Evaluation of Humanitarian Supply Chain Resilience in Flood Disaster

Abstract

Frequent natural hazards such as flooding and the devastating consequences of severe events make the humanitarian supply chain particularly important in alleviating the suffering of those communities impacted by such events. However, the ambiguity of information and the different goals of stakeholders demand that the humanitarian supply chain must be resilient. This research adopts the use of literature review and expert opinions to identify the indicators that affect the resilience of the humanitarian supply chain using the flood event in Hechuan District, China in 2020 as an example. Based on the combination of fuzzy Decision-making Trial and Evaluation Laboratory and Analytic Network Process (fuzzy-DEMATEL-ANP), the interrelationships between the indicators and the weights of each indicator are calculated. The research results indicate that decision-makers in the humanitarian supply chain should vigorously coordinate the cooperation among stakeholders, ensure the effective transmission of information, and formulate forward-looking strategic plans. At the same time, these key decision makers should also be aware of the need to adjust their strategies at different stages of the flooding event in order to achieve a flexible humanitarian supply chain that responds to the varying demands over the course of a flooding event. The results of this study will help professionals involved in humanitarian supply chains to develop strategies and plans to become more resilient thus helping to reduce losses from natural hazards such as floods.

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“…Due to the scale of experts is very large and evaluation factors are many, opinions from experts with similar characteristics are often equally important. In the whole information elicitation step: (1) All the opinions in the small subgroup and the weight of each small subgroup are considered; (2) We always use fuzzy information without changing the form of the evaluation values, which maintain the consistency of the data; (3) Giving the same weight to the experts in the same small subgroup, avoiding the subjectivity in the aggregation process; (4) At the same time, weighted averaging operator is applied for calculating the comprehensive information, the weight is determined by the number of members in each cluster, which is objective and considers the diverse importance of clusters (Harsanyi 1955 ; Ribeiro et al 2003; Xu et al 2021b ; Li et al 2022a , 2022b ). Therefore, the above measures can efficiently reduce the biases of information aggregation process.…”

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