Application of machine learning methods for pallet loading problem

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dc.authorid0000-0003-0067-1835
dc.contributor.authorAylak, Batin Latif
dc.contributor.authorİnce, Murat
dc.contributor.authorOral, Okan
dc.contributor.authorAlmasarwah, Najat
dc.contributor.authorSingh, Manjeet
dc.contributor.authorSalah, Bashir
dc.contributor.authorSüer, Gursel
dc.date.accessioned2022-01-11T07:12:25Z
dc.date.available2022-01-11T07:12:25Z
dc.date.issued2021
dc.departmentTAÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.description.abstractBecause of continuous competition in the corporate industrial sector, numerous companies are always looking for strategies to ensure timely product delivery to survive against their competitors. For this reason, logistics play a significant role in the warehousing, shipments, and transportation of the products. Therefore, the high utilization of resources can improve the profit margins and reduce unnecessary storage or shipping costs. One significant issue in shipments is the Pallet Loading Problem (PLP) which can generally be solved by seeking to maximize the total number of boxes to be loaded on a pallet. In many previous studies, various solutions for the PLP have been suggested in the context of logistics and shipment delivery systems. In this paper, a novel two-phase approach is presented by utilizing a number of Machine Learning (ML) models to tackle the PLP. The dataset utilized in this study was obtained from the DHL supply chain system. According to the training and testing of various ML models, our results show that a very high (>85%) Pallet Utilization Volume (PUV) was obtained, and an accuracy of >89% was determined to predict an accurate loading arrangement of boxes on a suitable pallet. Furthermore, a comprehensive analysis of all the results on the basis of a comparison of several ML models is provided in order to show the efficacy of the proposed methodology.
dc.identifier.citationAylak, B. L., İnce, M., Oral, O., Süer, G., Almasarwah, N., Singh, M., & Salah, B. (2021). Application of Machine Learning Methods for Pallet Loading Problem. Applied Sciences, 11(18), 8304.
dc.identifier.doi10.3390/app11188304
dc.identifier.issn2076-3417
dc.identifier.issue18en_US
dc.identifier.scopus2-s2.0-85114696081
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://hdl.handle.net/20.500.12846/624
dc.identifier.volume11en_US
dc.identifier.wosWOS:000699504600001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorAylak, Batin Latif
dc.language.isoen
dc.publisherMDPI-Multidisciplinary Digital Publishing Institute
dc.relation.ispartofApplied Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectLogisticsen_US
dc.subjectMachine Learningen_US
dc.subjectPallet Loading Problem (PLP)en_US
dc.subjectClassifiersen_US
dc.subjectLogistiken_US
dc.subjectMaschinelles Lernen Palettenladeproblemen_US
dc.subjectKlassifikatorenen_US
dc.subjectLojistiken_US
dc.subjectMakine Öğrenmeen_US
dc.subjectPalet Yükleme Problemien_US
dc.titleApplication of machine learning methods for pallet loading problem
dc.typeArticle

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