Open Access
Issue
RAIRO-Oper. Res.
Volume 57, Number 4, July-August 2023
Page(s) 2239 - 2265
DOI https://doi.org/10.1051/ro/2023107
Published online 18 September 2023
  • M. Alkahtani, M. Omair, Q.S. Khalid, G. Hussain, I. Ahmad and C. Pruncu, A covid-19 supply chain management strategy based on variable production under uncertain environment conditions. Int. J. Environ. Res. Publ. Health 18 (2021) 1662. [CrossRef] [Google Scholar]
  • S. Bhaskar, J. Tan, M.L. Bogers, T. Minssen, H. Badaruddin, S. Israeli-Korn and H. Chesbrough, At the epicenter of COVID-19 – the tragic failure of the global supply chain for medical supplies. Front. Publ. Health 8 (2020) 562882. [CrossRef] [Google Scholar]
  • K. Chen and L. Yang, Random yield and coordination mechanisms of a supply chain with emergency backup sourcing. Int. J. Prod. Res. 52 (2014) 4747–4767. [Google Scholar]
  • D.M. Desrochers, G.T. Gundlach and A.A. Foer, Analysis of antitrust challenges to category captain arrangements. J. Publ. Policy Market. 22 (2003) 201–215. [CrossRef] [Google Scholar]
  • L.N.K. Duong and J. Chong, Supply chain collaboration in the presence of disruptions: a literature review. Int. J. Prod. Res. 58 (2020) 3488–3507. [CrossRef] [Google Scholar]
  • B.C. Giri and S. Bardhan, Coordinating a supply chain under uncertain demand and random yield in presence of supply disruption. Int. J. Prod. Res. 53 (2015) 5070–5084. [Google Scholar]
  • B.C. Giri, S. Bardhan and T. Maiti, Coordinating a three-layer supply chain with uncertain demand and random yield. Int. J. Prod. Res. 54 (2016) 2499–2518. [CrossRef] [Google Scholar]
  • Y. Hegele and J. Schnabel, Federalism and the management of the COVID-19 crisis: centralisation, decentralisation and (non-) coordination. West Eur. Politics 44 (2021) 1052–1076. [CrossRef] [Google Scholar]
  • S. Hosseini and D. Ivanov, A multi-layer Bayesian network method for supply chain disruption modelling in the wake of the COVID-19 pandemic. Int. J. Prod. Res. 60 (2022) 5258–5276. [CrossRef] [Google Scholar]
  • K. Inderfurth and P. Kelle, Capacity reservation under spot market price uncertainty. Int. J. Prod. Econ. 133 (2011) 272–279. [CrossRef] [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]
  • P.K. Jha, S. Ghorai, R. Jha, R. Datt, G. Sulapu and S.P. Singh, Forecasting the impact of epidemic outbreaks on the supply chain: modelling asymptomatic cases of the COVID-19 pandemic. Int. J. Prod. Res. 61 (2023) 2670–2695. [CrossRef] [Google Scholar]
  • M. Jin and S.D. Wu, Capacity reservation contracts for high-tech industry. Eur. J. Oper. Res. 176 (2007) 1659–1677. [CrossRef] [Google Scholar]
  • S. Khalilpourazari and H. Hashemi Doulabi, Robust modelling and prediction of the COVID-19 pandemic in Canada. Int. J. Prod. Res. (2021) 1–17. [CrossRef] [Google Scholar]
  • S. Khalilpourazari, H.H. Doulabi, A.Ö. Çiftçioǧlu and G.W. Weber, Gradient-based grey wolf optimizer with Gaussian walk: application in modelling and prediction of the COVID-19 pandemic. Expert Syst. App. 177 (2021) 114920. [CrossRef] [Google Scholar]
  • S.K. Paul, R. Sarker and D. Essam, Managing risk and disruption in production-inventory and supply chain systems: a review. J. Ind. Manage. Optim. 12 (2016) 1009–1029. [Google Scholar]
  • S.K. Paul, M.A. Moktadir, K. Sallam, T.M. Choi and R.K. Chakrabortty, A recovery planning model for online business operations under the COVID-19 outbreak. Int. J. Prod. Res. 61 (2021) 2613–2635. [Google Scholar]
  • M.L. Ranney, V. Griffeth and A.K. Jha, Critical supply shortages – the need for ventilators and personal protective equipment during the COVID-19 pandemic. New England J. Med. 382 (2020) e41. [CrossRef] [PubMed] [Google Scholar]
  • T.G. Schmitt, S. Kumar, K.E. Stecke, F.W. Glover and M.A. Ehlen, Mitigating disruptions in a multi-echelon supply chain using adaptive ordering. Omega 68 (2017) 185–198. [CrossRef] [Google Scholar]
  • D.A. Serel, Capacity reservation under supply uncertainty. Comput. Oper. Res. 34 (2007) 1192–1220. [CrossRef] [Google Scholar]
  • E.B. Tirkolaee, A. Goli, P. Ghasemi and F. Goodarzian, Designing a sustainable closed-loop supply chain network of face masks during the COVID-19 pandemic: pareto-based algorithms. J. Cleaner Prod. 333 (2022) 130056. [CrossRef] [Google Scholar]
  • O. Torrealba-Rodriguez, R.A. Conde-Gutiérrez and A.L. Hernández-Javier, Modeling and prediction of COVID-19 in Mexico applying mathematical and computational models. Chaos Solitons Fractals 138 (2020) 109946. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]
  • H.L. Wu, J. Huang, C.J. Zhang, Z. He and W.K. Ming, Facemask shortage and the novel coronavirus disease (COVID-19) outbreak: reflections on public health measures. EClinicalMedicine 21 (2020) 100329. [CrossRef] [PubMed] [Google Scholar]
  • X. Xu, J. Shang, H. Wang and W.C. Chiang, Optimal production and inventory decisions under demand and production disruptions. Int. J. Prod. Res. 54 (2016) 287–301. [CrossRef] [Google Scholar]
  • V. Zakharov, Y. Balykina, O. Petrosian and H. Gao, CBRR model for predicting the dynamics of the COVID-19 epidemic in real time. Mathematics 8 (2020) 1727. [CrossRef] [Google Scholar]
  • M. Zheng, Y. Shu and K. Wu, On optimal emergency orders with updated demand forecast and limited supply. Int. J. Prod. Res. 53 (2015) 3692–3719. [CrossRef] [Google Scholar]
  • M. Zheng, J. Lin, X.M. Yuan and E. Pan, Impact of an emergency order opportunity on supply chain coordination. Int. J. Prod. Res. 57 (2019) 3504–3521. [CrossRef] [Google Scholar]
  • X. Zhou and Y. Li, Forecasting the COVID-19 vaccine uptake rate: an infodemiological study in the US. Human Vaccines Immunotherapeutics 18 (2022) 2017216. [CrossRef] [PubMed] [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.