Publication:
Demand forecasting with deep learning: Case study in a third-party logistics company for the COVID-19 period

dc.contributor.authorZeybel Peköz, Ayşe
dc.contributor.authorİnkaya, Tülin
dc.contributor.buuauthorZeybel Peköz, Ayşe
dc.contributor.buuauthorİNKAYA, TÜLİN
dc.contributor.departmentBursa Uludağ Üniversitesi/Mühendislik Fakültesi/Endüstri Mühendisliği Bölümü.
dc.contributor.orcid0000-0002-6260-0162
dc.contributor.researcheridJDL-0934-2023
dc.contributor.researcheridAAH-2155-2021
dc.date.accessioned2024-10-24T11:33:00Z
dc.date.available2024-10-24T11:33:00Z
dc.date.issued2023-01-01
dc.description.abstractThe restrictions and closures experienced during the COVID-19 pandemic period have affected the global supply chains greatly. The logistics sector is among the most affected sectors from this process. For this reason, accurate and fast estimation of logistics demand is important for effective resource planning. In this study, the aim is to predict the demand accurately in a third-party logistics company during the COVID-19 pandemic period. The shipment data of a logistics company between June 2020 and December 2020 were examined, and the prediction problem was considered as univariate time series. In the scope of the study, a deep learning-based demand forecasting model is proposed. In the proposed prediction model, convolutional neural network (CNN) and long short-term memory (LSTM) network are integrated. CNN provides feature extraction, LSTM captures long-term dependencies, and the proposed model is called hybrid CNN-LSTM. The prediction performance of the hybrid CNN-LSTM was evaluated by comparing it with the classical prediction approaches as well as machine learning and deep learning approaches. The parameter values of all forecasting methods were determined by experimental studies. According to the experimental results, the proposed hybrid CNN-LSTM method showed higher performance than the other methods. The proposed approach generates input to workforce and resource planning activities by providing accurate estimation of logistics demand.
dc.identifier.doi10.5505/pajes.2022.73537
dc.identifier.endpage339
dc.identifier.issn1300-7009
dc.identifier.issue4
dc.identifier.startpage331
dc.identifier.urihttps://doi.org/10.5505/pajes.2022.73537
dc.identifier.urihttp://pajes.pau.edu.tr/en/jvi.aspx?pdir=pajes&plng=eng&un=PAJES-73537
dc.identifier.urihttps://hdl.handle.net/11452/47016
dc.identifier.volume29
dc.identifier.wos001050703000004
dc.indexed.wosWOS.ESCI
dc.language.isoen
dc.publisherPamukkale Üniversitesi
dc.relation.journalPamukkale University Journal of Engineering Sciences-Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectDemand forecasting
dc.subjectThird-party logistics
dc.subjectDeep learning
dc.subjectConvolutional neural networks
dc.subjectLong short-term memory
dc.subjectEngineering
dc.titleDemand forecasting with deep learning: Case study in a third-party logistics company for the COVID-19 period
dc.typeArticle
dspace.entity.typePublication
relation.isAuthorOfPublication50789246-3e56-4752-a821-3ae9957be346
relation.isAuthorOfPublication.latestForDiscovery50789246-3e56-4752-a821-3ae9957be346

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