.png){kind=icon}
.png){kind=icon}
Return
COVID-19 repatriation programs - Classification and optimization models
.png){kind=icon}
OA
AI
DOI：10.1016/j.trip.2021.100499.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Repatriation
COVID-19
Transportation
Scheduling
Optimization
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.8
Papers:
2.0K
Citations:
4.6K