Humanitarian management strategy for interstate movement of migrant workers in India during COVID-19 pandemic: An optimization based approach

Palit, Niladri; Chaudhuri, Atanu; Mishra, Nishikant

Abstract

India faced a unique situation during the ongoing COVID-19 pandemic when millions of migrant workers, in different states had to be transported to their home states as workplaces shut down. The governments in respective states faced challenges of minimizing economic impact while ensuring that the risk of infection was also kept under control. This paper develops models based on various secondary data from governmental and relevant non-governmental sources, trying to minimize the economic impact while keeping the rate of infection low and determining whether the migrant workforce should be allowed to stay in their workplace state or allowed to return to their home state. We found that the number of days of lockdown had a significant impact on the results. Fewer days of lockdown resulted in workers remaining in their work state as the preferred outcome, while a higher number of days of lockdown implied that people traveled to their home state and remain there. The proportion of workers who were willing to return to their work state played an important role on the results too. Beyond the threshold percentages of migrant workers returning to their work state, it became optimal for the government to encourage the workers to travel to their home state. However, this was mostly visible for moderate number of lockdown days as the effects on results were dominated by the impact from the number of lockdown days for too high or too low number of lockdown days. There is also an important trade-off between the budget and infection rate ‘R’ for the governments to consider. Minimizing the risk of infection requires an additional budget.

Citation

Palit, N., Chaudhuri, A., & Mishra, N. (online). Humanitarian management strategy for interstate movement of migrant workers in India during COVID-19 pandemic: An optimization based approach. Annals of Operations Research, https://doi.org/10.1007/s10479-023-05199-4

• Journal Article Type: Article
• Acceptance Date: Jan 18, 2023
• Online Publication Date: Feb 14, 2023
• Deposit Date: Feb 8, 2023
• Publicly Available Date: Apr 19, 2023
• Journal: Annals of Operations Research
• Print ISSN: 0254-5330
• Electronic ISSN: 1572-9338
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1007/s10479-023-05199-4
• Public URL: https://durham-repository.worktribe.com/output/1181353