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Related Experiment Video

Updated: Sep 30, 2025

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

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Published on: July 4, 2007

A mixed integer linear programming model and a basic variable neighbourhood search algorithm for the repatriation

Sameh Al-Shihabi^1,2, Nenad Mladenović³

¹Industrial Engineering and Engineering Management Department, University of Sharjah, PO Box 27272, Sharjah, United Arab Emirates.

Expert Systems with Applications

|March 17, 2022

Summary

Repatriation scheduling during COVID-19 prioritized vulnerable citizens. A new algorithm (BVNS) efficiently solved complex flight scheduling problems, outperforming exact methods in speed and solution quality for many cases.

Keywords:

Covid-19 Optimization Repatriation Scheduling Variable neighbourhood search

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Area of Science:

Operations Research
Public Health
Logistics

Background:

The COVID-19 pandemic severely disrupted commercial air travel in 2020.
Repatriation flights became essential for returning stranded citizens globally.
Traditional flight scheduling is reversed for repatriation, requiring new optimization approaches.

Purpose of the Study:

To address the Repatriation Scheduling Problem (RSP) prioritizing vulnerable individuals.
To develop and evaluate an efficient algorithm for optimizing repatriation logistics.
To balance flight capacity and quarantine availability for maximum repatriation.

Main Methods:

Formulated the RSP using a mixed-integer linear program (MILP).
Developed a Basic Variable Neighbourhood Search (BVNS) algorithm to solve the MILP model.
Tested the BVNS algorithm on 108 RSP instances, comparing results against an exact solver.

Main Results:

The BVNS algorithm achieved superior results compared to lower bounds for 62 instances.
BVNS provided comparable solutions for 17 instances.
BVNS demonstrated significant computational efficiency, running in 20 seconds versus 8 hours for the exact solver.

Conclusions:

The BVNS algorithm is a highly effective and efficient method for solving the Repatriation Scheduling Problem.
This approach can optimize the return of vulnerable populations during global crises.
The study highlights the potential of metaheuristic algorithms in complex, time-sensitive logistical challenges.