Deep Learning and Statistical Models for Forecasting Transportation Demand: A Case Study of Multiple Distribution Centers

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Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorMamede, Fábio Polola-
Autor(es): dc.creatorda Silva, Roberto Fray-
Autor(es): dc.creatorde Brito Junior, Irineu-
Autor(es): dc.creatorYoshizaki, Hugo Tsugunobu Yoshida-
Autor(es): dc.creatorHino, Celso Mitsuo-
Autor(es): dc.creatorCugnasca, Carlos Eduardo-
Data de aceite: dc.date.accessioned2025-08-21T17:49:03Z-
Data de disponibilização: dc.date.available2025-08-21T17:49:03Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2023-11-30-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/logistics7040086-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/309886-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/309886-
Descrição: dc.descriptionBackground: Transportation demand forecasting is an essential activity for logistics operators and carriers. It leverages business operation decisions, infrastructure, management, and resource planning activities. Since 2015, there has been an increase in the use of deep learning models in this domain. However, there is a gap in works comparing traditional statistics and deep learning models for transportation demand forecasts. This work aimed to perform a case study of aggregated transportation demand forecasts in 54 distribution centers of a Brazilian carrier. Methods: A computational simulation and case study methods were applied, exploring the characteristics of the datasets through autoregressive integrated moving average (ARIMA) and its variations, in addition to a deep neural network, long short-term memory, known as LSTM. Eight scenarios were explored while considering different data preprocessing methods and evaluating how outliers, training and testing dataset splits during cross-validation, and the relevant hyperparameters of each model can affect the demand forecast. Results: The long short-term memory networks were observed to outperform the statistical methods in ninety-four percent of the dispatching units over the evaluated scenarios, while the autoregressive integrated moving average modeled the remaining five percent. Conclusions: This work found that forecasting transportation demands can address practical issues in supply chains, specially resource planning management.-
Descrição: dc.descriptionGraduate Program in Logistics Systems Engineering University of São Paulo-
Descrição: dc.descriptionInstitute of Advanced Studies University of São Paulo, São Paulo-
Descrição: dc.descriptionEnvironmental Engineering Department São Paulo State University-
Descrição: dc.descriptionDepartment of Production Engineering University of São Paulo-
Descrição: dc.descriptionEnvironmental Engineering Department São Paulo State University-
Idioma: dc.languageen-
Relação: dc.relationLogistics-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectARIMA-
Palavras-chave: dc.subjectdata preprocessing-
Palavras-chave: dc.subjectLSTM-
Palavras-chave: dc.subjectsupply chain management-
Palavras-chave: dc.subjecttransportation demand forecasting-
Título: dc.titleDeep Learning and Statistical Models for Forecasting Transportation Demand: A Case Study of Multiple Distribution Centers-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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