Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162344
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dc.contributor.authorZeng, Yixien_US
dc.contributor.authorKy, Gregoireen_US
dc.contributor.authorWu, Yuen_US
dc.contributor.authorDuong, Vu N.en_US
dc.date.accessioned2022-11-04T01:45:07Z-
dc.date.available2022-11-04T01:45:07Z-
dc.date.issued2022-
dc.identifier.citationZeng, 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/ITSC55140.2022.9922055en_US
dc.identifier.isbn978-1-6654-6881-7-
dc.identifier.urihttps://hdl.handle.net/10356/162344-
dc.description.abstractIn 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.en_US
dc.description.sponsorshipCivil Aviation Authority of Singapore (CAAS)en_US
dc.description.sponsorshipNanyang Technological Universityen_US
dc.language.isoenen_US
dc.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.en_US
dc.subjectEngineering::Aeronautical engineeringen_US
dc.titleOptimization of dynamic carousel circuit for droneport airside operations under weather uncertaintyen_US
dc.typeConference Paperen
dc.contributor.conference2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)en_US
dc.contributor.researchAir Traffic Management Research Instituteen_US
dc.identifier.doi10.1109/ITSC55140.2022.9922055-
dc.description.versionSubmitted/Accepted versionen_US
dc.subject.keywordsUncertaintyen_US
dc.subject.keywordsMonte Carlo Methodsen_US
dc.citation.conferencelocationMacau, Chinaen_US
dc.description.acknowledgementThis research is supported by the Civil Aviation Authority of Singapore and Nanyang Technological University, Singapore under their collaboration in the Air Traffic Management Research Institute. This work was also supported by the National Natural Science Foundation of China (grant number 52102453) and Chongqing Research Program of Basic Research and Frontier Technology, China (grant number cstc2020jcyj-msxmX0602).en_US
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