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
Yükleniyor...
Tarih
2022
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Ltd.
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
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.
Açıklama
Anahtar Kelimeler
Blood Supply Chain Network Design, COVID-19 Pandemic, Interactive Possibilistic Programming, Job Opportunity, Socio-economic Optimization, Uncertainty
Kaynak
Socio-Economic Planning Sciences
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
Cilt
Sayı
Künye
Tirkolaee, E. B., Golpîra, H., Javanmardan, A., & Maihami, R. (2022). 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, doi:10.1016/j.seps.2022.101439
