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

Author

Listed:
  • Fábio Polola Mamede

    (Graduate Program in Logistics Systems Engineering, University of São Paulo, São Paulo 05508-010, Brazil)

  • Roberto Fray da Silva

    (Institute of Advanced Studies, University of São Paulo, São Paulo 05508-010, Brazil)

  • Irineu de Brito Junior

    (Graduate Program in Logistics Systems Engineering, University of São Paulo, São Paulo 05508-010, Brazil
    Environmental Engineering Department, São Paulo State University, São José dos Campos 12247-004, Brazil)

  • Hugo Tsugunobu Yoshida Yoshizaki

    (Graduate Program in Logistics Systems Engineering, University of São Paulo, São Paulo 05508-010, Brazil
    Department of Production Engineering, University of São Paulo, São Paulo 05508-010, Brazil)

  • Celso Mitsuo Hino

    (Department of Production Engineering, University of São Paulo, São Paulo 05508-010, Brazil)

  • Carlos Eduardo Cugnasca

    (Graduate Program in Logistics Systems Engineering, University of São Paulo, São Paulo 05508-010, Brazil)

Abstract

Background : 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.

Suggested Citation

  • Fábio Polola Mamede & Roberto Fray da Silva & Irineu de Brito Junior & Hugo Tsugunobu Yoshida Yoshizaki & Celso Mitsuo Hino & Carlos Eduardo Cugnasca, 2023. "Deep Learning and Statistical Models for Forecasting Transportation Demand: A Case Study of Multiple Distribution Centers," Logistics, MDPI, vol. 7(4), pages 1-19, November.
    • Handle: RePEc:gam:jlogis:v:7:y:2023:i:4:p:86-:d:1285024
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    References listed on IDEAS

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    3. Anirut Kantasa-ard & Maroua Nouiri & Abdelghani Bekrar & Abdessamad Ait el cadi & Yves Sallez, 2021. "Machine learning for demand forecasting in the physical internet: a case study of agricultural products in Thailand," International Journal of Production Research, Taylor & Francis Journals, vol. 59(24), pages 7491-7515, December.
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