COVID-19 repatriation programs - Classification and optimization models.

Due to the COVID-19 pandemic and the subsequent measures to control the spread of the virus by border closure and suspension of commercial flights, decision-makers in several countries had to deal with one or more forms of repatriation problems, international and domestic. In addressing an international repatriation problem, a country must schedule flights to return its citizens. Typically, the flight schedules of commercial airlines are announced, and passengers buy their seats accordingly. However, in repatriation, the opposite happens: citizens express their wishes to return to their countries, and flights need to be scheduled. Due to several constraints, decision-makers should repatriate the most vulnerable citizens first. In this work, we offer an overview of repatriation problems from an optimization perspective by visiting real-life cases. We compare the Indian and Jordanian repatriation programs to find similarities and differences between the two. We also develop several mixed-integer linear programs (MILPs) to model different repatriation phases and solve respective illustrative examples to demonstrate the use of the MILPs. Last, we solve two stages of a problem that resembles Jordan's actual repatriation program. Decision-makers and researchers may use and extend this summary and the optimization models for any future pandemic that might lead to border closures and new repatriation problems.