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<p>Drone-based delivery systems have emerged as a promising solution for urban logistics, offering fast, flexible last-mile delivery. However, fluctuations in demand for requests over time make fleet sizing challenging. Overprovisioning leads to resource underutilization and higher operational costs, while underprovisioning may lead to delivery delays and violations of Service Level Agreements (SLAs). To address this challenge, this work proposes Drones-DT, a Digital Twin architecture designed for dynamic drone fleet management. The approach relies on a Stochastic Petri Net (SPN) analytical model synchronized with a delivery simulator to maintain a continuously updated Digital Shadow of the system. Based on real-time metrics, the Digital Twin performs predictive what-if analyses to estimate performance indicators, such as Mean Mission Time (MMT), system throughput, fleet utilization, and energy consumption, across different fleet configurations. A SLA-oriented decision mechanism, implemented through a binary search strategy, determines the minimum number of drones required to satisfy the target delivery time. The SPN model was statistically validated against a discrete-event simulator, yielding results equivalent to those of the simulator for MMT and throughput at the 95% confidence level. A case study demonstrates that Drones-DT maintains SLA compliance while operating with a significantly smaller average fleet than static provisioning strategies, achieving up to a 16% reduction in energy consumption and associated CO<sub>2</sub> emissions.</p>

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Drones-dt: dynamic management of drone fleets represented by Digital Twins

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月5日

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  • Miqueias Araújo,
  • Lucas Silva,
  • Iure Fé,
  • Jonas Nunes,
  • Francinaldo Nunes Barbosa,
  • Iago Almeida,
  • Luiz Bittencourt,
  • Francisco Airton Silva

摘要

Drone-based delivery systems have emerged as a promising solution for urban logistics, offering fast, flexible last-mile delivery. However, fluctuations in demand for requests over time make fleet sizing challenging. Overprovisioning leads to resource underutilization and higher operational costs, while underprovisioning may lead to delivery delays and violations of Service Level Agreements (SLAs). To address this challenge, this work proposes Drones-DT, a Digital Twin architecture designed for dynamic drone fleet management. The approach relies on a Stochastic Petri Net (SPN) analytical model synchronized with a delivery simulator to maintain a continuously updated Digital Shadow of the system. Based on real-time metrics, the Digital Twin performs predictive what-if analyses to estimate performance indicators, such as Mean Mission Time (MMT), system throughput, fleet utilization, and energy consumption, across different fleet configurations. A SLA-oriented decision mechanism, implemented through a binary search strategy, determines the minimum number of drones required to satisfy the target delivery time. The SPN model was statistically validated against a discrete-event simulator, yielding results equivalent to those of the simulator for MMT and throughput at the 95% confidence level. A case study demonstrates that Drones-DT maintains SLA compliance while operating with a significantly smaller average fleet than static provisioning strategies, achieving up to a 16% reduction in energy consumption and associated CO2 emissions.