ArticleSocio-economic planning sciences2022
Integrating GIS in reorganizing blood supply network in a robust-stochastic approach by combating disruption damages.
Article in Socio-economic planning sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Lessons learned from crises in blood donation services: A systematic review of operational practices.Vox sanguinis · 2026Pooled it
- Multi-objective two-stage emergent blood transshipment-allocation in COVID-19 epidemic.Complex & intelligent systems · 2023Article
- A new vaccine supply chain network under COVID-19 conditions considering system dynamic: Artificial intelligence algorithms.Socio-economic planning sciences · 2023Article
- A socio-economic optimization model for blood supply chain network design during the COVID-19 pandemic: An interactive possibilistic programming approach for a real case study.Socio-economic planning sciences · 2023Article
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4 authors.
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Abstract
As supplying adequate blood in multiple countries has failed due to the Covid-19 pandemic, the importance of redesigning a sensible protective-resilience blood supply chain is underscored. The outbreak-as an extensive disruption-has caused a delay in ordering and delivering blood and its by-products, which leads to severe social and financial loss to healthcare organizations. This paper presents a robust multi-phase optimization approach to model a blood supply network ensuring blood is collected efficiently. We evaluate the effectiveness of the model using real-world data from two mechanisms. Firstly, a Geographic Information System (GIS)-based method is presented to find potential alternative locations for blood donation centers to maximize availability, accessibility, and proximity to blood donors. Then, a protective mathematical model is developed with the incorporation of (a) blood perishability, (b) efficient collation centers, (c) multiple-source of suppliers, (d) back-up centers, (e) capacity limitation, and (f) uncertain demand. Emergency back-up for laboratory centers to supplement and offset the processing plants against the possible disorders is applied in a two-stage stochastic robust optimization model to maximize the level of hospitals' coverage. The results highlight the fraction cost of considering back-up facilities in the total costs and provide more resilient decisions with lower risks by examining resource limitations.
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