Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/16762
Full metadata record
DC FieldValueLanguage
dc.contributor.authorMaitra, Sarit-
dc.date.accessioned2024-12-12T09:29:58Z-
dc.date.available2024-12-12T09:29:58Z-
dc.date.issued2024-
dc.identifier.citationpp. 78-85en_US
dc.identifier.isbn9798400717291-
dc.identifier.urihttps://doi.org/10.1145/3665065.3665078-
dc.identifier.urihttps://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/16762-
dc.description.abstractThis paper introduces an adaptive Bayesian optimization (BayesOpt) framework with dynamic conditioning and jitter mechanisms. The new framework enhances the adaptability and effectiveness of optimization in unpredictable business environments. The dynamic scaling in this framework dynamically modifies the mean objective function in each iteration, and adaptive conditioning functions. The adaptive acquisition jitter function enhances adaptability by adjusting the jitter of the acquisition function. The framework is tested using single-objective, multi-objective, and decoupled multi-objective functions. Statistical analyses which include t-statistics, p-values, and effect size measures (Cohen's d and Hedges g) reveal the superiority of the proposed framework over the original Bayes optimization. The primary contribution is developing a novel and effective optimization approach in stochastic environments, especially in the context of supply chain inventory management. © 2024 ACM.en_US
dc.language.isoenen_US
dc.publisherACM International Conference Proceeding Seriesen_US
dc.publisherAssociation for Computing Machineryen_US
dc.subjectAdaptive Conditioningen_US
dc.subjectDynamic Scalingen_US
dc.subjectEffect Sizeen_US
dc.subjectOptimization Algorithmen_US
dc.subjectProbabilistic Modelingen_US
dc.subjectStochastic Environmentsen_US
dc.titleAdaptive Bayesian Optimization Algorithm for Unpredictable Business Environmentsen_US
dc.typeArticleen_US
Appears in Collections:Conference Papers

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.