Deep Learning and Statistical Models for Forecasting Transportation Demand: A Case Study of Multiple Distribution Centers
<i>Background</i>: 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...
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MDPI AG
2023-11-01
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| Series: | Logistics |
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| Online Access: | https://www.mdpi.com/2305-6290/7/4/86 |
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| author | Fábio Polola Mamede Roberto Fray da Silva Irineu de Brito Junior Hugo Tsugunobu Yoshida Yoshizaki Celso Mitsuo Hino Carlos Eduardo Cugnasca |
| author_facet | Fábio Polola Mamede Roberto Fray da Silva Irineu de Brito Junior Hugo Tsugunobu Yoshida Yoshizaki Celso Mitsuo Hino Carlos Eduardo Cugnasca |
| author_sort | Fábio Polola Mamede |
| collection | DOAJ |
| description | <i>Background</i>: 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. <i>Methods</i>: 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. <i>Results</i>: 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. <i>Conclusions</i>: This work found that forecasting transportation demands can address practical issues in supply chains, specially resource planning management. |
| format | Article |
| id | doaj-art-509873d4768c4f4598e36455ce93c835 |
| institution | Kabale University |
| issn | 2305-6290 |
| language | English |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Logistics |
| spelling | doaj-art-509873d4768c4f4598e36455ce93c8352025-08-20T03:34:35ZengMDPI AGLogistics2305-62902023-11-01748610.3390/logistics7040086Deep Learning and Statistical Models for Forecasting Transportation Demand: A Case Study of Multiple Distribution CentersFábio Polola Mamede0Roberto Fray da Silva1Irineu de Brito Junior2Hugo Tsugunobu Yoshida Yoshizaki3Celso Mitsuo Hino4Carlos Eduardo Cugnasca5Graduate Program in Logistics Systems Engineering, University of São Paulo, São Paulo 05508-010, BrazilInstitute of Advanced Studies, University of São Paulo, São Paulo 05508-010, BrazilGraduate Program in Logistics Systems Engineering, University of São Paulo, São Paulo 05508-010, BrazilGraduate Program in Logistics Systems Engineering, University of São Paulo, São Paulo 05508-010, BrazilDepartment of Production Engineering, University of São Paulo, São Paulo 05508-010, BrazilGraduate Program in Logistics Systems Engineering, University of São Paulo, São Paulo 05508-010, Brazil<i>Background</i>: 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. <i>Methods</i>: 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. <i>Results</i>: 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. <i>Conclusions</i>: This work found that forecasting transportation demands can address practical issues in supply chains, specially resource planning management.https://www.mdpi.com/2305-6290/7/4/86transportation demand forecastingsupply chain managementLSTMARIMAdata preprocessing |
| spellingShingle | Fábio Polola Mamede Roberto Fray da Silva Irineu de Brito Junior Hugo Tsugunobu Yoshida Yoshizaki Celso Mitsuo Hino Carlos Eduardo Cugnasca Deep Learning and Statistical Models for Forecasting Transportation Demand: A Case Study of Multiple Distribution Centers Logistics transportation demand forecasting supply chain management LSTM ARIMA data preprocessing |
| title | Deep Learning and Statistical Models for Forecasting Transportation Demand: A Case Study of Multiple Distribution Centers |
| title_full | Deep Learning and Statistical Models for Forecasting Transportation Demand: A Case Study of Multiple Distribution Centers |
| title_fullStr | Deep Learning and Statistical Models for Forecasting Transportation Demand: A Case Study of Multiple Distribution Centers |
| title_full_unstemmed | Deep Learning and Statistical Models for Forecasting Transportation Demand: A Case Study of Multiple Distribution Centers |
| title_short | Deep Learning and Statistical Models for Forecasting Transportation Demand: A Case Study of Multiple Distribution Centers |
| title_sort | deep learning and statistical models for forecasting transportation demand a case study of multiple distribution centers |
| topic | transportation demand forecasting supply chain management LSTM ARIMA data preprocessing |
| url | https://www.mdpi.com/2305-6290/7/4/86 |
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