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|Title:||Optimization of dynamic carousel circuit for droneport airside operations under weather uncertainty||Authors:||Zeng, Yixi
Duong, Vu N.
|Keywords:||Engineering::Aeronautical engineering||Issue Date:||2022||Source:||Zeng, Y., Ky, G., Wu, Y. & Duong, V. N. (2022). Optimization of dynamic carousel circuit for droneport airside operations under weather uncertainty. 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC). https://dx.doi.org/10.1109/ITSC55140.2022.9922055||Abstract:||In drone operations, changes in the weather can potentially influence the planned routes and schedules of drones. It is therefore vital to incorporate proper models of weather uncertainty into drone flow management methods, such as the dynamic carousel circuit. As an extension of our previous studies, this research aims to monitor weather uncertainty and validate the efficiency and effectiveness of the dynamic carousel circuit when considering weather uncertainty and dynamical incoming flow. A comparison of prediction accuracies for first-order and second-order Markov chain models with simple weather states or realistic weather states is presented in this paper. Besides, a novel approach, two-layer simulation optimization, is introduced to solve the optimization problem for large-scale stochastic simulation efficiently. This proposed approach is composed of a segmental simulation optimization algorithm with a Genetic Algorithm to obtain the optimum radius for the dynamic carousel circuit of each interval parallelly, and a ranking and selection process to find the best candidate for a whole-day simulation duration among these optimum results efficiently. The finding of this study shows that such approach can be successfully applied to obtain the optimum radius for the dynamic carousel circuit with stochastic inputs. Results from the Monte Carlo simulation prove the stability of the dynamic carousel circuit under weather uncertainty and changing demand.||URI:||https://hdl.handle.net/10356/162344||ISBN:||978-1-6654-6881-7||DOI:||10.1109/ITSC55140.2022.9922055||Rights:||© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/ITSC55140.2022.9922055.||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||ATMRI Conference Papers|
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Updated on Feb 4, 2023
Updated on Feb 4, 2023
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