EDP Sciences logo
Subscriber Authentication Point
Search
Menu
All issues Volume 58 / No 1 (January-February 2024) RAIRO-Oper. Res., 58 1 (2024) 591-612 References
Open Access
Issue
RAIRO-Oper. Res.
Volume 58, Number 1, January-February 2024
Page(s) 591 - 612
DOI https://doi.org/10.1051/ro/2023192
Published online 19 February 2024
  • S.M. Ali, M.H. Rahman, T.J. Tumpa, A.A. Moghul Rifat and S.K. Paul, Examining price and service competition among retailers in a supply chain under potential demand disruption. J. Retail. Consum. Serv. 40 (2018) 40–47. [CrossRef] [Google Scholar]
  • S. Asian and X.F. Nie, Coordination in supply chains with uncertain demand and disruption risks: existence, analysis, and insights. IEEE Trans. Syst. Man Cybern. Syst. 44 (2014) 1139–1154. [CrossRef] [Google Scholar]
  • R.C. Basole, S. Ghosh and M.S. Hora, Supply network structure and firm performance: evidence from the electronics industry. IEEE Trans. Eng. Manag. 65 (2018) 141–154. [CrossRef] [Google Scholar]
  • R.J. Basu, M.D. Abdulrahman and M. Yuvaraj, Improving agility and resilience of automotive spares supply chain: the additive manufacturing enabled truck model. Socio-Econ. Plan. Sci. 85 (2023) 101401. [CrossRef] [Google Scholar]
  • G. Behzadi, M.J. O’Sullivan, T.L. Olsen and A. Zhang, Allocation flexibility for agribusiness supply chains under market demand disruption. Int. J. Prod. Res. 56 (2018) 3524–3546. [Google Scholar]
  • Y. Cao, W.Y. Wei, L. Huang, H. Qiao and J. Du, Research on supply chain risk coping strategy based on fuzzy logic. J. Intell. Fuzzy Syst. 37 (2019) 4537–4546. [CrossRef] [Google Scholar]
  • S.R. Cardoso, A.P. Barbosa-Povoa, S. Relvas and A.Q. Novais, Resilience metrics in the assessment of complex supply-chains performance operating under demand uncertainty. Omega 56 (2015) 53–73. [CrossRef] [Google Scholar]
  • J. Chen and H. Wen, The application of complex network theory for resilience improvement of knowledge-intensive supply chains. Oper. Manag. Res. 16 (2023) 1140–1161. [CrossRef] [Google Scholar]
  • J.Z. Chen and H.F. Wang, A recovery strategy in manufacturing supply chains for long-term supply disruption, in 2021 Proceedings of the 40th Chinese Control Conference (CCC), IEEE (2021) 6487–6492. 10.23919/CCC52363.2021.9550232. [Google Scholar]
  • J.Z. Chen, H.F. Wang and Y.P. Fu, A multi-stage supply chain disruption mitigation strategy considering product life cycle during COVID-19. Environ. Sci. Pollut. Res. (2022). DOI: 10.1007/s11356-022-18931-7. [Google Scholar]
  • J.Z. Chen, H.F. Wang and R.Y. Zhong, A supply chain disruption recovery strategy considering product change under COVID-19. J. Manuf. Syst. 60 (2021) 920–927. [CrossRef] [Google Scholar]
  • T.Y. Choi, K.J. Dooley and M. Rungtusanatham, Supply networks and complex adaptive systems: control versus emergence. J. Oper. Manag. 19 (2001) 351–366. [CrossRef] [Google Scholar]
  • N. Choudhary, M. Ramkumar, T. Schoenherr and N.P. Rana, Assessing supply chain resilience during the pandemic using network analysis. IEEE Trans. Eng. Manag. (2021). DOI: 10.1109/Tem.2021.3124027. [Google Scholar]
  • J. Clausen, A. Larsen, J. Larsen and N.J. Rezanova, Disruption management in the airline industry – concepts, models and methods. Comput. Oper. Res. 37 (2010) 809–821. [CrossRef] [Google Scholar]
  • C.G. Corlu, B. Biller, E. Wolf and E. Yucesan. Inventory management with disruption risk, in 2020 Winter Simulation Conference (WSC), IEEE (2020) 2625–2636. [Google Scholar]
  • C.W. Craighead, J. Blackhurst, M.J. Rungtusanatham and R.B. Handfield, The severity of supply chain disruptions: design characteristics and mitigation capabilities. Decis. Sci. 38 (2007) 131–156. [CrossRef] [Google Scholar]
  • I.V. de Farias, S.L.D. Alvim, D. de Simas and E.M. Frazzon, Visibility model for enhancing supply chains resilience. IFAC Pap. 55 (2022) 2521–2525. [Google Scholar]
  • V. Dixit, P. Verma and M.K. Tiwari, Assessment of pre and post-disaster supply chain resilience based on network structural parameters with CVaR as a risk measure. Int. J. Prod. Econ. 227 (2020) 107655. [CrossRef] [Google Scholar]
  • A. Dolgui, D. Ivanov and B. Sokolov, Ripple effect in the supply chain: an analysis and recent literature. Int. J. Prod. Res. 56 (2018) 414–430. [CrossRef] [Google Scholar]
  • Y. He, S.S. Li, H. Xu and C.M. Shi, An in-depth analysis of contingent sourcing strategy for handling supply disruptions. IEEE Trans. Eng. Manag. 67 (2020) 201–219. [Google Scholar]
  • K.B. Hendricks and V.R. Singhal, An empirical analysis of the effect of supply chain disruptions on long-run stock price performance and equity risk of the firm. Prod. Oper. Manag. 14 (2005) 35–52. [CrossRef] [Google Scholar]
  • N.-O. Hohenstein, E. Feisel, E. Hartmann and L. Giunipero, Research on the phenomenon of supply chain resilience. Int. J. Phys. Distrib. Logist. Manag. 45 (2015) 90–117. [CrossRef] [Google Scholar]
  • K. Huang, J. Wang and J.X. Zhang, Automotive supply chain disruption risk management: a visualization analysis based on bibliometric. Processes 11 (2023) 710. [CrossRef] [Google Scholar]
  • R. Ishfaq, Resilience through flexibility in transportation operations. Int. J. Logist. Res. Appl. 15 (2012) 215–229. [CrossRef] [Google Scholar]
  • D. Ivanov, Structural dynamics and resilience in supply chain risk management. Springer International Publishing (2018). [CrossRef] [Google Scholar]
  • D. Ivanov, “A blessing in disguise” or “as if it wasn’t hard enough already”: reciprocal and aggravate vulnerabilities in the supply chain. Int. J. Prod. Res. 58 (2020) 3252–3262. [CrossRef] [Google Scholar]
  • D. Ivanov, Viable supply chain model: integrating agility, resilience and sustainability perspectives – lessons from and thinking beyond the COVID-19 pandemic. Ann. Oper. Res. 319 (2022) 1411–1431. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  • D. Ivanov and A. Dolgui, OR – methods for coping with the ripple effect in supply chains during COVID-19 pandemic: managerial insights and research implications. Int. J. Prod. Econ. 232 (2021) 107921. [CrossRef] [Google Scholar]
  • M. Kamalahmadi and M.M. Parast, A review of the literature on the principles of enterprise and supply chain resilience: major findings and directions for future research. Int. J. Prod. Econ. 171 (2016) 116–133. [CrossRef] [Google Scholar]
  • E. Katsoras and P. Georgiadis, An integrated system dynamics model for closed loop supply chains under disaster effects: the case of COVID-19. Int. J. Prod. Econ. 253 (2022) 108593. [CrossRef] [Google Scholar]
  • Y. Kim, Y.S. Chen and K. Linderman, Supply network disruption and resilience: a network structural perspective. J. Oper. Manag. 33–34 (2015) 43–59. [CrossRef] [Google Scholar]
  • S.S. Li, Y. He, H.F. Huang, J.Y. Lin and D. Ivanov, Supply chain hoarding and contingent sourcing strategies in anticipation of price hikes and product shortages. Iise Transactions (2023). DOI: 10.1080/24725854.2023.2184515. [Google Scholar]
  • S.S. Li, Y. He and L. Zhou, Dynamic sourcing strategies for supply disruptions under consumer stockpiling. Complex Intell. Syst. 8 (2022), 4543–4555. [CrossRef] [PubMed] [Google Scholar]
  • Y.H. Li and C.W. Zobel, Exploring supply chain network resilience in the presence of the ripple effect. Int. J. Prod. Econ. 228 (2020) 107693. [CrossRef] [Google Scholar]
  • Y.H. Li, C.W. Zobel, O. Seref and D. Chatfield, Network characteristics and supply chain resilience under conditions of risk propagation. Int. J. Prod. Econ. 223 (2020) 107529. [CrossRef] [Google Scholar]
  • M.F. Luthfiansyah and N.A. Masruroh, Evaluation of supply chain network resilience level in pre-disruption and post-disruption scenario, in 2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEE IEEM21), IEEE (2021) 167–171. [Google Scholar]
  • Z.H. Lv, L. Qiao, A. Mardani and H.B. Lv, Digital twins on the resilience of supply chain under COVID-19 pandemic. IEEE Trans. Eng. Manag. (2022). [Google Scholar]
  • C.M. Ma, Impacts of demand disruption and government subsidy on closed-loop supply chain management: a model based approach. Environ. Technol. Innov. 27 (2022) 102425. [CrossRef] [Google Scholar]
  • A. Majumdar, S.K. Sinha and K. Govindan, Prioritising risk mitigation strategies for environmentally sustainable clothing supply chains: Insights from selected organisational theories. Sustain. Prod. Consum. 28 (2021) 543–555. [CrossRef] [Google Scholar]
  • V.K. Manupati, S. Akash, K. Illaiah, E.S. Babu and M.L.R. Varela, Robust supply chain network design under facility disruption by consideration of risk propagation, in Innovations in Industrial Engineering. icieng 2021. Lecture Notes in Mechanical Engineering, Edited by J. Machado, F. Soares, J. Trojanowska, V. Ivanov. Springer, Cham (2022) 97–107. [Google Scholar]
  • N.A. Masruroh, R.K.E. Putra, Y.P. Mulyani and A.P. Rifai, Strategic insights into recovery from supply chain disruption: a multi-period production planning model. J. Oper. Res. Soc. 74 (2023) 1775–1799. [CrossRef] [Google Scholar]
  • P. Orenstein, The changing landscape of supply chain networks: an empirical analysis of topological structure. Infor 59 (2021) 53–73. [Google Scholar]
  • X. Qi, J.F. Bard and G. Yu, Supply chain coordination with demand disruptions. Omega 32 (2004) 301–312. [CrossRef] [Google Scholar]
  • Z. Sadeghi, O. Boyer, S. Sharifzadeh and N Saeidi, A robust mathematical model for sustainable and resilient supply chain network design: preparing a supply chain to deal with disruptions. Complexity 2021 (2021) 9975071. [CrossRef] [Google Scholar]
  • M.J. Sáenz, E. Revilla and B. Acero, Aligning supply chain design for boosting resilience. Bus. Horiz. 61 (2018) 443–452. [CrossRef] [Google Scholar]
  • T. Sawik, Stochastic optimization of supply chain resilience under ripple effect: a COVID-19 pandemic related study. Omega 109 (2022) 102596. [CrossRef] [Google Scholar]
  • K.S. Shahed, A. Azeem, S.M. Ali and M.A. Moktadir, A supply chain disruption risk mitigation model to manage COVID-19 pandemic risk. Environ. Sci. Pollut. Res. (2021). DOI: 10.1007/s11356-020-12289-4. [Google Scholar]
  • H.H. Shi and Y.D. Ni, Multiple strategies for supply chain to recover from major disruptions. J. Intell. Fuzzy Syst. 40 (2021) 9669–9686. [CrossRef] [Google Scholar]
  • Q. Sun, Y.Y. Gao, Q.H. Lu and Y.Y. Yan, A pricing and transshipment hybrid strategy for perishable product in two external scenarios for supply disruption. Kybernetes (2023). [Google Scholar]
  • M.Z. Wang and J.M. Yao, Intertwined supply network design under facility and transportation disruption from the viability perspective. Int. J. Prod. Res. 61 (2023) 2513–2543. [CrossRef] [Google Scholar]
  • J. Wu, Z.X. Chen and X. Ji, Sustainable trade promotion decisions under demand disruption in manufacturer-retailer supply chains. Ann. Oper. Res. 290 (2020) 115–143. [CrossRef] [MathSciNet] [Google Scholar]
  • T. Xiao and X. Qi, Price competition, cost and demand disruptions and coordination of a supply chain with one manufacturer and two competing retailers. Omega 36 (2008) 741–753. [CrossRef] [Google Scholar]
  • Q.H. Yang, C.M. Scoglio and D.M. Gruenbacher, Robustness of supply chain networks against underload cascading failures. Phys. A Stat. Mech. Appl. 563 (2021) 125466. [CrossRef] [Google Scholar]
  • T.J. Yang and W.G. Fan, Information management strategies and supply chain performance under demand disruptions. Int. J. Prod. Res. 54 (2016) 8–27. [CrossRef] [Google Scholar]
  • Y. Yang and C. Peng, A prediction-based supply chain recovery strategy under disruption risks. Int. J. Prod. Res. 61 (2023) 7670–7684. [CrossRef] [Google Scholar]
  • Q. Zhang, W.G. Fan, J.C. Lu, S.Q. Wu and X.C. Wang, Research on dynamic analysis and mitigation strategies of supply chains under different disruption risks. Sustainability 13 (2021) 2462. [CrossRef] [Google Scholar]
  • K. Zhao, Z. Zuo and J.V. Blackhurst, Modelling supply chain adaptation for disruptions: an empirically grounded complex adaptive systems approach. J. Oper. Manag. 65 (2019) 606–608. [Google Scholar]
  • Y.J. Zhao, H. Zhou and R. Leus, Recovery from demand disruption: two-stage financing strategy for a capital-constrained supply chain under uncertainty. Eur. J. Oper. Res. 303 (2022) 699–718. [CrossRef] [Google Scholar]
  • C.W. Zobel and L. Khansa, Quantifying cyberinfrastructure resilience against multi-event attacks. Decis. Sci. 43 (2012) 687–710. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.