| dc.contributor.author | Bridgelall, Raj | |
| dc.description.abstract | In major metropolitan areas, the growing levels of congestion pose a significant risk of supply chain disruptions by hindering surface transportation of commodities. To address this challenge, cargo drones are emerging as a potential mode of transport that could improve the reliability of the pharmaceutical supply chain and enhance healthcare. This study proposes a novel hybrid workflow that combines machine learning and a geographic information system to identify the fewest locations where providers can initiate cargo drone services to yield the greatest initial benefits. The results show that by starting a service in only nine metropolitan areas across four regions of the contiguous United States, drones with a robust 400-mile range can initially move more than 28% of the weight of all pharmaceuticals. The medical community, supply chain managers, and policymakers worldwide can use this workflow to make data-driven decisions about where to access the largest opportunities for pharmaceutical transport by drones. The proposed approach can inform policies and standards such as Advanced Air Mobility to help address supply chain disruptions, reduce transportation costs, and improve healthcare outcomes. | en_US |
| dc.rights | In copyright. Permission to make this version available has been granted by the author and publisher. | |
| dc.title | Unlocking Drone Potential in the Pharma Supply Chain: A Hybrid Machine Learning and GIS Approach | en_US |
| dc.type | Article | en_US |
| dc.description | Raj Bridgelall is the program director for the Upper Great Plains Transportation Institute (UGPTI) Center for Surface Mobility Applications & Real-time Simulation environments (SMARTSeSM). | en_US |
| dc.date.accessioned | 2023-08-17T18:15:36Z | |
| dc.date.available | 2023-08-17T18:15:36Z | |
| dc.date.issued | 2023 | |
| dc.identifier.uri | https://hdl.handle.net/10365/33224 | |
| dc.subject | supply chain management | en_US |
| dc.subject | supply chain resilience | en_US |
| dc.subject | sustainable supply chain | en_US |
| dc.subject | unsupervised machine learning | en_US |
| dc.subject | urban planning | en_US |
| dc.subject | truck emissions | en_US |
| dc.subject | vertiport locating | en_US |
| dc.identifier.orcid | 0000-0003-3743-6652 | |
| dc.identifier.citation | Bridgelall, Raj. "Unlocking Drone Potential in the Pharma Supply Chain: A Hybrid Machine Learning and GIS Approach." Standards, DOI:10.3390/standards3030021, 3(3):283-296, September 2023. | en_US |
| dc.description.sponsorship | The U.S. Department of Transportation supported this work, grant number [Mountain Plains Consortium]. | en_US |
| dc.description.uri | https://www.ugpti.org/about/staff/viewbio.php?id=79 | |
| dc.language.iso | en_US | en_US |
| dc.rights.uri | https://rightsstatements.org/vocab/InC/1.0/ | |
| dc.contributor.organization | Upper Great Plains Transportation Institute | |
| ndsu.college | College of Business | |
| ndsu.department | Transportation and Logistics | |
| dc.identifier.doi | https://doi.org/10.3390/standards3030021 | |
