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

被引:27
作者
Banik, Debapriya [1 ]
Hossain, Niamat Ullah Ibne [2 ]
Govindan, Kannan [3 ]
Nur, Farjana [4 ]
Babski-Reeves, Kari [4 ]
机构
[1] Univ Texas El Paso, Dept Ind Mfg & Syst Engn IMSE, El Paso, TX 79968 USA
[2] Arkansas State Univ, Dept Engn Management, Jonesboro, AR 72401 USA
[3] Univ Southern Denmark, Ctr Sustainable Supply Chain Engn, Danish Inst Adv Study, Dept Technol & Innovat, Odense, Denmark
[4] Mississippi State Univ, Dept Ind & Syst Engn, Mississippi State, MS 39762 USA
关键词
Unmanned aerial vehicle (UAV); Medicals supplies; Drone selection; Graph theory and matrix approach (GTMA); COVID-19; Disruptions; DRONE DELIVERY; FUZZY DEMATEL; TECHNOLOGY; LOGISTICS;
D O I
10.1108/IJLM-06-2021-0334
中图分类号
C93 [管理学];
学科分类号
12 ; 1201 ; 1202 ; 120202 ;
摘要
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.
引用
收藏
页码:473 / 496
页数:24
相关论文
共 72 条
[1]  
Adwibowo, 2021, ASSESSMENTS HEAVY LI, DOI [10.1101/2021.01.09.21249494, DOI 10.1101/2021.01.09.21249494]
[2]  
Anbaroglu B., 2017, Parcel delivery in an urban environment using unmanned aerial systems: a vision paper
[3]   Last mile delivery by drones: an estimation of viable market potential and access to citizens across European cities [J].
Aurambout, Jean-Philippe ;
Gkoumas, Konstantinos ;
Ciuffo, Biagio .
EUROPEAN TRANSPORT RESEARCH REVIEW, 2019, 11 (01)
[4]  
Aziz N F., 2016, ARPN Journal of Engineering and Applied Sciences, V11, P7217, DOI DOI 10.1109/TIE.2013.2297315
[5]   Battery-Aware Energy Model of Drone Delivery Tasks [J].
Baek, Donkyu ;
Chen, Yukai ;
Bocca, Alberto ;
Macii, Alberto ;
Macii, Enrico ;
Poncino, Massimo .
PROCEEDINGS OF THE INTERNATIONAL SYMPOSIUM ON LOW POWER ELECTRONICS AND DESIGN (ISLPED '18), 2018, :279-284
[6]  
Bamburry Dane, 2022, Design Management Review, P34, DOI 10.1111/drev.12315
[7]  
Bryan V., 2014, Reuters
[8]  
Bureau U.S.C., 2018, CENS URB RUR CLASS U
[9]   A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers [J].
Buyukozkan, Gulcin ;
Cifci, Gizem .
EXPERT SYSTEMS WITH APPLICATIONS, 2012, 39 (03) :3000-3011
[10]   Drone arc routing problems [J].
Campbell, James F. ;
Corberan, Angel ;
Plana, Isaac ;
Sanchis, Jose M. .
NETWORKS, 2018, 72 (04) :543-559