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• W3200729100 abstract "This study proposes a sustainable closed-loop supply chain under uncertainty to create a response to the COVID-19 pandemic. In this paper, a novel stochastic optimization model integrating strategic and tactical decision-making is presented for the sustainable closed-loop supply chain network design problem. This paper for the first time implements the concept of sustainable closed-loop supply chain for the application of ventilators using a stochastic optimization model. To make the problem more realistic, most of the parameters are considered to be uncertain along with the normal probability distribution. Since the proposed model is more complex than majority of previous studies, a hybrid whale optimization algorithm as an enhanced metaheuristic is proposed to solve the proposed model. The efficiency of the proposed model is tested in an Iranian medical ventilator production and distribution network in the case of the COVID-19 pandemic. The results confirm the performance of the proposed algorithm in comparison with two other similar algorithms based on different multi-objective criteria. To show the impact of sustainability dimensions and COVID-19 pandemic for our proposed model, some sensitivity analyses are done. Generally, the findings confirm the performance of the proposed sustainable closed-loop supply chain for the pandemic cases like COVID-19." @default.
• W3200729100 created "2021-09-27" @default.
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• W3200729100 date "2021-09-14" @default.
• W3200729100 modified "2023-09-26" @default.
• W3200729100 title "Sustainable closed-loop supply chain network under uncertainty: a response to the COVID-19 pandemic" @default.
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• W3200729100 doi "https://doi.org/10.1007/s11356-021-16077-6" @default.
• W3200729100 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8438288" @default.
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