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Resilient Supply Chain Under Risks: A Network and Structural Perspective | ||
| Interdisciplinary Journal of Management Studies (Formerly known as Iranian Journal of Management Studies) | ||
| دوره 14، شماره 4، دی 2021، صفحه 735-760 اصل مقاله (1.18 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/ijms.2020.306292.674139 | ||
| نویسندگان | ||
| Mohammad Hossein Dehghani Sadrabadi1؛ Abbasali Jafari Nodoushan2؛ Ali Bozorgi-Amiri* 3 | ||
| 1School of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran | ||
| 2Assistant Professor, Department of Industrial Engineering, Faculty of Engineering, Meybod University, Meybod, Iran | ||
| 3Associate Professor, School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran | ||
| چکیده | ||
| Constant development and change in the supply chain lead the system to meet various risks. Thus, a proper procedure should be adopted to cope with such issues. This study addresses a bi-objective model to design a resilient and robust forward supply chain under uncertainty and multiple disruptions. The investigated objective functions include minimizing the total cost and the total non-resiliency of the network, which is tackled using the ε-constraint method. Notably, resilience strategies and two-stage stochastic programming are respectively considered to cope with disruption and operational risks. Ultimately, some random numerical benchmarked examples are applied to the model to confirm the proposed formulation’s performance. The results indicate that considering risks in the system leads to increased costs, but it would be profitable in the long term. Notably, a resilient chain can prevent system failure and enhance capabilities to reduce risk exposure costs and damages. | ||
| کلیدواژهها | ||
| Resilient supply chain؛ Network non-resiliency؛ Stochastic programming؛ Disruption؛ Operational risk | ||
| مراجع | ||
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Adobor, H. (2019). Supply chain resilience: a multi-level framework. International Journal of Logistics Research and Applications, 22(6), 533-556. https://doi.org/10.1080/13675567.2018.1551483
Azad, N., Saharidis, G. K., Davoudpour, H., Malekly, H., & Yektamaram, S. A. (2013). Strategies for protecting supply chain networks against facility and transportation disruptions: An improved Benders decomposition approach. Annals of Operations Research, 210(1), 125-163. https://doi.org/10.1007/s10479-012-1146-x
Dehghani, E., Jabalameli, M. S., Jabbarzadeh, A., Pishvaee, M. S. J. C., & Engineering, C. (2018). Resilient solar photovoltaic supply chain network design under business-as-usual and hazard uncertainties. Computers & Chemical Engineering, 111, 288-310. https://doi.org/10.1016/j.compchemeng.2018.01.013
Dehghani Sadrabadi, M. H., Ghousi, R., & Makui, A. (2020). An enhanced robust possibilistic programming approach for forward distribution network design with the aim of establishing social justice: A real-world application. Journal of Industrial and Systems Engineering, 12(4), 76-106. https://doi.org/10.1016/j.cie.2018.10.001
Diabat, A., Jabbarzadeh, A., & Khosrojerdi, A. (2019). A perishable product supply chain network design problem with reliability and disruption considerations. International Journal of Production Economics, 212, 125-138. https://doi.org/10.1016/j.ijpe.2018.09.018
Elluru, S., Gupta, H., Kaur, H., & Singh, S. P. (2019). Proactive and reactive models for disaster resilient supply chain. Annals of Operations Research, 283(1-2), 199-224. https://doi.org/10.1007/s10479-017-2681-2
Garcia-Herreros, P., Wassick, J. M., & Grossmann, I. E. (2014). Design of resilient supply chains with risk of facility disruptions. Industrial & Engineering Chemistry Research, 53(44), 17240-17251. https://doi.org/10.1021/ie5004174
Ghavamifar, A., Makui, A., & Taleizadeh, A. A. (2018). Designing a resilient competitive supply chain network under disruption risks: A real-world application. Transportation Research Part E: Logistics and Transportation Review, 115, 87-109. https://doi.org/10.1016/j.tre.2018.04.014
Hajiaghaei-Keshteli, M., & Fard, A. M. F. (2019). Sustainable closed-loop supply chain network design with discount supposition. Neural Computing and Applications, 31(9), 5343-5377. https://doi.org/10.1007/s00521-018-3369-5
Hamdan, B., & Diabat, A. (2020). Robust design of blood supply chains under risk of disruptions using Lagrangian relaxation. Transportation Research Part E: Logistics and Transportation Review, 134, 101764. https://doi.org/10.1016/j.tre.2019.08.005
Hosseini-Motlagh, S.-M., Samani, M. R. G., & Saadi, F. A. (2020). A novel hybrid approach for synchronized development of sustainability and resiliency in the wheat network. Computers and Electronics in Agriculture, 168, 105095. https://doi.org/10.1016/j.compag.2019.105095
Hosseini, S., Ivanov, D., & Dolgui, A. (2019). Review of quantitative methods for supply chain resilience analysis. Transportation Research Part E: Logistics and Transportation Review, 125, 285-307. https://doi.org/10.1016/j.tre.2019.03.001
Machado, S. M., Paiva, E. L., & da Silva, E. M. (2018). Counterfeiting: Addressing mitigation and resilience in supply chains. International Journal of Physical Distribution & Logistics Management. https://doi.org/10.1108/IJPDLM-01-2017-0004
Mohammed, A., Harris, I., Soroka, A., & Nujoom, R. (2019). A hybrid MCDM-fuzzy multi-objective programming approach for a G-Resilient supply chain network design. Computers & Industrial Engineering, 127, 297-312. https://doi.org/10.1016/j.cie.2018.09.052
Nemati, Y., Madhoushi, M., & Safaei Ghadikolaei, A. (2017). Towards supply chain planning integration: Uncertainty analysis using fuzzy mathematical programming approach in a plastic forming company. Iranian Journal of Management Studies, 10(2), 335-364. https://doi.org/10.22059/ijms.2017.218842.672334
Nooraie, S. V., & Parast, M. M. (2016). Mitigating supply chain disruptions through the assessment of trade-offs among risks, costs and investments in capabilities. International Journal of Production Economics, 171, 8-21. https://doi.org/10.1016/j.ijpe.2015.10.018
Olivares-Benitez, E., Ríos-Mercado, R. Z., & González-Velarde, J. L. (2013). A metaheuristic algorithm to solve the selection of transportation channels in supply chain design. International Journal of Production Economics, 145(1), 161-172. https://doi.org/10.1016/j.ijpe.2013.01.017
Pavlov, A., Ivanov, D., Pavlov, D., & Slinko, A. (2019). Optimization of network redundancy and contingency planning in sustainable and resilient supply chain resource management under conditions of structural dynamics. Annals of Operations Research, 2019, 1-30. https://doi.org/10.1007/s10479-019-03182-6
Pettit, T. J., Croxton, K. L., & Fiksel, J. (2019). The evolution of resilience in supply chain management: A retrospective on ensuring supply chain resilience. Journal of Business Logistics, 40(1), 56-65. https://doi.org/10.1111/jbl.12202
Rahimi, M., Ghezavati, V., & Asadi, F. (2019). A stochastic risk-averse sustainable supply chain network design problem with quantity discount considering multiple sources of uncertainty. Computers & Industrial Engineering, 130, 430-449. https://doi.org/10.1016/j.cie.2019.02.037
Sabouhi, F., & Jabalameli, M. S. (2019). A stochastic bi-objective multi-product programming model to supply chain network design under disruption risks. Journal of Industrial and Systems Engineering, 12(3), 196-209. https://doi.org/10.1080/00207543.2018.1461950
Shi, D. (2004). A review of enterprise supply chain risk management. Journal of Systems Science and Systems Engineering, 13(2), 219-244. https://doi.org/10.1007/s11518-006-0162-2
Siar, M. V., & Roghanian, E. (2020). Resilient mixed supply chain network redesign under operational and disruption risks: A case study. Journal of Industrial Engineering Research in Production Systems, 8(16), 113-135. https://doi.org/10.22084/ier.2020.20467.1915
Sreedevi, R., & Saranga, H. (2017). Uncertainty and supply chain risk: The moderating role of supply chain flexibility in risk mitigation. International Journal of Production Economics, 193, 332-342. https://doi.org/10.1016/j.ijpe.2017.07.024
Tomlin, B. (2006). On the value of mitigation and contingency strategies for managing supply chain disruption risks. Management Science, 52(5), 639-657. https://doi.org/10.1287/mnsc.1060.0515
Tucker, E. L., Daskin, M. S., Sweet, B. V., & Hopp, W. J. (2020). Incentivizing resilient supply chain design to prevent drug shortages: policy analysis using two-and multi-stage stochastic programs. IISE Transactions, 52(4), 394-412. https://doi.org/10.1080/24725854.2019.1646441
Yan, S., & Ji, X. (2020). Supply chain network design under the risk of uncertain disruptions. International Journal of Production Research, 58(6), 1724-1740. https://doi.org/10.1080/00207543.2019.1696999
Zahiri, B., Zhuang, J., & Mohammadi, M. (2017). Toward an integrated sustainable-resilient supply chain: A pharmaceutical case study. Transportation Research Part E: Logistics and Transportation Review, 103, 109-142. https://doi.org/10.1016/j.tre.2017.04.009
Zhao, J., & Ke, G. Y. (2019). Optimizing Emergency Logistics for the Offsite Hazardous Waste Management. Journal of Systems Science and Systems Engineering, 28(6), 747-765. https://doi.org/10.1007/s11518-019-5429-5
Zhen, L., Zhuge, D., & Lei, J. (2016). Supply chain optimization in context of production flow network. Journal of Systems Science and Systems Engineering, 25(3), 351-369. https://doi.org/10.1007/s11518-016-5304-6
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