  In this study, we addressed the refugee crisis through two main models. For
predicting the ultimate number of refugees, we first established a Logistic
Regression Model, but due to the limited data points, its prediction accuracy
was suboptimal. Consequently, we incorporated Gray Theory to develop the Gary
Verhulst Model, which provided scientifically sound and reasonable predictions.
Statistical tests comparing both models highlighted the superiority of the Gary
Verhulst Model. For formulating refugee allocation schemes, we initially used
the Factor Analysis Method but found it too subjective and lacking in rigorous
validation measures. We then developed a Refugee Allocation Model based on the
Analytic Hierarchy Process (AHP), which absorbed the advantages of the former
method. This model underwent extensive validation and passed consistency
checks, resulting in an effective and scientific refugee allocation scheme. We
also compared our model with the current allocation schemes and proposed
improvements. Finally, we discussed the advantages and disadvantages of our
models, their applicability, and scalability. Sensitivity analysis was
conducted, and directions for future model improvements were identified.
