Finding Humanitarian Supply Chain Management Challenges using Uncertain MCDM Methodology

Kamal Hossain Gazi; Aditi Biswas; Tripti Basuri; Arijit Ghosh; Sankar Prasad Mondal

doi:10.31181/smeor21202548

Authors

Kamal Hossain Gazi Department of Applied Mathematics, Maulana Abul Kalam Azad University of Technology, West Bengal, 741249, India Author https://orcid.org/0000-0001-7179-984X
Aditi Biswas Department of Basic Science and Humanities, Greater Kolkata College of Engineering & Management, West Bengal, India Author https://orcid.org/0009-0001-1972-6536
Tripti Basuri Uttar Dum Dum Vidyapith (Girls) Primary Section, Birati, India Author https://orcid.org/0009-0001-8701-9040
Arijit Ghosh Department of Mathematics, St. Xavier’s College (Autonomous), Kolkata 700016, India Author https://orcid.org/0000-0001-7200-4180
Sankar Prasad Mondal Department of Applied Mathematics, Maulana Abul Kalam Azad University of Technology, West Bengal, 741249, India Author https://orcid.org/0000-0003-4690-2598

DOI:

https://doi.org/10.31181/smeor21202548

Keywords:

Humanitarian Supply Chain Management (HSCM), Challenges of Supply Chain, Triangular Fuzzy Set (TFS), CRITIC

Abstract

Challenges have emerged for Humanitarian Supply Chain Management (HSCM), a valid and prominent subject today. It primarily focuses on stakeholders, including governments, various non-governmental organisations (NGOs), donors, suppliers, and affected communities. Addressing these challenges enhances disaster response efficiency, resource allocation, and aid delivery. In this work, we have selected several criteria, namely funding, coordination, infrastructure, transparency, logistics, and sustainability. Triangular fuzzy numbers (TFNs) are used as a mathematical tool to handle uncertainty. Data sets are collected from two decision-makers who provide their decisions in linguistic terms, which are then converted into crisp numbers. The weight of the criteria is evaluated using a popular multi-criteria decision-making (MCDM) methodology, specifically the Criteria Importance Through Inter-criteria Correlation (CRITIC) method.

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