With the evolution of technology, drones hold immense potential to address the last mile delivery challenges in e-commerce. However, a holistic and systematic literature review of all the studies and an understanding of the interrelationships between the different dimensions of drone-based last mile delivery, such as customer expectations, stakeholders and desired outcomes, is lacking in the literature. To address this gap, we conducted a systematic literature review and analysed the literature on drone-based last mile delivery using a morphological analysis approach to develop a conceptual framework with seven dimensions and 37 variants. The dimensions identified are customer expectations, distribution mode, inventory planning, returns handling, geographical scope, stakeholders and desired outcomes. The Variance Intersection Matrix revealed 176 research gaps that future researchers can explore. This study also helps managers and policymakers visualize the different dimensions of drone-based last mile delivery and their interrelationships, which will help accelerate the adoption of drones in delivery fleets.
AgatzN., BoumanP., & SchmidtM. (2018). Optimization approaches for the traveling salesman problem with drone optimization. Transportation Science, 52(4), 965–981. https://doi.org/10.1287/trsc.2017.0791
2.
ArditoL., CerchioneR., MazzolaE., & RaguseoE. (2022). Industry 4.0 transition: A systematic literature review combining the absorptive capacity theory and the data–information–knowledge hierarchy. Journal of Knowledge Management, 26(9), 2222–2254.
3.
Aviation Rulemaking Committee. (2022). Unmanned aircraft systems beyond visual line of sight. Federal Aviation Administration. https://rb.gy/yrda4
4.
BaligaA. J., ChawlaV., Sunder MV., GaneshL. S., & SivakumaranB. (2021). Service failure and recovery in B2B markets: A morphological analysis. Journal of Business Research, 131(February2020), 763–781.
5.
BalochG., & GzaraF. (2020). Strategic network design for parcel delivery with drones under competition. Transportation Science, 54(1), 204–228.
6.
BanikD., UllahN., HossainI., GovindanK., NurF., & Babski-ReevesK. (2023). A decision support model for selecting unmanned aerial vehicle for medical supplies: Context of COVID-19 pandemic. The International Journal of Logistics Management, 34(2), 473–496. https://doi.org/10.1108/IJLM-06-2021-0334
7.
BergP. W., IsaacsS., & BlodgettK. L. (2016). Airborne fulfillment center utilizing unmanned aerial vehicles for item delivery (Patent No. US9305280B1). U.S. Patent and Trademark Office.
8.
BosmanJ., MourikI. van, & RaschM. (2006). Scopus reviewed and compared: The coverage and functionality of the citation database Scopus, including comparisons with Web of Science and Google Scholar (pp. 1–63). Utrecht University Library.
9.
Bots & Drones India. (2021, May6). 20 Indian companies receive permission for BVLOS drone operations. Bots & Drones India. https://rb.gy/ir7pi
10.
BoumanP., AgatzN., & SchmidtM. (2018). Dynamic programming approaches for the traveling salesman problem with drone. Networks, 72(4), 528–542.
11.
CaiL., YuenK. F., XieD., FangM., & WangX. (2021). Consumer’s usage of logistics technologies: Integration of habit into the unified theory of acceptance and use of technology. Technology in Society, 67, 101789.
12.
CastilloV. E., BellJ. E., RoseW. J., & RodriguesA. M. (2018). Crowdsourcing last mile delivery: Strategic implications and future research directions. Journal of Business Logistics, 39(1), 7–25.
13.
CavaniS., IoriM., & RobertiR. (2021). Exact methods for the traveling salesman problem with multiple drones. Transportation Research Part C: Emerging Technologies, 130, 103280.
14.
ChenH., HuZ., & SolakS. (2021). Improved delivery policies for future drone-based delivery systems. European Journal of Operational Research, 294(3), 1181–1201.
15.
ChopraS. (2003). Designing the distribution network in a supply chain. Transportation Research Part E: Logistics and Transportation Review, 39(2), 123–140.
16.
ChowdhuryS., EmeloguA., MarufuzzamanM., NurreS. G., & BianL. (2017). Drones for disaster response and relief operations: A continuous approximation model. International Journal of Production Economics, 188, 167–184.
17.
ChristopherM., & PeckH. (2004). Building the resilient supply chain. International Journal of Logistics Management, 15(2), 1–13.
18.
ChungS. H., SahB., & LeeJ. (2020). Optimization for drone and drone-truck combined operations: A review of the state of the art and future directions. Computers and Operations Research, 123, 105004.
19.
CokyasarT., DongW., JinM., & Verbasİ. Ö. (2021). Designing a drone delivery network with automated battery swapping machines. Computers and Operations Research, 129, 105177.
20.
Conqueror Freight Network. (2022, July28). The use of delivery drones in logistics industry.
21.
CornellA., KlossB., PresserD., & RiedelR. (2023). Drones take to the sky, potentially disrupting last-mile delivery. McKinsey & Company. https://rb.gy/av0ux
22.
CoxworthB. (2021, November23). DroneDek mailbox designed to securely accept deliveries-by-drone. New Atlas. https://rb.gy/nc6o1
23.
CullinaneS. (2009). From bricks to clicks: The impact of online retailing on transport and the environment. Transport Reviews, 29(6), 759–776.
24.
DeloisonT., HannonE., HuberA., HeidB., KlinkC., SahayR., & WolffC. (2020). The future of the last-mile ecosystem. World Economic Forum. www.weforum.org
25.
Di Puglia PuglieseL., GuerrieroF., & ScutelláM. G. (2021). The last-mile delivery process with trucks and drones under uncertain energy consumption. Journal of Optimization Theory and Applications, 191(1), 31–67. https://doi.org/10.1007/S10957-021-01918-8/TABLES/8
26.
D’OnfroJ. (2019, June5). Amazon’s new delivery drone will start shipping packages ‘in a matter of months’. Forbes. https://rb.gy/chr0y
27.
ElsayedM., & MohamedM. (2020). The impact of airspace regulations on unmanned aerial vehicles in last-mile operation. Transportation Research Part D: Transport and Environment, 87, 102480.
28.
Es YurekE., & OzmutluH. C. (2018). A decomposition-based iterative optimization algorithm for traveling salesman problem with drone. Transportation Research Part C: Emerging Technologies, 91(April), 249–262. https://doi.org/10.1016/j.trc.2018.04.009
29.
eMarketer. (2022). Retail e-commerce sales worldwide from 2014 to 2026 (in billion U.S. dollars) [Graph]. Statista. https://rb.gy/yufax
30.
Federal Aviation Administration. (2020, August6). Small unmanned aircraft systems (UAS) regulations (Part 107). United States Department of Transportation.
31.
FigliozziM. A. (2020). Carbon emissions reductions in last mile and grocery deliveries utilizing air and ground autonomous vehicles. Transportation Research Part D: Transport and Environment, 85, 102443.
32.
FoxR. (2023, April10). The ultimate guide to reverse logistics for eCommerce. Returngo. https://returngo.ai/ultimate-guide-to-reverse-logistics/
33.
FrederickS., LimW. T., & WinkenbachM. (2019). Configuring the last-mile in business-to-consumer e-retailing. California Management Review, 61(2), 132–154.
34.
GanjipourH., & EdrisiA. (2023). Applying the integrated model to understanding online buyers’ intention to adopt delivery drones in Iran. Transportation Letters, 15(2), 98–110.
35.
GlaserA. (2017, January27). One of China’s largest online retailers is adding dozens of drone delivery routes to rural villages in 2017. Vox. https://rb.gy/mra9m
36.
GoelA., GaneshL. S., & KaurA. (2019). Sustainability integration in the management of construction projects: A morphological analysis of over two decades’ research literature. Journal of Cleaner Production, 236, 117676.
37.
GoodchildA., & ToyJ. (2018). Delivery by drone: An evaluation of unmanned aerial vehicle technology in reducing CO2 emissions in the delivery service industry. Transportation Research Part D: Transport and Environment, 61, 58–67.
38.
HaQ. M., DevilleY., PhamQ. D., & HàM. H. (2018). On the min-cost traveling salesman problem with drone. Transportation Research Part C: Emerging Technologies, 86, 597–621.
39.
HarringtonT. S., SinghaiJ., KumarM., & WohlrabJ. (2016). Identifying design criteria for urban system last-mile solutions—A multi-stakeholder perspective. Production Planning and Control, 27(6), 456–476.
40.
HübnerA., KuhnH., & WollenburgJ. (2016). Last mile fulfilment and distribution in omni-channel grocery retailing: A strategic planning framework. International Journal of Retail and Distribution Management, 44(3), 228–247.
41.
IshfaqR., GibsonB., DefeeC., & Davis-SramekB. (2018, December18). Urban fulfillment: The battle for city shoppers. Supply Chain Quarterly. https://rb.gy/1o41j
42.
JanakiramanN., SyrdalH. A., & FrelingR. (2016). The effect of return policy leniency on consumer purchase and return decisions: A meta-analytic review. Journal of Retailing, 92(2), 226–235.
43.
JanakiramanV., & SankarN. (2022, December21). Electric vehicles: EVs—The future of last mile deliveries. ET Auto. https://rb.gy/7zau4
44.
JanjevicM., & WinkenbachM. (2020). Characterizing urban last-mile distribution strategies in mature and emerging e-commerce markets. Transportation Research Part A: Policy and Practice, 133, 164–196.
45.
JassinL., & RobinsonM. (2017, December20). This blood delivery drone is saving lives in remote areas. Business Insider India. https://rb.gy/jt7hs
46.
JoerssM., NeuhausF., & SchröderJ. (2016). How customer demands are reshaping last-mile delivery. McKinsey & Company. https://www.mckinsey.com/industries/logistics/our-insights/how-customer-demands-are-reshaping-last-mile-delivery
47.
KimD., LeeK., & MoonI. (2019). Stochastic facility location model for drones considering uncertain flight distance. Annals of Operations Research, 283(1–2), 1283–1302.
48.
KinB., VerlindeS., & MacharisC. (2017). Sustainable urban freight transport in megacities in emerging markets. Sustainable Cities and Society, 32, 31–41.
49.
KirschsteinT. (2020). Comparison of energy demands of drone-based and ground-based parcel delivery services. Transportation Research Part D: Transport and Environment, 78, 102209.
50.
KitjacharoenchaiP., MinB. C., & LeeS. (2020). Two echelon vehicle routing problem with drones in last mile delivery. International Journal of Production Economics, 225(December2019), 107598.
51.
KitjacharoenchaiP., VentrescaM., Moshref-JavadiM., LeeS., TanchocoJ. M. A., & BruneseP. A. (2019). Multiple traveling salesman problem with drones: Mathematical model and heuristic approach. Computers and Industrial Engineering, 129(June2018), 14–30.
52.
KoetsierJ. (2021, August18). Drone delivery is live today, and it’s 90% cheaper than car-based services. Forbes. https://rb.gy/1qbea
53.
LagorioA., PintoR., & GoliniR. (2016). Research in urban logistics: A systematic literature review. International Journal of Physical Distribution and Logistics Management, 46(10), 908–931.
54.
LeH. T. K., CarrelA. L., & ShahH. (2022). Impacts of online shopping on travel demand: A systematic review. Transport Reviews, 42(3), 273–295.
55.
LeeC. Y., ÇetinkayaS., & WagelmansA. P. M. (2001). A dynamic lot-sizing model with demand time windows. Management Science, 47(10), 1384–1395.
56.
LeeH. L., ChenY., GillaiB., & RammohanS. (2016, June). Technological disruption and innovation in last-mile delivery. Value Chain Innovation Initiative, 4, 1–24.
57.
LemardeléC., EstradaM., PagèsL., & BachofnerM. (2021). Potentialities of drones and ground autonomous delivery devices for last-mile logistics. Transportation Research Part E: Logistics and Transportation Review, 149, 102325.
58.
LeonS., ChenC., & RatcliffeA. (2023). Consumers’ perceptions of last mile drone delivery. International Journal of Logistics Research and Applications, 26(3), 345–364.
59.
LiH., ChenJ., WangF., & BaiM. (2021). Ground-vehicle and unmanned-aerial-vehicle routing problems from two-echelon scheme perspective: A review. European Journal of Operational Research, 294(3), 1078–1095.
60.
LipsmanA., & LiuC. (2020). Coronavirus boosts ecommerce forecast and will accelerate channel-shift. eMarketer. https://www.insiderintelligence.com/content/us-ecommerce-2020
61.
LiuY. (2019). An optimization-driven dynamic vehicle routing algorithm for on-demand meal delivery using drones. Computers & Operations Research, 111, 1–20.
62.
LiuS., LiY., HuangJ., & ZhaoX. (2019). Understanding the consumer satisfaction of the ‘last-mile’ delivery of E-business services: Computer and computing technologies in agriculture XI. 11th IFIP WG 5.14 International Conference, CCTA 2017, 546, 411–418. https://doi.org/10.1007/978-3-030-06179-1_41
63.
LudwigH., Nils-ArneH., JanK., & ThomasN. (2014). Same-day delivery: The next evolutionary step in parcel logistics. McKinsey & Company. https://rb.gy/9odgy
64.
LuoQ., WuG., JiB., WangL., & SuganthanP. N. (2021). Hybrid multi-objective optimization approach with pareto local search for collaborative truck-drone routing problems considering flexible time windows. IEEE Transactions on Intelligent Transportation Systems, 1–15. https://doi.org/10.1109/TITS.2021.3119080
65.
MacrinaG., Di Puglia PuglieseL., GuerrieroF., & LaporteG. (2020). Drone-aided routing: A literature review. Transportation Research Part C: Emerging Technologies, 120(February), 102762.
66.
MangiaracinaR., PeregoA., SeghezziA., & TuminoA. (2019). Innovative solutions to increase last-mile delivery efficiency in B2C e-commerce: A literature review. International Journal of Physical Distribution and Logistics Management, 49(9), 901–920.
67.
Mbiadou SaleuR. G., DeroussiL., FeilletD., GrangeonN., & QuilliotA. (2022). The parallel drone scheduling problem with multiple drones and vehicles. European Journal of Operational Research, 300(2), 571–589.
68.
MerkertR., BliemerM. C. J., & FayyazM. (2022). Consumer preferences for innovative and traditional last-mile parcel delivery. International Journal of Physical Distribution and Logistics Management, 52(3), 261–284.
69.
MogiliU. R., & DeepakB. B. V. L. (2018). Review on application of drone systems in precision agriculture. Procedia Computer Science, 133, 502–509.
70.
Moshref-JavadiM., HemmatiA., & WinkenbachM. (2021). A comparative analysis of synchronized truck-and-drone delivery models. Computers and Industrial Engineering, 162(June), 107648.
71.
Moshref-JavadiM., & LeeS. (2016). The customer-centric, multi-commodity vehicle routing problem with split delivery. Expert Systems with Applications, 56, 335–348.
72.
Muñoz-VillamizarA., Velázquez-MartínezJ. C., HaroP., FerrerA., & MariñoR. (2021). The environmental impact of fast shipping ecommerce in inbound logistics operations: A case study in Mexico. Journal of Cleaner Production, 283, 125400.
73.
MurrayC. C., & ChuA. G. (2015). The flying sidekick traveling salesman problem: Optimization of drone-assisted parcel delivery. Transportation Research Part C: Emerging Technologies, 54, 86–109.
74.
MurrayC. C., & RajR. (2020). The multiple flying sidekicks traveling salesman problem: Parcel delivery with multiple drones. Transportation Research Part C: Emerging Technologies, 110(February2019), 368–398.
75.
NguyenM. A., DangG. T. H., HàM. H., & PhamM. T. (2022). The min-cost parallel drone scheduling vehicle routing problem. European Journal of Operational Research, 299(3), 910–930. https://doi.org/10.1016/j.ejor.2021.07.008
76.
NicholsonJ. D., LaPlacaP., Al-AbdinA., BreeseR., & KhanZ. (2018). What do introduction sections tell us about the intent of scholarly work: A contribution on contributions. Industrial Marketing Management, 73, 206–219.
77.
OsakweC. N., HudikM., ŘíhaD., StrosM., & RamayahT. (2022). Critical factors characterizing consumers’ intentions to use drones for last-mile delivery: Does delivery risk matter?Journal of Retailing and Consumer Services, 65, 102865.
78.
OttoA., AgatzN., CampbellJ., GoldenB., & PeschE. (2018). Optimization approaches for civil applications of unmanned aerial vehicles (UAVs) or aerial drones: A survey. Networks, 72(4), 411–458.
79.
PandeyD. (2022, July18). How drone delivery could change the shipping industry. AeoLogic. https://www.aeologic.com/blog/how-drone-delivery-could-change-the-shipping-industry/
80.
Pina-PardoJ. C., SilvaD. F., & SmithA. E. (2021). The traveling salesman problem with release dates and drone resupply. Computers and Operations Research, 129, 105170.
81.
PintoR., & LagorioA. (2022). Point-to-point drone-based delivery network design with intermediate charging stations. Transportation Research Part C: Emerging Technologies, 135(December2021), 103506.
82.
PintoR., ZambettiM., LagorioA., & PirolaF. (2020). A network design model for a meal delivery service using drones. International Journal of Logistics Research and Applications, 23(4), 354–374.
83.
PrasharA. (2023). Quality management in industry 4.0 environment: A morphological analysis and research agenda. International Journal of Quality and Reliability Management, 40(3), 863–885.
84.
RajR., & MurrayC. (2020). The multiple flying sidekicks traveling salesman problem with variable drone speeds. Transportation Research Part C: Emerging Technologies, 120, 102813.
85.
RamanathanR. (2011). An empirical analysis on the influence of risk on relationships between handling of product returns and customer loyalty in e-commerce. International Journal of Production Economics, 130(2), 255–261.
86.
RaoS., GriffisS. E., & GoldsbyT. J. (2011). Failure to deliver? Linking online order fulfillment glitches with future purchase behavior. Journal of Operations Management, 29(7–8), 692–703.
87.
RashidzadehE., Hadji MolanaS. M., SoltaniR., & HafezalkotobA. (2021). Assessing the sustainability of using drone technology for last-mile delivery in a blood supply chain. Journal of Modelling in Management, 16(4), 1376–1402.
88.
RitcheyT. (1998). General morphological analysis: A general method for non-quantified modelling. 16th Euro Conference on Operational Analysis, 2002 (revised).
89.
RussoF., & ComiA. (2011). Measures for sustainable freight transportation at urban scale: Expected goals and tested results in Europe. Journal of Urban Planning and Development, 137(2), 142–152.
90.
SacramentoD., PisingerD., & RopkeS. (2019). An adaptive large neighborhood search metaheuristic for the vehicle routing problem with drones. Transportation Research Part C: Emerging Technologies, 102, 289–315.
91.
SaritasO., & BurmaogluS. (2016). Future of sustainable military operations under emerging energy and security considerations. Technological Forecasting and Social Change, 102, 331–343.
92.
SchermerD., MoeiniM., & WendtO. (2019). A matheuristic for the vehicle routing problem with drones and its variants. Transportation Research Part C: Emerging Technologies, 106, 166–204.
93.
SeghezziA., MangiaracinaR., TuminoA., & PeregoA. (2021). Pony express’ crowdsourcing logistics for last-mile delivery in B2C e-commerce: An economic analysis. International Journal of Logistics Research and Applications, 24(5), 456–472.
94.
SeghezziA., SiragusaC., & MangiaracinaR. (2022). Parcel lockers vs. home delivery: A model to compare last-mile delivery cost in urban and rural areas. International Journal of Physical Distribution and Logistics Management, 52(3), 213–237.
95.
SheR., & OuyangY. (2021). Efficiency of UAV-based last-mile delivery under congestion in low-altitude air. Transportation Research Part C: Emerging Technologies, 122, 102878. https://doi.org/10.1016/j.trc.2020.102878
96.
SongL., CherrettT., McLeodF., & GuanW. (2009). Addressing the last mile problem: Transport impacts of collection and delivery points. Transportation Research Record, 2097(1), 9–18.
StockJ., SpehT., & ShearH. (2006). Managing product returns for competitive advantage. MIT Sloan Management Review, 48(1), 57–62.
99.
SunderV. M., GaneshL. S., & MaratheR. R. (2018). A morphological analysis of research literature on Lean Six Sigma for services. International Journal of Operations and Production Management, 38(1), 149–182.
100.
TarasovK. (2022, November11). A first look at Amazon’s new delivery drone. CNBC. https://rb.gy/ocyrr
101.
TavanaM., Khalili-DamghaniK., Santos-ArteagaF. J., & ZandiM.-H. (2017). Drone shipping versus truck delivery in a cross-docking system with multiple fleets and products. Expert Systems with Applications, 72, 93–107.
102.
TeimouryE., & RashidR. (2023). The sustainable hybrid truck-drone delivery model with stochastic customer existence. Research in Transportation Economics, 100, 101325. https://doi.org/10.1016/J.RETREC.2023.101325
103.
TranfieldD., DenyerD., & SmartP. (2003). Towards a methodology for developing evidence-informed management knowledge by means of systematic review. British Journal of Management, 14(3), 207–222.
104.
United Nations. (2021, May3). Global e-commerce jumps to $26.7 trillion, fuelled by COVID-19. UN News.
105.
VeenstraM., RoodbergenK. J., CoelhoL. C., & ZhuS. X. (2018). A simultaneous facility location and vehicle routing problem arising in health care logistics in the Netherlands. European Journal of Operational Research, 268(2), 703–715.
106.
VolleroA., SardanelliD., & SianoA. (2021). Exploring the role of the Amazon effect on customer expectations: An analysis of user-generated content in consumer electronics retailing. Journal of Consumer Behaviour, 22(5), 1062–1073.
107.
WangK., PeschE., KressD., FridmanI., & BoysenN. (2022). The piggyback transportation problem: Transporting drones launched from a flying warehouse. European Journal of Operational Research, 296(2), 504–519.
108.
WangK., YuanB., ZhaoM., & LuY. (2020). Cooperative route planning for the drone and truck in delivery services: A bi-objective optimisation approach. Journal of the Operational Research Society, 71(10), 1657–1674. https://doi.org/10.1080/01605682.2019.1621671
109.
WangX., ZhanL., RuanJ., & ZhangJ. (2014). How to choose ‘last mile’ delivery modes for E-fulfillment. Mathematical Problems in Engineering, 2014, 1–11.
110.
WangY., ZhangD., LiuQ., ShenF., & LeeL. H. (2016). Towards enhancing the last-mile delivery: An effective crowd-tasking model with scalable solutions. Transportation Research Part E: Logistics and Transportation Review, 93, 279–293.
111.
WymanO. (2018, September10). Why the use of drones still faces big regulatory hurdles. Forbes. https://rb.gy/cexgs
112.
XiaY., ZengW., XingX., ZhanY., TanK. H., & KumarA. (2023). Joint optimisation of drone routing and battery wear for sustainable supply chain development: A mixed-integer programming model based on blockchain-enabled fleet sharing. Annals of Operations Research, 327, 89–127.
113.
YangX., StraussA. K., CurrieC. S. M., & EgleseR. (2016). Choice-based demand management and vehicle routing in e-fulfillment. Transportation Science, 50(2), 473–488.
114.
YangY., YanC., CaoY., & RobertiR. (2023). Planning robust drone-truck delivery routes under road traffic uncertainty. European Journal of Operational Research, 309(3), 1145–1160.
115.
ZorziniM., HendryL. C., HuqF. A., & StevensonM. (2015). Socially responsible sourcing: Reviewing the literature and its use of theory. International Journal of Operations and Production Management, 35(1), 60–109.
116.
ZwickyF. (1969). Discovery, invention, research through the morphological approach (1st ed.). Macmillan.
