ArticleSocio-economic planning sciences2023
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.
Article in Socio-economic planning sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- A bi-objective robust fuzzy programming model to configure a microgrid-based cement supply chain.Scientific reports · 2025Article
- A two-stage robust optimization model for emergency service facilities location-allocation problem under demand uncertainty and sustainable development.Scientific reports · 2025Article
- A fuzzy robust optimization model for dual objective forward and reverse logistics networks considering carbon emissions.PloS one · 2025Article
- A robust optimization model for multi-objective blood supply chain network considering scenario analysis under uncertainty: a multi-objective approach.Scientific reports · 2024Article
- Designing a sustainable-resilient-responsive supply chain network considering uncertainty in the COVID-19 era.Expert systems with applications · 2023Article
- Supply chain planning of vaccine and pharmaceutical clusters under uncertainty: The case of COVID-19.Socio-economic planning sciences · 2023Article
- Predicting and explaining absenteeism risk in hospital patients before and during COVID-19.Socio-economic planning sciences · 2023Article
- Designing a Resilient-Sustainable Supply Chain Network of Age-Differentiated Blood Platelets Using Vertical-Horizontal Transshipment and Grey Wolf Optimizer.International journal of environmental research and public health · 2023Article
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4 authors.
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Abstract
In uncertain circumstances like the COVID-19 pandemic, designing an efficient Blood Supply Chain Network (BSCN) is crucial. This study tries to optimally configure a multi-echelon BSCN under uncertainty of demand, capacity, and blood disposal rates. The supply chain comprises blood donors, collection facilities, blood banks, regional hospitals, and consumption points. A novel bi-objective Mixed-Integer Linear Programming (MILP) model is suggested to formulate the problem which aims to minimize network costs and maximize job opportunities while considering the adverse effects of the pandemic. Interactive possibilistic programming is then utilized to optimally treat the problem with respect to the special conditions of the pandemic. In contrast to previous studies, we incorporated socio-economic factors and COVID-19 impact into the BSCN design. To validate the developed methodology, a real case study of a Blood Supply Chain (BSC) is analyzed, along with sensitivity analyses of the main parameters. According to the obtained results, the suggested approach can simultaneously handle the bi-objectiveness and uncertainty of the model while finding the optimal number of facilities to satisfy the uncertain demand, blood flow between supply chain echelons, network cost, and the number of jobs created.
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