ArticleTransportation research. Part E, Logistics and transportation review2020
A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19).
Article in Transportation research. Part E, Logistics and transportation review, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 131 papers.
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131 citing papers in PubMed, 627 citations in OpenAlex.
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- Optimization Modeling for Pandemic Vaccine Supply Chain Management: A Review and Future Research Opportunities.Naval research logistics · 2024Article
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- Review on Panic Buying Behavior during Pandemics: Influencing Factors, Stockpiling, and Intervention Strategies.Behavioral sciences (Basel, Switzerland) · 2024Review
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- System dynamics model for an agile pharmaceutical supply chain during COVID‑19 pandemic in Iran.PloS one · 2024Article
- Predicting the effects of supply chain resilience and robustness on COVID-19 impacts and performance: Empirical investigation through resources orchestration perspective.Journal of business research · 2023Article
- A dynamic approach to supply chain reconfiguration and ripple effect analysis in an epidemic.International journal of production economics · 2023Article
- Rapid cycle training for non-critical care physicians to meet intensive care unit staff shortage at an academic training center in a developing country during the COVID-19 pandemic.BMC medical education · 2023Article
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- Multi-mitigation strategies in medical supplies for epidemic outbreaks.Socio-economic planning sciences · 2023Article
- Modelling supply chain sustainability challenges in the food processing sector amid the COVID-19 outbreak.Socio-economic planning sciences · 2023Article
- Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study.Journal of business research · 2023Article
- Health Care Logistics Network Design and Analysis on Pandemic Outbreaks: Insights From COVID-19.Transportation research record · 2023Article
- Designing an optimization model for the vaccine supply chain during the COVID-19 pandemic.Expert systems with applications · 2023Article
71 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The disasters caused by epidemic outbreaks is different from other disasters due to two specific features: their long-term disruption and their increasing propagation. Not controlling such disasters brings about severe disruptions in the supply chains and communities and, thereby, irreparable losses will come into play. Coronavirus disease 2019 (COVID-19) is one of these disasters that has caused severe disruptions across the world and in many supply chains, particularly in the healthcare supply chain. Therefore, this paper, for the first time, develops a practical decision support system based on physicians' knowledge and fuzzy inference system (FIS) in order to help with the demand management in the healthcare supply chain, to reduce stress in the community, to break down the COVID-19 propagation chain, and, generally, to mitigate the epidemic outbreaks for healthcare supply chain disruptions. This approach first divides community residents into four groups based on the risk level of their immune system (namely, very sensitive, sensitive, slightly sensitive, and normal) and by two indicators of age and pre-existing diseases (such as diabetes, heart problems, or high blood pressure). Then, these individuals are classified and are required to observe the regulations of their class. Finally, the efficiency of the proposed approach was measured in the real world using the information from four users and the results showed the effectiveness and accuracy of the proposed approach.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.