sustainability in a lot-sizing and scheduling problem with delivery time window and sequence-dependent setup cost consideration. Sustainable Cities Soc. 51 (2019) 101718. [Google Scholar]
  • P. Vaez, A. Jabbarzadeh and N. Azad, Designing a scheduling decision support system for the skin pass line: a case study of the steel finishing line. Proc. Inst. Mech. Eng. Part B: J. Eng. Manuf. 234 (2020) 1640–1655. [Google Scholar]
  • B. Zahiri, J. Zhuang and M. Mohammadi, Toward an integrated sustainable-resilient supply chain: a pharmaceutical case study. Transp. Res. Part E: Logistics Transp. Rev. 103 (2017) 109–142. [Google Scholar]
  • M. Zhalechian, S.A. Torabi and M. Mohammadi, Hub-and-spoke network design under operational and disruption risks. Transp. Res. Part E: Logistics Transp. Rev. 109 (2018) 20–43. [Google Scholar]
  • J. Zhao and G.Y. Ke, Optimizing emergency logistics for the offsite hazardous waste management. J. Syst. Sci. Syst. Eng. 28 (2019) 747–765. [Google Scholar]
  • L. Zhen, D. Zhuge and J. Lei, Supply chain optimization in context of production flow network. J. Syst. Sci. Syst. Eng. 25 (2016) 351–369. [Google Scholar]

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