Robust Composition of Drone Delivery Services under Uncertainty
This addresses drone delivery optimization in urban areas, but it appears incremental as it builds on existing composition methods with a focus on uncertainty.
The paper tackles the problem of composing drone delivery services under wind uncertainty by proposing a robust framework and Probabilistic Forward Search algorithm, achieving effectiveness and efficiency as demonstrated through experiments with a real drone dataset.
We propose a novel robust composition framework for drone delivery services considering changes in the wind patterns in urban areas. The proposed framework incorporates the dynamic arrival of drone services at the recharging stations. We propose a Probabilistic Forward Search (PFS) algorithm to select and compose the best drone delivery services under uncertainty. A set of experiments with a real drone dataset is conducted to illustrate the effectiveness and efficiency of the proposed approach.