Hybridization Approach to Optimize the Expected Loss of Demand in Drone Delivery Scheduling

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dc.contributor.authorHarsha, S.S.
dc.contributor.authorMuddi, K.S.
dc.contributor.authorJindrali, S.S.
dc.contributor.authorReji, S.
dc.contributor.authorDas, M.
dc.contributor.authorMohan, B.R.
dc.date.accessioned2026-02-06T06:34:08Z
dc.date.issued2024
dc.description.abstractThis paper explores a hybrid-optimization approach for reducing the expected loss of delivery in drone delivery.This paper aims to give a deep knowledge about drone scheduling using machine learning and bio-optimized approaches. Using hybridization of K-Mean Clustering algorithms and Genetic algorithms, the paper makes a comparison between the performance of the above algorithm with the hybridization of hierarchical agglomerative clustering algorithms and ant colony optimization algorithms, resulting in valuable insights into drone delivery efficiency and reliability. © 2024 IEEE.
dc.identifier.citation2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024, 2024, Vol., , p. -
dc.identifier.urihttps://doi.org/10.1109/IATMSI60426.2024.10503082
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/29074
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subjectAnt Colony Optimization
dc.subjectBio-Optimized Approach
dc.subjectDrone
dc.subjectExpected Loss Of Demand
dc.subjectGenetic Algorithm
dc.subjectHierarchical Agglomerative Clustering
dc.subjectK-means Clustering
dc.subjectPayload
dc.subjectProbability Of Failure
dc.titleHybridization Approach to Optimize the Expected Loss of Demand in Drone Delivery Scheduling

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