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Cited by 76Urban and Freight Transport LogisticsVehicle Routing Optimization MethodsTransport and Logistics Innovations

Toward a modern last-mile delivery: consequences and obstacles of intelligent technology

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Sorooshian·2022·Applied System Innovation
DOI 10.3390/asi5040082
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Abstract

Last-mile delivery (LMD) is essential in supply chains, and providers of logistics services are aware that they must adapt to changing customer and society expectations, competition challenges, and modern technologies. In light of the importance of artificial intelligence (AI) in modern technologies, this article employed a narrative literature review to recognize impacts of AI-powered technologies that aid in optimizing the LMD component. For this study, two possible classes of technologies were considered: tangible technologies, such as robots, drones, and autonomous vehicles, and intangible technologies, such as decision support tools and operating systems. Thus, this article frames potential developments in modern LMD, taking into account the fact that technological advancement brings both opportunities and challenges. According to this literature review, the modern version of LMD is capable of providing services that are both more productive and more sustainable, thus satisfying demands for better services. Last but not least, another goal of this article was to look into the challenges that limit the benefits of modern LMDs. Finally, suggestions are provided as a resource for practitioners and policymakers working in the supply chain to improve performance.

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  • When drones are delivering packages, AI and ML algorithms can guide them and assist them to avoid obstacles.

    “When drones are delivering packages, these algorithms can guide them and assist them avoid obstacles [100] .”

    From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [100]

    Section 2.2 says AI-powered drones can change routes in flight and use sensors to react to obstacles; Section 3 discusses AI-guided routing for drones and robots in last-mile delivery. The paper defines machine learning as part of AI, but the passages do not specifically say that ML algorithms guide delivery drones or help them avoid obstacles. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • Packages and supplies can be delivered to far-flung or difficult-to-reach regions using drones with AI and ML algorithms.

    “Packages and supplies can be delivered to far-flung or difficult-to-reach regions using drones with AI and ML algorithms.”

    From Artificial intelligence, machine learning and deep learning in advanced robotics, a review (2023) · cites this paper as [100]

    Section 2.2 describes AI-powered drones for parcel delivery in rural areas and areas with limited infrastructure. Section 3 says delivery drones transport packages and medical supplies and may serve rural areas. The paper discusses ML in last-mile delivery generally, but does not establish that the delivery drones use ML algorithms specifically. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Cites

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Research works printed but not linked: 83

  1. Lai, K.-H.; Cheng, T.E. Just-in-Time Logistics; Routledge: London, UK, 2016.
  2. Sorooshian, S. Fuzzy approach to statistical control charts. J. Appl. Math. 2013, 2013, 745153. [CrossRef] · DOI
  3. Correia, D.; Teixeira, L.; Marques, J. Last-mile-as-a-service (LMaaS): An innovative concept for the disruption of the supply chain. Sustain. Cities Soc. 2021, 75, 103310. [CrossRef] · DOI
  4. Devari, A.; Nikolaev, A.; He, Q. Crowdsourcing the last mile delivery of online orders by exploiting the social networks of retail store consumers. Transp. Res. Part E Logist. Transp. Rev. 2017, 105, 105-122. [CrossRef] · DOI
  5. Wang, Y.; Zhang, D.; Liu, Q.; Shen, F.; Lee, L.H. Towards enhancing the last-mile delivery: An effective crowd-tasking model with scalable solutions. Transp. Res. Part E Logist. Transp. Rev. 2016, 93, 279-293. [CrossRef] · DOI
  6. Balcik, B.; Beamon, B.; Smilowitz, K. Last Mile Distribution in Humanitarian Relief. J. Intell. Transp. Syst. 2008, 12, 51-63. [CrossRef] · DOI
  7. Tiwapat, N.; Pornsing, C.; Jomthong, P. Last Mile Delivery: Modes, Efficiencies, Sustainability, and Trends. In Proceedings of the 3rd International Conference on Intelligent Transportation Engineering, Singapore, 3-5 September 2018.
  8. Loan, N.T.C.; Huyen, N.T.; Loan, B.T.T.; Giang, T.T.T.; Trinh, H.T.; Dat, K.M.; Mai, P.T.N. Last-Mile Delivery in B2C E-Commerce- Common Practices in Some Countries, But What Do They Mean for Businesses in Vietnam? J. Hunan Univ. Nat. Sci. 2022, 49, 87-92. [CrossRef] · DOI
  9. Escudero-Santana, A.; Muñuzuri, J.; Lorenzo-Espejo, A.; Muñoz-Díaz, M.L. Improving E-Commerce Distribution through Last-Mile Logistics with Multiple Possibilities of Deliveries Based on Time and Location. J. Theor. Appl. Electron. Commer. Res. 2022, 17, 507-521. [CrossRef] · DOI
  10. Pahwa, A.; Jaller, M. A cost-based comparative analysis of different last-mile strategies for e-commerce delivery. Transp. Res. Part E Logist. Transp. Rev. 2022, 164, 102783. [CrossRef] · DOI
  11. Bosona, T. Urban Freight Last Mile Logistics-Challenges and Opportunities to Improve Sustainability: A Literature Review. Sustainability 2020, 12, 8769. [CrossRef] · DOI
  12. OECD. E-Commerce in the Times of COVID-19. 2020. Available online: https://read.oecd-ilibrary.org/view/?ref=137_137212-t0 fjgnerdb&title=E-commerce-in-the-time-of-COVID-19&_ga=2.163882806.842985313.1632042260-595419267.1575664195 (accessed on 15 September 2021).
  13. Calabrò, G.; Le Pira, M.; Giuffrida, N.; Fazio, M.; Inturri, G.; Ignaccolo, M. Modelling the dynamics of fragmented vs. consolidated last-mile e-commerce deliveries via an agent-based model. Transp. Res. Procedia 2022, 62, 155-162. [CrossRef] · DOI
  14. Boysen, N.; Fedtke, S.; Schwerdfeger, S. Last-mile delivery concepts: A survey from an operational research perspective. OR Spectrum 2021, 43, 1-58. [CrossRef] · DOI
  15. Sorooshian, S. Delay-based reliability analysis on construction projects. Life Sci. J. 2014, 11, 104-113.
  16. Macioszek, E. First and last mile delivery-problems and issues. In Scientific and Technical Conference Transport Systems Theory and Practice; Springer: Cham, Switzerland, 2017; pp. 147-154.
  17. Pannirselvan, M.D.; Bin Rahamaddulla, S.R.; Muuhamad, P.F.; Maarof, M.G.; Sorooshian, S. Innovative solution for barriers of green logistics in food manufacturing industries. Int. J. Appl. Eng. Res. 2016, 11, 9478-9487.
  18. Aziz, A.A.; Daud, S.; Sorooshian, S. Exploring quality influencing factors for frozen food industry. IOP Conf. Ser. Mater. Sci. Eng. 2019, 697, 012018. [CrossRef] · DOI
  19. Viu-Roig, M.; Alvarez-Palau, E.J. The impact of E-Commerce-related last-mile logistics on cities: A systematic literature review. Sustainability 2020, 12, 6492. [CrossRef] · DOI
  20. Cui, R.; Lu, Z.; Sun, T.; Golden, J. Sooner or Later? Promising Delivery Speed in Online Retail. Promising Delivery Speed in Online Retail. 29 March 2020. Available online: https://ssrn.com/abstract=3563404 (accessed on 31 July 2022).
  21. Sarker, V.K.; Gia, T.N.; Ben Dhaou, I.; Westerlund, T. Smart parking system with dynamic pricing, edge-cloud computing and lora. Sensors 2010, 20, 4669. [CrossRef] [PubMed] · DOI
  22. Falatoonitoosi, E.; Ahmed, S.; Sorooshian, S. A multicriteria framework to evaluate supplier's greenness. Abstr. Appl. Anal. 2014, 2014, 396923. [CrossRef] · DOI
  23. Sorooshian, S.; Qi, L.C.; Li Fei, L. Characterization of ISO 14001 implementation. Environ. Qual. Manag. 2018, 27, 97-105. [CrossRef] · DOI
  24. Sorooshian, S.; Ting, K.C. Reasons for implementing ISO 14001 in malaysia. Environ. Qual. Manag. 2018, 27, 125-133. [CrossRef] · DOI
  25. Boysen, N.; Schwerdfeger, S.; Weidinger, F. Scheduling last-mile deliveries with truck-based autonomous robots. Eur. J. Oper. Res. 2018, 271, 1085-1099. [CrossRef] · DOI
  26. Mangano, G.; Zenezini, G.; Cagliano, A.C.; De Marco, A. The dynamics of diffusion of an electronic platform supporting City Logistics services. Oper. Manag. Res. 2019, 12, 182-198. [CrossRef] · DOI
  27. Vakulenko, Y.; Shams, P.; Hellström, D.; Hjort, K. Service innovation in e-commerce last mile delivery: Mapping the e-customer journey. J. Bus. Res. 2019, 101, 461-468. [CrossRef] · DOI
  28. Ranieri, L.; Digiesi, S.; Silvestri, B.; Roccotelli, M. A Review of Last Mile Logistics Innovations in an Externalities Cost Reduction Vision. Sustainability 2018, 10, 782. [CrossRef] · DOI
  29. Tumino, A.; Mangiaracina, R.; Perego, A.; Seghezzi, A. Innovative solutions to increase last-mile delivery efficiency in B2C e-commerce: A literature review. Int. J. Phys. Distrib. Logist. Manag. 2019, 49, 901-920.
  30. Vanelslander, T.; Deketele, L.; Van Hove, D. Commonly used e-commerce supply chains for fast moving consumer goods: Comparison and suggestions for improvement. Int. J. Logist. Res. Appl. 2013, 16, 243-256. [CrossRef] · DOI
  31. Jucha, P. Use of artificial intelligence in last mile delivery. SHS Web Conf. 2021, 92, 04011. [CrossRef] · DOI
  32. Part E Logist. Transp. Rev. 2020, 142, 102049. [CrossRef] · DOI
  33. Zhu, F.; Liu, Q. Competing with complementors: An empirical look at Amazon.com. Strateg. Manag. J. 2018, 39, 2618-2642. [CrossRef] · DOI · Paper page
  34. Hoffmann, T.; Prause, G. On the Regulatory Framework for Last-Mile Delivery Robots. Machines 2018, 6, 33. [CrossRef] · DOI
  35. Jennings, D.; Figliozzi, M. Study of Sidewalk Autonomous Delivery Robots and Their Potential Impacts on Freight Efficiency and Travel. Transp. Res. Rec. J. Transp. Res. Board 2019, 2673, 317-326. [CrossRef] · DOI
  36. Chen, C.; Demir, E.; Huang, Y.; Qiu, R. The adoption of self-driving delivery robots in last mile logistics. Transp. Res. Part E Logist. Transp. Rev. 2021, 146, 102214. [CrossRef] · DOI
  37. Goel, R.; Gupta, P. Robotics and industry 4.0. In A Roadmap to Industry 4.0: Smart Production, Sharp Business and Sustainable Development. Advances in Science, Technology Innovation; Springer International Publishing: Cham, Switzerland, 2020; pp. 157-169.
  38. Asim, Z.; Sorooshian, S. Exploring the role of knowledge, innovation and technology management (KNIT) capabilities that influence research and development. J. Open Innov. Technol. Mark. Complex. 2019, 5, 21. [CrossRef] · DOI
  39. Moshood, T.D.; Nawanir, G.; Sorooshian, S.; Mahmud, F.; Adeleke, A.Q. Barriers and benefits of ICT adoption in the nigerian construction industry. A comprehensive literature review. Appl. Syst. Innov. 2020, 3, 46. [CrossRef] · DOI
  40. Wanganoo, L.; Patil, A. Preparing for the smart cities: IoT enabled last-mile delivery. In Proceedings of the 2020 Advances in Science and Engineering Technology International Conferences, Dubai, United Arab Emirates, 4 February-9 April 2020.
  41. Alkhalifah, A.; Alorini, F.; Alturki, R. Enhancement of E-commerce service by designing last mile delivery platform. Comput. Syst. Sci. Eng. 2022, 42, 49-67. [CrossRef] · DOI
  42. Bansal, S.; Goel, R.; Maini, R. Ground vehicle and UAV collaborative routing and scheduling for humanitarian logistics using random walk based ant colony optimization. Sci. Iran. 2022, 29, 632-644. [CrossRef] · DOI
  43. Campisi, T.; Severino, A.; Al-Rashid, M.A. Introducing the benefits of autonomous vehicles to logistics during the COVID-19 era. Int. Rev. Civ. Eng. 2022, 13, 83-90. [CrossRef] · DOI
  44. Giuffrida, N.; Fajardo-Calderin, J.; Masegosa, A.D.; Werner, F.; Steudter, M.; Pilla, F. Optimization and machine learning applied to last-mile logistics: A review. Sustainability 2022, 14, 5329. [CrossRef] · DOI
  45. Leung, E.K.H.; Lee, C.K.H.; Ouyang, Z. From traditional warehouses to physical internet hubs: A digital twin-based inbound synchronization framework for PI-order management. Int. J. Prod. Econ. 2022, 244, 108353. [CrossRef] · DOI
  46. Luo, R.; Ji, S.; Ji, Y. An active-learning pareto evolutionary algorithm for parcel locker network design considering accessibility of customers. Comput. Oper. Res. 2022, 141, 105677. [CrossRef] · DOI
  47. Moadab, A.; Farajzadeh, F.; Fatahi Valilai, O. Drone routing problem model for last-mile delivery using the public transportation capacity as moving charging stations. Sci. Rep. 2022, 12, 6361. [CrossRef] · DOI
  48. Peppel, M.; Ringbeck, J.; Spinler, S. How will last-mile delivery be shaped in 2040? A delphi-based scenario study. Technol. Forecast. Soc. Chang. 2022, 177, 121493. [CrossRef] · DOI
  49. Pinto, R.; Lagorio, A. Point-to-point drone-based delivery network design with intermediate charging stations. Transp. Res. Part C Emerg. Technol. 2022, 135, 103506. [CrossRef] · DOI
  50. Sorokin, M.; Tan, J.; Karen Liu, C.; Ha, S. Learning to navigate sidewalks in outdoor environments. IEEE Robot. Autom. Lett. 2022, 7, 3906-3913. [CrossRef] · DOI
  51. Rajendran, S.D.; Wahab, S.N. Investigating last-mile delivery options on online shoppers experience and repurchase intention. Int. J. Electron. Mark. Retail. 2022, 13, 224-241. [CrossRef] · DOI
  52. Woody, M.; Craig, M.T.; Vaishnav, P.T.; Lewis, G.M.; Keoleian, G.A. Optimizing future cost and emissions of electric delivery vehicles. J. Ind. Ecol. 2022, 26, 1108-1122. [CrossRef] · DOI
  53. Xu, J.; Liu, X.; Li, X.; Zhang, L.; Jin, J.; Yang, Y. Energy-aware computation management strategy for smart logistic system with MEC. IEEE Internet Things J. 2022, 9, 8544-8559. [CrossRef] · DOI
  54. Yan, Y.; Chow, A.H.F.; Ho, C.P.; Kuo, Y.H.; Wu, Q.; Ying, C. Reinforcement learning for logistics and supply chain management: Methodologies, state of the art, and future opportunities. Transp. Res. Part E Logist. Transp. Rev. 2022, 162, 102712. [CrossRef] · DOI
  55. Zennaro, I.; Finco, S.; Calzavara, M.; Persona, A. Implementing E-commerce from logistic perspective: Literature review and methodological framework. Sustainability 2022, 14, 911. [CrossRef] · DOI
  56. Woschank, M.; Rauch, E.; Zsifkovits, H. A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics. Sustainability 2020, 12, 3760. [CrossRef] · DOI · Paper page
  57. Kreuzer, R.; Sirrenberg, M. Understanding Artificial Intelligence Fundamentals, Use Cases and Methods for a Corporate AI Journey; Springer: Cham, Switzerland, 2020.
  58. Asim, Z.; Sarwat, S.; Sorooshian, S.; Shahid Ahmad, M. Overview on pakistan R&D sector in context adopting technological capabilities. Information 2017, 20, 7217-7226.
  59. Modgil, S.; Singh, R.; Hannibal, C. Artificial intelligence for supply chain resilience: Learning from COVID-19. Int. J. Logist. Manag. 2021. ahead-of-print. [CrossRef] · DOI
  60. Borhan, R.I.; Fhang, O.W.; Sorooshian, S. Operations analysis: Practice on performance measurement in supply chain. In Advanced Materials Research; Trans Tech Publications Ltd.: Bäch, Switzerland, 2013. [CrossRef] · DOI
  61. Nakaie, M.; Hau, G.Z.; Sorooshian, S. Operations analysis: A case for supply chain performance. In Advanced Materials Research; Trans Tech Publications Ltd.: Bäch, Switzerland, 2013. [CrossRef] · DOI
  62. Chu, H.; Zhang, W.; Bai, P.; Chen, Y. Data-driven optimization for last-mile delivery. Complex Intell. Syst. 2021, 107, 1-14. [CrossRef] · DOI
  63. Gutierrez-Franco, E.; Mejia-Argueta, C.; Rabelo, L. Data-Driven Methodology to Support Long-Lasting Logistics and Decision Making for Urban Last-Mile Operations. Sustainability 2021, 13, 6230. [CrossRef] · DOI
  64. Barreto, L.; Amaral, A.; Pereira, T. Industry 4.0 implications in logistics: An overview. Procedia Manuf. 2017, 13, 1245-1252. [CrossRef] · DOI
  65. Kayikci, Y. Sustainability impact of digitization in logistics. Procedia Manuf. 2018, 21, 782-789. [CrossRef] · DOI
  66. Winkelhaus, S.; Grosse, E.H. Logistics 4.0: A systematic review towards a new logistics system. Int. J. Prod. Res. 2019, 58, 18-43. [CrossRef] · DOI
  67. Bányai, T. Real-Time Decision Making in First Mile and Last Mile Logistics: How Smart Scheduling Affects Energy Efficiency of Hyperconnected Supply Chain Solutions. Energies 2018, 11, 1833. [CrossRef] · DOI
  68. McLeod, F.; Cherrett, T. Loading bay booking and control for urban freight. Int. J. Logist. Res. Appl. 2011, 14, 385-397. [CrossRef] · DOI
  69. Bartodziej, C.J. The Concept Industry 4.0: An Empirical Analysis of Technologies and Applications in Production Logistics; Springer Gabler: Wiesbaden, Germany, 2017.
  70. Appelbaum, D.; Nehmer, R.A. Using drones in internal and external audits: An exploratory framework. J. Emerg. Technol. Account. 2017, 14, 99-113. [CrossRef] · DOI
  71. Dalamagkidis, K. Classification of uavs. In Handbook of Unmanned Aerial Vehicles; Springer: Dordrecht, The Netherlands, 2015.
  72. Kim, H.; Ben-Othman, J.; Mokdad, L.; Son, J.; Li, C. Research Challenges and Security Threats to AI-Driven 5G Virtual Emotion Applications Using Autonomous Vehicles, Drones, and Smart Devices. IEEE Netw. 2020, 34, 288-294. [CrossRef] · DOI
  73. Sindi, S.; Woodman, R. Autonomous Goods Vehicles for Last-mile Delivery: Evaluation of Impact and Barriers. In Proceedings of the 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), Rhodes, Greece, 20-23 September 2020. [CrossRef] · DOI
  74. Jucha, P. Use of artificial intelligence in last mile delivery. In Proceedings of the Globalization and its Socio Economic Consequences, Rajecke Teplice, Slovak Republic, 21-22 October 2020.
  75. Alfandari, L.; Ljubić, I.; De Melo da Silva, M. A tailored Benders decomposition approach for last-mile delivery with autonomous robots. Eur. J. Oper. Res. 2022, 299, 510-525. [CrossRef] · DOI
  76. Turi, J.A.; Sorooshian, S.; Mahmud, F.B.; Javed, Y. The cognitive, behavioral and social prospectives of organizational learning through information system. Malays. Online J. Educ. Manag. 2018, 6, 68-86. [CrossRef] · DOI
  77. Schaudt, S.; Clausen, U. Exact Approach for Last Mile Delivery with Autonomous Robots. In Operations Research Proceedings; Springer: Cham, Switzerland, 2020; pp. 405-411. [CrossRef] · DOI
  78. Amazon. Amazon Prime Air. 2021. Available online: https://www.amazon.com/Amazon-Prime-Air/b?ie=UTF8&node=803772 0011 (accessed on 14 September 2021). 100. Datex Corporation. Using Technology to Solve Last Mile Delivery Challenges. 2021. Available online: https://www.datexcorp. com/using-technology-solve-last-mile-delivery-challenges/ (accessed on 30 July 2022). 101. Nentwich, M.; Horváth, D.M. The vision of delivery drones: Call for a technology assessment perspective. TATuP-Z. Tech. Theor. Prax. J. Technol. Assess. Theory Pract. 2018, 27, 46-52. 102. Jain, A.K.; Svetlik, M.; Machak, N.; Sikand, K.S. An open-source framework for last mile delivery with heterogeneous robots. Good Syst. Publ. Res. 2021, 97-99. 103.
  79. Casado-Vara, R.; Alonso, R.S.; García-Coria, J.A.; Rodríguez, S.; Prieto, J. Efficient collaborative strategy for last mile package delivery optimization: Salamanca case study. In Intelligent Environments; IOS Press: Amsterdam, The Netherlands, 2020. 104.
  80. Rashidzadeh, E.; Hadji Molana, S.M.; Soltani, R.; Hafezalkotob, A. Assessing the sustainability of using drone technology for last-mile delivery in a blood supply chain. J. Model. Manag. 2021. ahead-of-print. [CrossRef] 105. Iyer, L.S. AI enabled applications towards intelligent transportation. Transp. Eng. 2021, 5, 100083. [CrossRef] 106. Poeting, M.; Schaudt, S.; Clausen, U. Simulation of an Optimized Last-Mile Parcel Delivery Network Involving Delivery Robots. In Advances in Production, Logistics and Traffic; Springer: Cham, Switzerland, 2019; pp. 1-19. [CrossRef] 107. Ostermeier, M.; Heimfarth, A.; Hübner, A. Cost-optimal truck-and-robot routing for last-mile delivery. Networks 2022, 79, 364-389. [CrossRef] 108. Joshi, N. Using Robots for Last-Mile Delivery. 2020. Available online: https://www.bbntimes.com/technology/using-robots-for- last-mile-delivery (accessed on 31 July 2022). 109. · DOI
  81. Aurambout, J.P.; Gkoumas, K.; Ciuffo, B. Last mile delivery by drones: An estimation of viable market potential and access to citizens across European cities. Eur. Transp. Res. Rev. 2019, 11, 30. [CrossRef] 110. Tang, C.S.; Veelenturf, L.P. The strategic role of logistics in the industry 4.0 era. Transp. Res. Part E Logist. Transp. Rev. 2019, 129, 1-11. [CrossRef] 111. Park, J.; Kim, S.; Suh, K. A Comparative Analysis of the Environmental Benefits of Drone-Based Delivery Services in Urban and Rural Areas. Sustainability 2018, 10, 888. [CrossRef] 112. Du, D. Research on the application of "last-mile" autonomous delivery vehicles in the context of epidemic prevention and control. In Proceedings of the 2021 International Symposium on Artificial Intelligence and Its Application on Media (ISAIAM), Xi'an, China, 21-23 May 2021; pp. 74-77. [CrossRef] 113. · DOI
  82. Conde, M.L.; Twinn, I. How Artificial Intelligence is Making Transport Safer, Cleaner, More Reliable and Efficient in Emerging Markets 8; World Bank Group: Washingto, DC, USA, 2019. 114. van der Zande, J.; Teigland, K.; Siri, S.; Teigland, R. The Substitution of Labor: From technological feasibility to other factors influencing job automation. Stockh. Sch. Econ. Inst. Res. 2018, 11, 40. 115.
  83. Vattapparamban, E.; Güvenç, ˙I.; Yurekli, A. ˙I.; Akkaya, K.; Ulua gaç, S. Drones for smart cities: Issues in cybersecurity, privacy, and public safety. In Proceedings of the 2016 International Wireless Communications and Mobile Computing Conference (IWCMC), Paphos, Cyprus, 5-9 September 2016. [CrossRef] 116. Sudbury, A.W.; Hutchinson, E.B. A cost analysis of amazon prime air (drone delivery). J. Econ. Educ. 2016, 16, 1-12. 117. Sah, B.; Gupta, R.; Bani-Hani, D. Analysis of barriers to implement drone logistics. Int. J. Logist. Res. Appl. 2020, 24, 531-550. [CrossRef] 118. Feng, Z.; Guan, N.; Lv, M.; Liu, W.; Deng, Q.; Liu, X.; Yi, W. Efficient drone hijacking detection using two-step GA-XGBoost. J. Syst. Archit. 2020, 103, 101694. [CrossRef] · DOI

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Sorted by citations

Authors
Sorooshian
Venue
Applied System Innovation
Publisher
MDPI AG
Published
2022-08-17
Volume
5
Issue
4
Pages
82
ISSN
2571-5577 (ISSN-L)
DOI
10.3390/asi5040082
OpenAlex
W4292262934
Cited by
76
References
3
Citations by year
2026: 20 · 2025: 29 · 2024: 18 · 2023: 9
FWCI
6.12
Type
journal article
Language
en
Open access
Licence
Locations
  • Applied System Innovation (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Engineering › Building and Construction › Urban and Freight Transport Logistics
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Vehicle Routing Optimization Methods
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Transport and Logistics Innovations
Keywords
last-mile delivery, artificial intelligence, logistics, delivery robots, unmanned aerial vehicles, autonomous vehicles, decision support system, operating systems, green logistics, supply chain performance, technology adoption barriers

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