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A decision support model for selecting unmanned aerial vehicle for medical supplies: context of COVID-19 pandemic

Debapriya Banik (Department of Industrial, Manufacturing and Systems Engineering (IMSE), The University of Texas at El Paso, El Paso, Texas, USA)
Niamat Ullah Ibne Hossain (Department of Engineering Management, Arkansas State University, Jonesboro, Arkansas, USA)
Kannan Govindan (Department of Technology and Innovation, Center for Sustainable Supply Chain Engineering, Danish Institute for Advanced Study, University of Southern Denmark, Odense, Denmark) (Yonsei Frontier Lab, Yonsei University, Seoul, Korea)
Farjana Nur (Department of Industrial and Systems Engineering, Mississippi State University, Mississippi State, Mississippi, USA)
Kari Babski-Reeves (Department of Industrial and Systems Engineering, Mississippi State University, Mississippi State, Mississippi, USA)

The International Journal of Logistics Management

ISSN: 0957-4093

Article publication date: 3 March 2022

Issue publication date: 14 March 2023

584

Abstract

Purpose

In recent times, due to rapid urbanization and the expansion of the E-commerce industry, drone delivery has become a point of interest for many researchers and industry practitioners. Several factors are directly or indirectly responsible for adopting drone delivery, such as customer expectations, delivery urgency and flexibility to name a few. As the traditional mode of delivery has some potential drawbacks to deliver medical supplies in both rural and urban settings, unmanned aerial vehicles can be considered as an alternative to overcome the difficulties. For this reason, drones are incorporated in the healthcare supply chain to transport lifesaving essential medicine or blood within a very short time. However, since there are numerous types of drones with varying characteristics such as flight distance, payload-carrying capacity, battery power, etc., selecting an optimal drone for a particular scenario becomes a major challenge for the decision-makers. To fill this void, a decision support model has been developed to select an optimal drone for two specific scenarios related to medical supplies delivery.

Design/methodology/approach

The authors proposed a methodology that incorporates graph theory and matrix approach (GTMA) to select an optimal drone for two specific scenarios related to medical supplies delivery at (1) urban areas and (2) rural/remote areas based on a set of criteria and sub-criteria critical for successful drone implementation.

Findings

The findings of this study indicate that drones equipped with payload handling capacity and package handling flexibility get more preference in urban region scenarios. In contrast, drones with longer flight distances are prioritized most often for disaster case scenarios where the road communication system is either destroyed or inaccessible.

Research limitations/implications

The methodology formulated in this paper has implications in both academic and industrial settings. This study addresses critical gaps in the existing literature by formulating a mathematical model to find the most suitable drone for a specific scenario based on its criteria and sub-criteria rather than considering a fleet of drones is always at one's disposal.

Practical implications

This research will serve as a guideline for the practitioners to select the optimal drone in different scenarios related to medical supplies delivery.

Social implications

The proposed methodology incorporates GTMA to assist decision-makers in order to appropriately choose a particular drone based on its characteristics crucial for that scenario.

Originality/value

This research will serve as a guideline for the practitioners to select the optimal drone in different scenarios related to medical supplies delivery.

Keywords

Acknowledgements

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Citation

Banik, D., Ibne Hossain, N.U., Govindan, K., Nur, F. and Babski-Reeves, K. (2023), "A decision support model for selecting unmanned aerial vehicle for medical supplies: context of COVID-19 pandemic", The International Journal of Logistics Management, Vol. 34 No. 2, pp. 473-496. https://doi.org/10.1108/IJLM-06-2021-0334

Publisher

:

Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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