The establishment of an optimal migration model for refugees Based on Dijkstra algorithm

DOI

10.2991/iccia-17.2017.110How to use a DOI?

Keywords

Dijkstra algorithm, Bayes statistical model, Optimization.

Abstract

In recent years, the problem of large-scale migration of refugees has not only become an international hotspot that has plagued many countries around the world. More than a million migrants and refugees crossed into Europe in 2015, sparking a crisis as countries struggle to cope with the influx, and creating division in the EU over how best to deal with resettling people [1]. Because of the limited choice of refugees, and limited capacity of the recipient countries, the first step is to deal with the allocation of refugees in various routes to different countries. In the second step, a lot of external events will be added to interfere. At this point, it is needed to test whether the model can maintain stability under the interference of certain dynamic factors. Therefore, the basic model needs to meet the conditions of dynamic planning, and Dijkstra algorithm is chosen to optimize the migration route for the refugees. Its advantage is that when the dynamic interference occurs, only some parameters are adjusted slightly, and the basic model is still applicable. Bayes statistical model is used to predict the rate of refugees leaving the transit countries. And the proportion of leaving refugees at each point of action can be obtained. Then, the optimal allocation of the refugees can be calculated based on integer programming.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Mingyue Sun
PY  - 2016/07
DA  - 2016/07
TI  - The establishment of an optimal migration model for refugees Based on Dijkstra algorithm
BT  - Proceedings of the 2nd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2017)
PB  - Atlantis Press
SP  - 648
EP  - 651
SN  - 2352-538X
UR  - https://doi.org/10.2991/iccia-17.2017.110
DO  - 10.2991/iccia-17.2017.110
ID  - Sun2016/07
ER  -