Daily Forecasting of Demand Orders with Optimal Architecture Artificial Neural Network Learning Models

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dc.authorscopusid57226654737
dc.authorscopusid57221078940
dc.contributor.authorSimsek O.I.
dc.contributor.authorAlagoz B.B.
dc.date.accessioned2024-08-04T20:03:56Z
dc.date.available2024-08-04T20:03:56Z
dc.date.issued2021
dc.departmentİnönü Üniversitesien_US
dc.descriptionUmniah and UWalleten_US
dc.description2021 International Conference on Information Technology, ICIT 2021 -- 14 July 2021 through 15 July 2021 -- 170653en_US
dc.description.abstractIn recent years, with the increase in volume of buying orders, demand forecast based on the order data is important for improvement of production, distribution and selling services. For this reason, the predictability of orders will increase efficiency in many areas by timely delivering orders, increasing earnings, and customer satisfaction in trading. This article aims to estimate total amount of daily orders by using an optimal structured artificial neural network learning model. To optimize rectangular architecture of artificial neural network model, a metaheuristic optimization, which determines the number of hidden layers and number of neurons, is used. In the study, training of neural networks was carried out with the Levenberg-Marquardt backpropagation algorithm for daily orders collected for 60 days. During this training, the network's layer and neuron number were optimized with a gray wolf optimization algorithm. Results indicate that optimal architecture neural network can better estimate total daily demand orders. © 2021 IEEE.en_US
dc.identifier.doi10.1109/ICIT52682.2021.9491784
dc.identifier.endpage360en_US
dc.identifier.isbn9781665428705
dc.identifier.scopus2-s2.0-85112218492en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage355en_US
dc.identifier.urihttps://doi.org/10.1109/ICIT52682.2021.9491784
dc.identifier.urihttps://hdl.handle.net/11616/92221
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2021 International Conference on Information Technology, ICIT 2021 - Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Neural Networken_US
dc.subjectDemand forecastingen_US
dc.subjectGray Wolf Optimizationen_US
dc.subjectLM Backpropagation Algorithmsen_US
dc.subjectMetaheuristicen_US
dc.subjectOrdersen_US
dc.titleDaily Forecasting of Demand Orders with Optimal Architecture Artificial Neural Network Learning Modelsen_US
dc.typeConference Objecten_US

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