Drone selection for disaster responses: an application of the stratified-best-worst method

What is it about?

The optimal selection of drones for disaster response operations poses significant challenges due to the dynamic and uncertain nature of such disasters. The traditional static models fail to adequately capture the associated complexities, necessitating a more resilient and adaptable decision-making framework that can address the uncertainties. This study employs an integrated approach combining the Best-Worst Method (BWM) with Stratified Multi-Criteria Decision-making (SMCDM) to evaluate and optimally select drones for disaster response. The methodology involves identifying seven essential criteria for drone evaluation following contingency theory. BWM is used to derive the optimal weights for each criterion by comparing the best and worst alternatives. Various uncertainty scenarios, such as weather conditions and communication challenges, are incorporated to perform SMCDM. Sensitivity analysis is conducted to assess the robustness of the decisions under different criteria weightings and operational scenarios. The analysis reveals that Drone-C consistently outperforms others due to its robust payload handling capability and adaptability, making it the optimal choice under most scenarios. The practical application of this integrated method in the context of the Kerala Flood demonstrates its superior performance in capturing the complexities of disaster response compared with the traditional methods. It also highlights the effectiveness of the proposed advanced decision-making approach, advocating its adoption for disaster response planning and operations.

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