Drones-DT: Gerenciamento Dinâmico de Frotas de Drones Representados por Gêmeos Digitais
Resumo
Sistemas de entregas com drones têm se mostrado uma alternativa promissora para aplicações urbanas que exigem rapidez e flexibilidade operacional. Contudo, a variabilidade da demanda dificulta o dimensionamento adequado da frota, podendo resultar em subutilização de recursos ou em violações de Acordos de Nível de Serviço (SLA), especialmente relacionados ao tempo médio de entrega. Este trabalho propõe Drones-DT, uma arquitetura de Gêmeo Digital baseada em um modelo de Redes de Petri Estocásticas para o gerenciamento dinâmico de frotas de drones. O Drones-DT é continuamente sincronizado com um simulador de drones e realiza análises preditivas do tipo what-if para estimar métricas de desempenho. A partir dessas estimativas, um mecanismo de decisão orientado por SLA ajusta dinamicamente a quantidade de drones em operação. Os resultados indicam que a abordagem proposta mantém o cumprimento do SLA ao mesmo tempo em que reduz a quantidade média de drones utilizados quando comparada a estratégias estáticas.Referências
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Mohammad El-Basioni, B. M. (2025). Data-driven joint routing, topology, and mobility design for fanet systems using a digital twin approach. Journal of Electrical Systems and Information Technology, 12(1):1.
Moshood, T. D., Nawanir, G., Sorooshian, S., and Okfalisa, O. (2021). Digital twins driven supply chain visibility within logistics: A new paradigm for future logistics. Applied System Innovation, 4(2):29.
Nurgaliev, I., Eskander, Y., and Lis, K. (2023). The use of drones and autonomous vehicles in logistics and delivery. Logistics and Transport, 57.
Rejeb, A., Rejeb, K., Simske, S. J., and Treiblmaier, H. (2023). Drones for supply chain management and logistics: a review and research agenda. International Journal of Logistics Research and Applications, 26(6):708–731.
Salem, T., Dragomir, M., and Chatelet, E. (2024). Strategic integration of drone technology and digital twins for optimal construction project management. Applied Sciences, 14(11):4787.
Sells, B. E. and Crossley, W. A. (2023). Optimization and decision-making framework for small unmanned aircraft systems fleet design. Journal of Aircraft, 60(4):981–994.
Walton, R. B., Ciarallo, F. W., and Champagne, L. E. (2024). A unified digital twin approach incorporating virtual, physical, and prescriptive analytical components to support adaptive real-time decision-making. Computers & Industrial Engineering, 193:110241.
Betti Sorbelli, F. (2024). Uav-based delivery systems: A systematic review, current trends, and research challenges. Journal on Autonomous Transportation Systems, 1(3):1–40.
Dinh, Q. M. (2024). Utilizing unmanned aerial vehicles in commerce and managing supply chains-a literature review.
Filippi, G., Basu, T., Patelli, E., Vasile, M., and Fossati, M. (2023). A digital twin model for drone based distributed healthcare network. In Proceedings of the 33rd European Safety and Reliability Conference, Southampton, UK, pages 3–7.
Lakhwani, T. S. (2025). Integrating 5pl frameworks with drone-based last-mile delivery: A model for future-ready logistics. Transportation Development Research, 3(1):27–45.
Little, J. D. and Graves, S. C. (2008). Little’s law. In Building intuition: insights from basic operations management models and principles, pages 81–100. Springer.
Lv, Z., Chen, D., Feng, H., Zhu, H., and Lv, H. (2021). Digital twins in unmanned aerial vehicles for rapid medical resource delivery in epidemics. IEEE Transactions on Intelligent Transportation Systems, 23(12):25106–25114.
Maciel, P., Matos, R., Silva, B., Figueiredo, J., Oliveira, D., Fé, I., Maciel, R., and Dantas, J. (2017). Mercury: Performance and dependability evaluation of systems with exponential, expolynomial, and general distributions. In 2017 IEEE 22nd Pacific Rim international symposium on dependable computing (PRDC), pages 50–57. IEEE.
Mohammad El-Basioni, B. M. (2025). Data-driven joint routing, topology, and mobility design for fanet systems using a digital twin approach. Journal of Electrical Systems and Information Technology, 12(1):1.
Moshood, T. D., Nawanir, G., Sorooshian, S., and Okfalisa, O. (2021). Digital twins driven supply chain visibility within logistics: A new paradigm for future logistics. Applied System Innovation, 4(2):29.
Nurgaliev, I., Eskander, Y., and Lis, K. (2023). The use of drones and autonomous vehicles in logistics and delivery. Logistics and Transport, 57.
Rejeb, A., Rejeb, K., Simske, S. J., and Treiblmaier, H. (2023). Drones for supply chain management and logistics: a review and research agenda. International Journal of Logistics Research and Applications, 26(6):708–731.
Salem, T., Dragomir, M., and Chatelet, E. (2024). Strategic integration of drone technology and digital twins for optimal construction project management. Applied Sciences, 14(11):4787.
Sells, B. E. and Crossley, W. A. (2023). Optimization and decision-making framework for small unmanned aircraft systems fleet design. Journal of Aircraft, 60(4):981–994.
Walton, R. B., Ciarallo, F. W., and Champagne, L. E. (2024). A unified digital twin approach incorporating virtual, physical, and prescriptive analytical components to support adaptive real-time decision-making. Computers & Industrial Engineering, 193:110241.
Publicado
25/05/2026
Como Citar
MIQUEIAS, José; LOPES, Lucas Silva; FÉ, Iure; NUNES, Jonas; BITTENCOURT, Luiz Fernando; WICKBOLDT, Juliano; SILVA, Francisco Airton.
Drones-DT: Gerenciamento Dinâmico de Frotas de Drones Representados por Gêmeos Digitais. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 44. , 2026, Praia do Forte/BA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 673-686.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2026.19309.
