Due to the high risk in the business environment, supply chains must adopt a tailored mechanism to deal with disruptions. This research proposes a multi-objective formulation to design a robust and resilient forward supply chain under multiple disruptions and uncertainty. The mentioned objective functions include minimizing the total cost, environmental impacts, and the network non-resiliency associated with the supply chain simultaneously countered using an augmented ε-constraint method. A Mulvey robust optimization approach is also utilized to deal with uncertainty. Ultimately, the developed model is validated based on three datasets associated with a case study of the steel industry. The results indicate that preventive and mitigation resilience strategies have significantly promoted the supply chain’s capabilities to deal with disruptions. Controlling network resiliency via non-resiliency measures has also created a risk-aware and robust structure in the incidence of disturbances. Numerical results reveal that multiple sourcing, lateral transshipment, and fortification of facilities will lead to the greatest cost-efficiency in the case study. Observations also indicate that the fortified supply chain will be highly economically viable in the long run due to the reduction of costs resulting from lost sales, unnecessary inventory holding, and the company’s credit risk.
Keywords: Resilient system, network non-resiliency, robust optimization, disruption, operational risk
@article{RO_2021__55_5_2827_0,
author = {Dehghani Sadrabadi, Mohammad Hossein and Ghousi, Rouzbeh and Makui, Ahmad},
title = {Designing a disruption-aware supply chain network considering precautionary and contingency strategies: a real-life case study},
journal = {RAIRO. Operations Research},
pages = {2827--2860},
year = {2021},
publisher = {EDP-Sciences},
volume = {55},
number = {5},
doi = {10.1051/ro/2021123},
mrnumber = {4313832},
language = {en},
url = {https://www.numdam.org/articles/10.1051/ro/2021123/}
}
TY - JOUR AU - Dehghani Sadrabadi, Mohammad Hossein AU - Ghousi, Rouzbeh AU - Makui, Ahmad TI - Designing a disruption-aware supply chain network considering precautionary and contingency strategies: a real-life case study JO - RAIRO. Operations Research PY - 2021 SP - 2827 EP - 2860 VL - 55 IS - 5 PB - EDP-Sciences UR - https://www.numdam.org/articles/10.1051/ro/2021123/ DO - 10.1051/ro/2021123 LA - en ID - RO_2021__55_5_2827_0 ER -
%0 Journal Article %A Dehghani Sadrabadi, Mohammad Hossein %A Ghousi, Rouzbeh %A Makui, Ahmad %T Designing a disruption-aware supply chain network considering precautionary and contingency strategies: a real-life case study %J RAIRO. Operations Research %D 2021 %P 2827-2860 %V 55 %N 5 %I EDP-Sciences %U https://www.numdam.org/articles/10.1051/ro/2021123/ %R 10.1051/ro/2021123 %G en %F RO_2021__55_5_2827_0
Dehghani Sadrabadi, Mohammad Hossein; Ghousi, Rouzbeh; Makui, Ahmad. Designing a disruption-aware supply chain network considering precautionary and contingency strategies: a real-life case study. RAIRO. Operations Research, Tome 55 (2021) no. 5, pp. 2827-2860. doi: 10.1051/ro/2021123
[1] , , , and , Strategies for protecting supply chain networks against facility and transportation disruptions: an improved Benders decomposition approach. Ann. Oper. Res. 210 (2013) 125–163. | MR | Zbl | DOI
[2] and , The price of robustness. Oper. Res. 52 (2004) 35–53. | MR | Zbl | DOI
[3] , and , Data-driven robust optimization. Math. Program. 167 (2018) 235–292. | MR | DOI
[4] , , and , 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. | DOI
[5] and , Supply-chain breakdown. MIT Sloan Manage. Rev. 46 (2004) 53–61.
[6] , , , and , Resilient solar photovoltaic supply chain network design under business-as-usual and hazard uncertainties. Comput. Chem. Eng. 111 (2018) 288–310. | DOI
[7] , and , Robust design and optimization of solar photovoltaic supply chain in an uncertain environment. Energy 142 (2018) 139–156. | DOI
[8] , and , 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.
[9] , and , Resilient supply chain under risks: a network and structural perspective. Iran. J. Manage. Stud. (2020). DOI: . | DOI
[10] , and , Multi-directional local search for sustainable supply chain network design. Int. J. Prod. Res. 59 (2021) 412–428. | DOI
[11] , and , A decision support framework to assess supply chain resilience. In: Proceedings of the 5th International ISCRAM Conference (2008).
[12] , and , 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. | DOI
[13] , and , Design of resilient supply chains with risk of facility disruptions. Ind. Eng. Chem. Res. 53 (2014) 17240–17251. | DOI
[14] , and , 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. | DOI
[15] , A review of multi-objective optimization: methods and its applications. Cogent Eng. 5 (2018) 1502242. | DOI
[16] and , Sustainable closed-loop supply chain network design with discount supposition. Neural Comput. App. 31 (2019) 5343–5377. | DOI
[17] and , Robust design of blood supply chains under risk of disruptions using Lagrangian relaxation. Transp. Res. Part E: Logistics Transp. Rev. 134 (2020) 101764. | DOI
[18] , and , A novel hybrid approach for synchronized development of sustainability and resiliency in the wheat network. Comput. Electron. Agric. 168 (2020) 105095. | DOI
[19] , and , 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. | DOI
[20] , and , Resilient and sustainable supply chain design: sustainability analysis under disruption risks. Int. J. Prod. Res. 56 (2018) 5945–5968. | DOI
[21] , , and , A robust optimization model for multi-site production planning problem in an uncertain environment.v Eur. J. Oper. Res. 181 (2007) 224–238. | Zbl | DOI
[22] , , and , A multi-objective optimization model for designing resilient supply chain networks. Int. J. Prod. Econ. 204 (2018) 174–185. | DOI
[23] , Effective implementation of the -constraint method in multi-objective mathematical programming problems. Appl. Math. Comput. 213 (2009) 455–465. | MR | Zbl
[24] , , and , A hybrid MCDM-fuzzy multi-objective programming approach for a G-Resilient supply chain network design. Comput. Ind. Eng. 127 (2019) 297–312. | DOI
[25] , and , Robust optimization of large-scale systems. Oper. Res. 43 (1995) 264–281. | MR | Zbl | DOI
[26] , , and , A robust augmented ε-constraint method (AUGMECON-R) for finding exact solutions of multi-objective linear programming problems. Oper. Res. (2020) 1–42. DOI: . | DOI
[27] , , and , 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. | DOI
[28] , , and , 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.
[29] and , 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. | DOI
[30] , and , A metaheuristic algorithm to solve the selection of transportation channels in supply chain design. Int. J. Prod. Econ. 145 (2013) 161–172. | DOI
[31] , , and , 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: . | DOI
[32] , and , Ensuring supply chain resilience: development of a conceptual framework. J. Bus. Logistics 31 (2010) 1–21. | DOI
[33] , , and , Developing a resilient supply chain strategy during “boom” and “bust”. Prod. Plan. Control 27 (2016) 579–590.
[34] , and , Supply chain resiliency: a review. In: Supply Chain Risk Management. Springer (2018) 215–235. | DOI
[35] , and , Resilient supply chain network design under competition: a case study. Eur. J. Oper. Res. 259 (2017) 1017–1035. | MR | DOI
[36] and , A stochastic bi-objective multi-product programming model to supply chain network design under disruption risks. J. Ind. Syst. Eng. 12 (2019) 196–209.
[37] , and , 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. | DOI
[38] , , and , A multi-cut L-shaped method for resilient and responsive supply chain network design. Int. J. Prod. Res. 58 (2020) 7353–7381. | DOI
[39] , Selection of resilient supply portfolio under disruption risks. Omega 41 (2013) 259–269. | DOI
[40] and , A supply chain view of the resilient enterprise. MIT Sloan Manage. Rev. 47 (2005) 41.
[41] , A review of enterprise supply chain risk management. J. Syst. Sci. Syst. Eng. 13 (2004) 219–244. | DOI
[42] and , Dual sourcing under disruption risk and cost improvement through learning. Eur. J. Oper. Res. 250 (2016) 226–238. | MR | DOI
[43] and , Uncertainty and supply chain risk: the moderating role of supply chain flexibility in risk mitigation. Int. J. Prod. Econ. 193 (2017) 332–342. | DOI
[44] , and , Resilient supplier selection and order allocation under operational and disruption risks. Transp. Res. Part E: Logistics Transp. Rev. 79 (2015) 22–48. | DOI
[45] , and , An enhanced risk assessment framework for business continuity management systems. Saf. Sci. 89 (2016) 201–218. | DOI
[46] , and , Sustainability in a lot-sizing and scheduling problem with delivery time window and sequence-dependent setup cost consideration. Sustainable Cities Soc. 51 (2019) 101718. | DOI
[47] , and , 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. | DOI
[48] , and , Toward an integrated sustainable-resilient supply chain: a pharmaceutical case study. Transp. Res. Part E: Logistics Transp. Rev. 103 (2017) 109–142. | DOI
[49] , and , Hub-and-spoke network design under operational and disruption risks. Transp. Res. Part E: Logistics Transp. Rev. 109 (2018) 20–43. | DOI
[50] and , Optimizing emergency logistics for the offsite hazardous waste management. J. Syst. Sci. Syst. Eng. 28 (2019) 747–765. | DOI
[51] , and , Supply chain optimization in context of production flow network. J. Syst. Sci. Syst. Eng. 25 (2016) 351–369. | DOI
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