A data-driven robust optimization model by cutting hyperplanes on vaccine access uncertainty in COVID-19 vaccine supply chain. (July 2022) - Gilani, Hani; Sahebi, Hadi
Highlights: Developing a COVID-19 vaccine supply chain based on domestic/foreign supply network. Analyzing environmental effects based on ReCiPe measures in SimaPro application. Estimating COVID-19 vaccine supply chain social effects based on ISO2600 in three indicators. Addressing robust models' high conservatism through a robust data-driven model. Using cutting hyper-planes to implement more accurate and realistic data. Validating Robust proposed model by comparing it with the box and polyhedral uncertainty set. Abstract: The worldwide COVID-19 pandemic sparked such a wave of concern that made access to vaccines more necessary than before. As the vaccine inaccessibility in developing countries has made pandemic eradication more difficult, this study has presented a mathematical model of a sustainable SC for the COVID-19 vaccine that covers the economic, environmental and social aspects and provides vaccine both domestically and internationally. It has also proposed a robust data-driven model based on a polyhedral uncertainty set to address the unjust worldwide vaccine distribution as an uncertain parameter. It is acceptably robust and is also less conservative than its classical counterparts. For validation, the model has been implemented in a real case in Iran, and the results have shown that it is 21% less conservative than its classical rivals (Box and Polyhedral convex uncertainty sets) in facing the uncertain parameter. As a result, the model proposes the construction of two domestic vaccine production centers, including Pasteur Institute and Razi Institute, and five foreign distributors in Tehran, Isfahan, Ahvaz, Kermanshah, and Bandar Abbas strategically.