Cluster of Vulnerable Municipalities in Mexico to facilitate the Creation of Coordination Mechanisms in the Humanitarian Responses through Machine Learning Techniques

Authors

  • Diana Sánchez-Partida Department of Logistics and Supply Chain Management, Faculty of Engineering, UPAEP
  • MarÍa Beatriz Bernábe Loranca Benemérita Universidad Autónoma de Puebla
  • Jorge A. Ruiz-Vanoye Universidad Politecnica de Pachuca
  • Ricardo A. Barrera Camara Universidad Autónoma del Carmen

DOI:

https://doi.org/10.61467/2007.1558.2024.v15i3.458

Keywords:

Humanitarian Logistics Management, Stakeholder Coordination, Logistics System, Unsupervised Learning, Machine Learning, Medoid Algorithm, PAM algorithm, P-median

Abstract

Cluster of Vulnerable Municipalities in Mexico to facilitate the Creation of Coordination Mechanisms in the Humanitarian Responses through Machine Learning Techniques. Coordination among actors in the supply chain is essential for ensuring a well-organized, efficient, and effective response to humanitarian crises, ultimately leading to better outcomes for those affected by disasters. Effective coordination ensures that resources, information, and aid are distributed efficiently and promptly to those in need during humanitarian crises. It also helps optimize resource allocation, prevent duplication of efforts, and ensure that aid reaches the right places at the right time. It supports identifying and mitigating risks in the supply chain, such as delays, bottlenecks, or disruptions, which can impact aid delivery. Coordination fosters better communication among stakeholders, enabling them to share information, collaborate on solutions, and make informed decisions. Precise coordination mechanisms help establish accountability among actors, ensuring that responsibilities are defined, monitored, and fulfilled. This document proposes an efficient logistics system capable of providing aid in a rapid and coordinated manner through unsupervised learning and a medoid partitioning algorithm called PAM (Partitioning Around Medoids) that achieves a global optimum for grouping the vulnerable municipalities of Mexico using INECC data. In addition, the PAM algorithm was compared with the P-Median to check the efficiency of the global optimum.

Author Biography

MarÍa Beatriz Bernábe Loranca, Benemérita Universidad Autónoma de Puebla

9TH INTERNATIONAL SYMPOSIUM ON LANGUAGE & KNOWLEDGE ENGINEERING LKE 2024

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Published

2024-10-01

How to Cite

Sánchez-Partida, D., Bernábe Loranca, M. B., Ruiz-Vanoye, J. A., & Barrera Camara, R. A. (2024). Cluster of Vulnerable Municipalities in Mexico to facilitate the Creation of Coordination Mechanisms in the Humanitarian Responses through Machine Learning Techniques. International Journal of Combinatorial Optimization Problems and Informatics, 15(3), 28–42. https://doi.org/10.61467/2007.1558.2024.v15i3.458

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